Adaptive Flux Morphologies (MArch)

This research would not have been possible without the help of our Emtech colleagues. Thanks to everyone who dedicated their free time to help us in the project.

To Erin, Harriet, Jacqui, Allie, Mom & Dad. I sincerely appreciate the chance to focus on a personal research for such an intense time. I am forever grateful to everyone who helped and to my teammates for the chance to collaborate with such incredible people.

-Mary I would like to thank my parents for not hesitating to make all of this possible, my brother and sister for their support and encouragement, my nieces for always cheering me up, Agnes for showing me what strong really is, and Leo, who surely would have read every page three times over.

-Dennis Thanks to my family and Jose, who have been always there supporting me, to all the friends I have met during EmTech and made it a wonderful personal experience, and specially to my groupmates. Despite the struggle it has been wonderful, guys.

-Javi

CONTENTS

0.0 Abstract

006

1.0 Introduction

008

2.0 Domain

2.1 Cities and public transport

2.2 Flow and movement

2.3 Metabolist movement

2.4 Spatial analysis of urban fabrics

2.5 Slime mould

3.0 Methods

3.1 Agent-based modelling

3.2 Space syntax

3.3 Network graphs

3.4 Application of methods

012 014 018 020 022 024

4.0

Site: Lagos (Nigeria) 5.1 Site chosen 5.2 Locating activity nodes

038 040 050

5.0 Research and network development

5.1 Slime Mould. Physical testing 5.2 Slime Mould. Digital simulation

5.3 Historic underground networks

5.4 Generation of the network

5.5 Node ranking

5.6 Space syntax

5.7 Line division

5.8 Station placement

5.9 Evaluation

052 054 060 066 072 074 076 088 090 098

028 030 032 034 036

6.0

Regional implications 6.1 Station categories 6.2 Selected nodes 6.3 Intermodal station case studies 6.4 Pedestrian node generation 6.5 Regional network generation 6.6 Population density distribution 6.7 Summary

102 104 106 108 114 122 124 126

7.0

Urban morphologies 7.1 Market characteristics 7.2 Morphology and flow 7.3 Morphological refinement 7.4 Evaluation

128 130 136 142 156

8.0 Conclusions

8.1 System potential

8.2 Further development

8.3 Conclusions

158 160 164 168

9.0 Appendix

9.1 Slime Mould scripts

9.2 Charts and tables

9.3 Station analysis

174 177 192 200

Bibliography

202

173

Illustration credits

ABSTRACT

0.0

0-1 Slime Mould growth simulating road network in different regions: Spain and Africa 6

ABSTRACT

0.0

An inevitable result of transportation networks is the variation of flow of people across the network. Too often, these flows are thought of simply as an output of a network.

The network is created and implemented, and the flows are extracted from the resulting usage. It is very rare to couple flow with morphology in urban design. We suggest a method wherein the flows are predicted and used as inputs into a system that can provide more accurate predictions of the number of people that will be using the network on the global scale and on the more localised scale of individual nodes. In this way, the individual network nodes and their corresponding local networks will reflect the rules of the network within the city. As information is passed from the city scale to the node scale, it is coupled to architectural morphologies on the scale of individual blocks and buildings. Our research utilises agent based computing as an

adaptive, generative tool for creating network solutions.

The outputs of this system are evaluated using space syntax software in order to determine their potential effects on the urban context. By extracting flows from this system and coupling them with architectural sections, it is possible to create a connection between the flows generated by an urban scale network and the architectural morphologies of the city itself.

Keywords: Infrastructure, Transportation, Flow, Slime mould, Agent-Based Modelling, Adaptation, Space Syntax, Integration, Morphology

7

8

1.0

INTRODUCTION

9

INTRODUCTION

1.0

1-1 Traffic congestion in Lagos, Nigeria

1-2 Slime Mould growth

1-3 Infrastructure network

10

INTRODUCTION

1.0

The main objective of this research is to develop a system for generating transportation networks in growing urban areas, and to couple the resulting flows within this network with built morphologies at the architectural scale. “Transportation and communication infrastructure systems have played a dramatic role in contributing to explosive urban growth, and therefore impact on its form,”1 thus a system that is capable of linking large urban networks with urban morphologies is necessary in order to ensure the continued effectiveness of the city as a whole. Just as transportation networks can contribute to urban growth, they must also adapt to it. This research addresses the development of urbanism through network design, which aims to develop connections that are “able to adapt to new requirements over time.”2 Numerous factors go into the planning, design, and implementation of a transportation network. Typically, this process is a largely top-down approach based on imposing a network on the landscape that has been predefined according to some design criteria regarding a number of routes or the areas that need to be connected. This predefined process leaves little room for adjustment and does not address the development of nodes within the city, but rather, the general connectivity of the city. This process also has a limited ability to deal with changing conditions over time.

The modern concept of the network is that it is not only a planned system of infrastructure; it is an emergent quality of the city. A single network is connected to multiple networks and functions on many social and ubiquitous levels.

These complex relationships lend themselves to design methods that can accommodate large amounts of information and high degrees of connection.

Past movements such as the Metabolists attempted to create architectural systems that had the ability to adjust to changing urban conditions such as increasing populations, and rapidly expanding cities, but were limited by economic and social constraints of their time. Their designs centred on adapting to changing urban conditions through physically modifying the architecture as was seen in the capsule buildings that they have become known for. However the impracticality of this approach proved to be too much. A

more adaptive system is necessary in order to better address urban changes such as exploding populations and sprawling urban growth.

Precedents by influential icons such as Ildefons Cerdâ and Frank Lloyd Wright have also dealt with the concept of designing for the dynamic flows associated with urban growth.

Cerdà envisioned a network with constant and unlimited movement of rapid, direct flows without bounds within the city2 and Wright suggested a concept that everyone is connected to the collective space and all directions are equally open for exploration.3 Typically, evaluation of a network occurs after its implementation. However, methods exist that allow for evaluation before implementation and for the relationships between the urban fabric and network to be explored simultaneously.

One such method is Space Syntax analysis, which offers the possibility of making preliminary predictions and evaluations of the network’s effect. There are currently many different sources of software that are capable of this type of evaluation. Other methods entail simulating human behaviour and are therefore highly computationally intensive.

We propose that through the use of agent-based computing there is great potential for developing adaptive systems.

Through clear definition of rules of interaction, and flow of agents in the system, it is possible to develop a system that can quickly adjust to changing conditions. Given the proper rule-set, an agent-based model has the potential to generate multiple variations of network solutions which can be individually evaluated to further the robustness of the system. This type of system is also capable of making suggestions of flows across the network. Our method of designing on the architectural scale is to develop a link between these flows and architectural morphology, mainly with respect to the section. By creating this relationship, it is possible to suggest an urban model in which there is a loop of information being passed between the urban networks and the urban morphologies.

1. Stoll, Lloyd; 2010; p. 6.

2. Dupuy; 2008; p 20.

3. Dupuy; 2008; p 35.

11

12

2.0

DOMAIN 2.1 Cities and Public transport 2.2 Flow and movement 2.3 Metabolist Movement 2.4 Spatial analysis of urban fabrics 2.5 Slime mould

13

CITIES AND PUBLIC TRANSPORT

2.1

2-1 Density levels and populations size of metropolitan regions.

Fast growth Stable growth

14

CITIES AND PUBLIC TRANSPORT

2.1

Emergence of cities The year 2008 marked the first time that more than 50% of world’s population is living in cities (Fig. 2-1). This number is expected to increase to 70% by 2050. While urban populations in developed countries are expected to reach a plateau, the urban populations in developing parts of the world are expected to increase significantly, and many new cities will begin to appear.

Cities may have changed morphologically, but from the first settlements of humans to the latest newly-built megacities in China, the essence is still the same. Cities consist of systems whose agents co-exist in an “amplified flow of materials, energy and information, and an increase in social and cultural complexity”1 While cities started from 20,000 inhabitants, the biggest cities in the world today exceed 42,000,000 people. As a result, the complexity of the dynamics and processes within the city develop and are more susceptible to changes in its environment and internal dynamics.

Layout of cities change with the evolution of transportation The spatial structure of the city is directly linked to the means of transportation that prevails during the period of growth. Early settlements were constrained by the topography of the site coupled with the traces of historical settlements. The successfulness in development was also related to the ability to move commodities, which is increased by the proximity to bodies of water. During the Roman Empire the grid was first implemented in new settlements. The same layout was used for their camps because it adapted easily to the movement of troops by horse. With the emergence of electric streetcars and the automobile, street layouts became more curvilinear and presented more cul-desac streets.

1. Weinstock; 2010; p. 186.

In the early history of cities, the absence of long-distance transportation made human activity nodes compact and varied in different activities. Over time as the speed of transportation increased, the clustering of activities disappeared.

Segregation of activities has been present in the Modern Movement (Fig. 2-2) from different architects. The centralized cities started the craze of automobile as indispensable for moving through the city. Road traffic increased considerably, producing problems of connectivity, traffic jams and pollution.

Growth of cities Over time, cities grow or remain stable in size depending on their importance and growth rates. As population increases, two possible ways of growth are available: horizontal or vertical.

Increasing density vertically implies the reconfiguration of the urban tissue proceeding with the destruction of existing urban fabrics (Fig. 2-3). As the city increases in height, many characteristics in the city change, such as climatic impact on solar radiation and influencing wind direction. The existing road network in these cities is required to accommodate more flows with the same public infrastructure, which implies a modification of the existing fabrics.

Horizontal growth can accommodate more flows only if transport infrastructure is developed efficiently. Nevertheless this resolution must take into account the available territory. Land is a scarce commodity that must be shared with vital needs such as production areas for food, energy, and water. If new cities choose a model of growth towards sustainability this pattern of growth has limitations.

There is not a singular solution to accommodating growth of population in existing cities. The city models can be evaluated depending on site-specific characteristics to inform the development. Nevertheless, it is probable that most of the emergent cities lack the proper urban planning to deal with the increase of population or do not have the means to 15

CITIES AND PUBLIC TRANSPORT

2.1

2-2 Le Corbusier's Ville Radieuse.

1924.

The project was divided in four categories which organized into hierarchies different programs.

The use of automobile is essential for the mobility of the inhabitants.

2-3 New York city: the densest city in the United States.

16

CITIES AND PUBLIC TRANSPORT

2.1

control or respond to it. This happens in many cities in the world with high growth rates. This lack of control leads to deterioration of the existing fabric and leads to the formation of slums: whole neighbourhoods without the proper health requirements and isolated areas regarding other parts of the city. Disregarding social implications, this phenomenon produces disadvantages both locally and globally in the city.

With this in mind the challenges that cities must confront in the future is resolving its model of growth. The mentioned advantages and disadvantages must be evaluated in order to discern a suitable developmental option for each particular case.

Means of urban mobility On average, people in cities spend 1.2 hours per day commuting. As a result, people are comfortable with long trips in order to arrive at their destination. This fact marks a new horizon for transportation, as the influence of an urban network can be broadened past the city scale to the inter-urban scale. Both short and long-lasting trips can be addressed by the network service.

As cities evolved, the levels of population density lead to a significant increase in road traffic and multiple problems regarding pollution and transportation. New means of transportation were needed to solve this problem and make mega-cities feasible. In this context, the underground emerged in 1863 as an efficient, powerful means of transportation. This multi-layered system for the city allowed for independence from road dynamics and street layouts, which permitted building pathways connecting points in a more straight-forward fashion. The initial tunnels were built using the cut-and cover procedure. This caused a lot of trouble over ground until new technologies were developed that allowed complete independence from surface dynamics. Direct paths from station to station were feasible which optimised travel time.

Today, many modes of urban mobility such as bus, light rail,

railway, underground, ferries, etc., are implemented in cities at once, each with different advantages and disadvantages.

They all interconnect forming a complex system in which flow dynamics are shared amongst the different systems.

The way in which these systems are evaluated will be shown in a following section.

Problems with implementation of transportation networks While nodes are implemented in the urban tissue with local changes in a relatively simple way, the linkages between them carry more inconveniences to address. Streets represent the basic level of connectivity, while other, more complex means such as underground or train affect more the over-ground situation regarding ecology, comfort, and pedestrian connectivity.

Despite this, the city is not a stable system. Even though an area within a city presents a huge amount of activity in one moment in time, it is not guaranteed that it will do so forever. Networks need to adapt to changes in service in different scales of time: for a special event, seasonal fluctuations, or overall changes in the city with new developments built.

Collapse of cities As explained previously, the needs of a commuting population in every city means that transportation plays an important role in city dynamics and a poorly functioning system can lead to a city not working at all. A long-term successful development of a transportation network requires a meticulous study of both physical facts and sociological behaviour within it. The implementation of this new network is vital to assure a decrease in the automobile flow and thus a reduction of pollution, street congestion, and energy consumption in transportation. Transport networks should not represent a problem, but rather an efficient way of moving through the city in the fastest way possible while benefitting as much of the population as possible. How can a transportation network adapt to every possible change avoiding the collapse of future mega-cities?

17

FLOW AND MOVEMENT

2.2

2-5 Map of New York City showing the use of the city from geotagged Twitter Posts

2-6 Diagrams of movement in Philadelphia (Louis Kahn)

2-7 Highway interchanges have become a defining characteristic of urban areas 18

FLOW AND MOVEMENT

2.2

Flow and Movement Infrastructure networks are the supporting system for movement and flow. “Manuel Castell conceives of the space of flows as the ‘specialization’ of today’s society, a society which is deeply infused by the complex and flexible morphology of the network”.1 Therefore, there should be an emphasis on the effect of movement and flows on the organization of cities. Although flows have certain directionality due to their intensity and the network that carries them, they remain fundamentally intangible (Fig. 2-5).

Therefore their supporting network is subject to an everchanging geometry of flows. We suggest another view of the space of flows where the society (flows and movement) and physical places mutually constitute each other. Space is the material support of movement and flows, and if urban environments are constructed around flows, then a spatial form emerges, one which can determine the morphology of its supporting network.2 Today’s society is increasingly defined by its flows and the rate of its population growth. The fluctuations in the two create variable geometries, and as a result there can be no such thing as fixed position in a network whose shape is changing constantly in response to these fluctuations (Fig. 2-6). The notion of specific places in a network is subject to constant changes as existing nodes temporarily gain or lose importance. “Since the origin and destination of flows can neither be controlled nor predicted, the key issue becomes flexibility and adaptability to the potential and requirements of the networks of flows”.3 The chaotic conditions of cities and urban landscapes today should be informed by these extreme fluctuations. Flows have an origin and a destination, and their trajectories remain deeply governed by the

morphology of the network. The change in flow that occurs along these networks invokes a new internal space which is characterized by a “fluid and flexible topology, and should be investigated as a space that is no less real than physical spaces we inhabit”.4 Cities respond to their increasing growth rate and flow fluctuations through their infrastructure, as it governs both their role of cultural and economic drivers, central to the production of flows. “Hence, cities cannot be conceived of without taking into account the network of flows within which they are positioned; neither is it possible to conceive of flows independently from cities that produce them. The global economy therefore creates a new global context of interaction, where flows and cities become mutually defining entities.”5 In this mutual relationship, infrastructures are the mediators as they both create the primary support for flow and movement within cities and lead to the emergence of a range of public spaces (Fig. 2-7).

These changes in flow can play an integral role in development of an area. Placing a network node in an underdeveloped part of the city will obviously change the nature of that particular area. By increasing the area’s connection to the city, it will inherently attract more and more people, which will in turn lead to the area becoming more developed and more important to the city, which will then lead to it needing more connection. This kind of developing cycle can be used as a proactive approach to developing a city through the use of a network. By intentionally bringing a major network into an underdeveloped part of the city, it is possible to stimulate the regeneration of that area.

1. Delalex; 2006; p.60.

2. Delalex; 2006; p.60.

4. Delalex; 2006; p.70.

3. Delalex; 2006; p.65.

5. Delalex; 2006; p.179.

19

METABOLIST MOVEMENT

2.3

1st year 2nd year 3rd year

4th year 2-8 (Left) Illustrations used by Kenzo Tange to justify the linear growth of the city and phases of growth of Tange's plan for Tokyo Bay. 2-9 (Right) Kenzo Tange plan for Tokyo.

1960

2-10 Nakagin Capsule Tower. Model. and floor type plan.

Kisho Kurokawa 20

METABOLIST MOVEMENT

2.3

The Metabolists After the Second World War, Japan was left in ruins and the challenge of rebuilding the nation became a common topic among architects. This challenge, coupled with the rapid development of technology, led to the emergence of a group of young architects who would become known as the Metabolists. They envisioned a utopian architecture empowered by technological advances and unlimited resources.

A large focus of the Metabolist movement was urban design. With much of the city destroyed by bombing during the war, the Metabolists began rethinking traditional urban design. They strove for a new method which would create a “dynamic relationship between space and function”. 1 They saw the current standards for urban design as creating a “static” condition. They wanted cities to be able to grow organically by adapting to the increasing population. Tokyo was a major case study for many of their projects as its population “explode[d] from 3.5 million in 1945 to 9.5 million in 1960”.2 Several proposals were made to develop Tokyo Bay. The Metabolists rejected the system in place for land ownership, which led them to the idea of creating “an estate where there are no landowners”.3 This idea developed into the concept of “artificial ground”. They proposed reclaiming Tokyo bay in a series of projects all offering ideas of how to create new patches of land in Tokyo Bay for creating a new model of the city. Some projects did so sparingly, while other more extreme projects included proposals of using an atomic bomb to destroy a mountain in order to harvest enough earth to create the artificial islands. The most famous Tokyo Bay proposal was that of Kenzo Tange (Fig. 2-9). European urban designers of the time were promoting the importance of “an identifiable core modeled after traditional civic centres such as Italian piazzas”.4 Tange’s proposal suggested a different solution: the “civic axis”. This was a departure from radial urban planning, moving into a more linear solution (Fig.2-8). Tange argued that this solution was analogous to biological development saying: The amoeba and the asteroid have radial centripetal forms, but vertebrates have linear bone structures with parallel radiations. When the living functions of organisms differentiate and perform composite function of life, the centripetal pattern evolves into a system of parallel lines grouped around an axis formed of a spine and arteries.

The process whereby a vertebrate body hatches

from an egg illustrates the possibility of gradual development of the part of a linear system.5 Another common theme in Metabolist architecture was the idea of the capsule. Many Metabolist projects (both built and un-built) were based around the idea of modular units plugged into larger mega-structures. The most famous example of this concept is the Nakagin capsule tower by Kisho Kurokawa (Fig. 2-10). The building consists of structural core into which individual capsules are inserted.

The capsule is a compact living or work space with built-in appliances. The original idea was that the building would be able to adapt to the changing city and the individual units could be removed, replaced or rearranged. However, since the tower’s completion, not one capsule has ever been replaced and in 2007 it was scheduled to be demolished and substituted with a taller new tower of dwellings.

This is a common outcome for the Metabolist built projects.

Their work might have been highly visionary but, the built projects do not translate to reality very well. The expected time rate for each project was not determined correctly as the rapid pace of economic and social factors surpassed all expectations. Nevertheless, this uncertainty is highly related to the consumerism which drives the use of private architecture. The time rates of private programs are subject to more uncertainty due to the changing trends and economic profitability for the same resource over time. With the unsuccessful built examples, the concept of mega-structure decayed over time being substituted by what Fumihiko Maki called micro-scale planning. These are small interventions in the city tissue that would generate local changes that adapt to the preexisting conditions of the area. This was in contrast to the excess of ambitions of Metabolists which Maki suggested.6 While private programs manifested many flaws, public developments may guarantee a longer life span. Many examples such as the designs in Tokyo Bay consisted of transport linkages and planning their growth with the city.

Fifty years ago, the Metabolist movement dealt with the same issues that affect our current cities. The unlimited expansion of cities due to the exponential increase of human population was and is a major problem, both in developed and developing countries. The creation of artificial ground to increase the usable area in densely-built cities may represent an efficient mechanism for relieving city conditions.

Infrastructure as a driver of urban sprawl has always been used in traditional urban planning not as a response to the unpredictable urban sprawl, but rather has imposed rules which are not always effective.

1. Zhongjie, 2010, p.176.

2. Koolhaas, Obrist, 2011, p.267.

3. Koolhaas, Obrist, 2011, p.274.

5. Zhongjie, 2010, p.158.

4. Zhongjie, 2010, p.154.

6. Zhongjie, 2010, p.228.

21

SPATIAL ANALYSIS OF URBAN FABRICS

2.4

2-11 Observation of how people use urban spaces is one method of evaluating urban networks

2-12 Wayfinding studies how easy it is to find your way in an urban network

2-13 Space syntax analysis of Greater London area 22

SPATIAL ANALYSIS OF URBAN FABRICS

2.4

When designing a network, a major challenge that is presented is evaluating how people will actually use it in the urban fabric. Over time, many different methods have been proposed. Some are based purely on observation after the implementation of a network, while others can be employed before the network is actually implemented, which provides the benefit of giving preliminary data which can be fed back into the system in order to improve the proposed solution.

Observation Method One method of evaluating how people use a network comes from a study that was done in the 1970’s by The Street Life Project, which was formed by a sociologist named William H. Whyte. This group’s research was a “firsthand observation project [that] studied how people inhabit the most intensely used urban spaces”1. The methods employed in this study involved personal interviews with people and by time-lapse photography of the public spaces in order to track the movement of individuals through the space. This method can tell a great deal about the personal choices that people make when occupying space or accessing a network (Fig. 2-11).

Wayfinding Analysis Another method which has become more common, especially with the advent of digital tools, is Wayfinding. Wayfinding deals with how easy it is for a person to navigate a spatial network. Initial attempts to model this behaviour computationally were based around simulated “people” building up a “knowledge base”. This base would grow as the person moves through the network and acquires more information about the overall patterns of the network.

Eventually, there was a shift from trying to model knowledge to trying to model behaviour. The people designing these systems realised that “the ‘simple’ intelligence that they were trying to model (from knowledge based approach)

was present in animals in their adaptive behaviour studied in ethology”2. This method draws on concepts of emergence wherein individuals are given a simple intelligence and the interaction of large amounts of these simple individuals results in higher level, global intelligence (Fig. 2-12).

Space Syntax The third method of evaluation is Space Syntax analysis. Through abstraction of space between buildings into straight lines, this method is based on calculations which represent usage of networks by analysing “spatial configurations in relation to human socio-economics”3. The advantage of this method is that these values can be calculated without having to actually simulate the human behaviour.

This makes this method a very fast and accurate way of evaluating networks on an urban scale. The calculation is very fast and easily visualised. This makes it a highly valuable tool for evaluating multiple solutions for an urban network side by side (Fig. 2-13).

Conclusion Even though all three of these methods have their own merits, Space Syntax analysis will be the tool we use in evaluating our system. The fact that it can produce accurate representations of human use without the need for the added computational burden of simulating human behaviour makes it an ideal tool for quickly evaluating various network solutions. Also, since the information is all quantified into numerical values, it is possible to easily integrate the results into algorithmic processes. It is also highly visual which makes the results comprehensible to outside viewers.

2. Therakomen; 2001; p. 13.

1. Therakomen; 2001; p. 9.

3. Therakomen; 2001; p. 13.

23

SLIME MOULD

2.5

2-14 Plasmodial stage of physarum polycephalum.

2-15 Different phases of growth of slime mould when selecting the optimal path to the solution of the maze.

2-16 Network formation in physarum polycephalum in the city of Tokyo after 8 hours (left) and 26 hours (right)

24

SLIME MOULD

2.5

Biological Network Systems

Inter-City Network Simulation

The organism Physarum Polycephalum (commonly known as slime mould) has been studied extensively for its emergent behaviors (Figs. 2-14).This primitive organism is capable of solving relatively complex problems through its foraging behavior. This organism produces a plasmodium which is a “large amoeba-like cell consisting of a dendritic network of tube-like structures”.1 At first, the spread of the plasmodium is generally homogenous until it encounters food sources. When it is presented with multiple food sources in a field, the plasmodium becomes more concentrated and produces a network of tubes which connect these food sources. Areas that are not in close proximity to food sources shrink and contract back into the bigger more connected tubes. The result is an efficient network which is able to transport nutrients to the extent of the organism’s body while minimizing overall length of the network paths.

These studies were taken to a new scale in 2010, when a team of Japanese scientists began testing the physarum network on an urban scale. Rather than presenting the slime mould with a maze with 2 food sources, they presented the mould with a distributed group of food sources that were laid out to match the pattern of distribution of cities around Tokyo. The slime growth was initiated from the location of the Tokyo metro area. At the beginning, the slime “filled much of the available land space”3 in order to discover all of the available food sources. However, as time went on, the plasmodium refined itself down to an interconnected network connecting all of the food sources. Terrain obstacles were represented by areas of high illumination. Physarum is sensitive to intense light, and therefore avoids areas that are brightly lit. The resulting networks bore a high resemblance to the existing train networks connecting Tokyo to the surrounding cities (Fig. 2-16).

Problem Solving In 2000, the scientists Toshiyuki Nakagaki, Hiroyasu Yamada, and Ágota Tóth published a paper showcasing the problem solving capabilities of this primitive organism. They first placed several pieces of a slime mould culture inside a labyrinth. Next, they placed food sources at the entrance and the exit of the labyrinth. The slime mould began to forage for food, and after four hours, a network was already beginning to emerge. The parts of the plasmodium occupying “dead-ends” of the labyrinth began to recede and integrate into the stronger parts of the network. After another four hours, the network had reduced down to “one thick tube covering the shortest distance”2 through the labyrinth.

In 8 hours, an organism with little to no actual intelligence was able to solve a complex problem in the most efficient way (Fig. 2-15).

A similar experiment was carried out using the layout of cities in the United Kingdom (Figs. 2-17, 2-18). This experiment followed similar procedures as the Tokyo experiment for generating national transportation networks, but compared the results to the existing road network rather than the existing train network. This experiment also employed the use of a digital simulation in parallel to the physical tests (Fig 2-17). The results of this experiment produced a strange anomaly. For the most part, the resulting networks closely mimicked the existing transportation network in the UK as they did in the Tokyo experiment. However, the results suggested that the M6/M74 route between Manchester and Glasgow is not “optimally positioned”.4 This route only appeared in “three of twenty-five experiments”5, 3. Tero, Takagi, Saigusa, Ito, Bebber, Fricker, Yumiki, Kobayashi, Nakagaki; 2010; p.439.

1. Nakagaki, Yamada, Tóth; 2000; p.470.

4. Adamatzky, Jones; 2009; p.14.

2. Nakagaki, Yamada, Tóth; 2000; p.470.

5. Adamatzky, Jones; 2009; p.13.

25

SLIME MOULD

2.5

2-17 Digital simulation and optimization of the network applied in Great Britain layout.

26

SLIME MOULD

2.5

or only 12% of the time. The simulation suggested that there should be a route connecting Newcastle to Glasgow, either in addition to the existing Manchester-Glasgow route, or without the Manchester-Glasgow route altogether. One possible explanation for this phenomenon is that this experiment did not take terrain into account. When looking at a map of the UK, it can be seen that the area between Newcastle and Glasgow is quite mountainous, which would explain why there is no major road link between the two.

In a totally flat world, the best solution may very well be to eliminate the Manchester-Glasgow route in favor of a Newcastle-Glasgow route as the slime mould suggested.

Another important strategy that was incorporated in the UK experiment was the simulation of natural disasters. In order to accomplish this simulation, a city was selected to have a “disaster”. Rather than placing a piece of food at this city, salt was placed there. The salt repels the slime mould, so the network abandons this particular node and rebuilds itself in order to avoid the contaminated city. Once the salt has diffused out of the node, the network returns “reconnects with previously contaminated nodes”.6 Conclusions As a result of researching these different case studies, it is our conclusion that physarum polycephalum’s emergent behaviors develop patterns that can be applied to the physical world. They have been used at scales as small as solving a simple maze and scales as big as simulating the transportation networks of an entire country. These simulations are effective as they allow for mathematical and geometric patterns that define spatial relationships by connection.

These connections are applicable to multiple degrees of scale. One scale where they have not been utilized so far is at the scale of a single city. In the example of the intercity network studies that were discussed previously, cities are made up of activity centres which act as nodes for the movement of people. By distributing food sources to these major activity centres within a city, similar experiments can be done in order to simulate the existing rail transportation networks in individual cities. The experiments can also be used in a way other than simulating an existing network. It is our conclusion that they can be used as a generative design tool in order to design networks that are efficient and evenly distributed in urban environments.

2-18

6. Adamatzky, Jones; 2009; p.20.

Growth of 4 independent samples of Physarum Polycephalum in the layout of Great Britain. Food sources are placed where the 10 most populated cities are.

27

28

3.0

METHODS 3.1 Agent-based modelling 3.2 Space Syntax 3.3 Network graphs 3.4 Application of methods

29

AGENT-BASED MODELLING

3.1

3-1 (left) Flocks present a similar behavior as agent-based simulations.

3-2 (right) A shoal, another natural agentbased behavior.

30

AGENT-BASED MODELLING

3.1

Agent-Based Modeling Modeling the behaviour of physarum polycephalum in a digital environment requires a highly adaptive form of programming. Through our research we arrived at the conclusion that the best option was to use an agent-based system. Unlike Cellular Automata systems, which are made up of stationary cells which simply fluctuate between states (on/off, dead/alive, etc.), an agent-based system is made up of autonomous “agents” which have a small intelligence built in to them and are able to move about and interact with each other and their environment. These agents can represent anything, be it a point in space, a bird, or even a human being. The important thing is their rules of interaction. Each individual has a set of rules for how it interacts with its surrounding environment and its neighbouring individuals. A possible example of these rules would be: If X happens, do Y. Through this interaction, these agents are capable of producing complex behaviours from simple rules. It is similar to natural phenomena such as starling murmurations (Fig. 3-1) or fish schooling (Fig. 3-2). In these behaviours, none of the individual birds or fish is aware of the global geometry of the group. All they are doing is sensing their immediate surroundings, and based on “rules” of proximity and movement, making decisions regarding how to move next. The result is an

amazing display of coherent group behaviour that is actually completely emergent. This is what agent-based programming aims for.

3-3 Agent based simulation of rovers on the surface of Mars

By defining simple rules for a system, the system itself is able to generate complex solutions. In this type of system the designer makes certain assumptions and observes the system as the system runs in order to observe the phenomena that arise from the agent behaviours. Agentbased systems are highly emergent and allow for realistic simulations of natural phenomena (Fig. 3-3). It is for this reason that we pursued an agent-based model in our further research. The environment for our simulations will be Rhinoceros 5.0 beta running on laptop computers making use of the Grasshopper plugin and we will be scripting in the Python scripting language.

31

SPACE SYNTAX

3.2

3

2

1 n = number of connected spaces d = depth values Σd = sum of depth values MD = Mean Depth = Σd / n I = Integration = 1/MD

3-4 Mean depth value (MD)

1

R

1

1 n=3 d = 1,1,1 Σd = 3 MD = Σd/n = 1 I=1

R

Space Syntax Built environments are spatial configurations that are not formed by nature but rather a collection of objects that are generated through certain logic.1 These configurations have a long-term effect on the space they occupy. In other words, “they aggregate by occupying a particular region of space for a long time”.2 “The built environment is the largest and most complex artefact that human beings construct apart from society itself ”.3 This results in the emergence of cities that are subject to continual evolution.

Space syntax is the relationship between space and society which allows for the representation of spatial relationships.

The aim of space syntax is to capture the configuration of built elements and the emergence of their global complexity and translate them into spatial data. In order to analyse this data, the configuration of built elements needs to be converted to an axial map. The axial map is one of the main tools of space syntax. Axial maps are used in order to visualise the space between buildings and their surroundings. It is basically an abstraction of this space into straight lines, which results in a graph that can be further analysed in order to approximate how it is used by people. Space syntax analysis allows for a visual approximation of pedestrian movement in an urban settlement. It can identify areas

1. Hillier; 1996; p.69.

2. Hillier; 1996; p.68.

3. Hillier; 1996; p.68.

32

2

2

1 n=3 d = 1,2,3 Σd = 6 MD = Σd/n = 2 I = 0.5

R

n=3 d = 1,2,2 Σd = 5 MD = Σd/n = 1.67 I = 0.6

of high flow and which areas need to be better connected within an urban fabric.

Syntactic Measures In order to analyze the global complexity of cities, measures such as depth, integration, and connectivity can be used.

Depth is a measure which represents the distance from one space to another space. For each individual space R, R is treated as the root space. Next, every other space is assigned a value of depth which represents the number of spaces that you have to go through to get there from space R. Averaging these depth values will give the Mean Depth for space R which represents the average distance from space R to all other spaces (Fig. 3-4). “Integration measures how many turns one has to make from a street segment to reach all other street segments in the network within a radius, using shortest paths”.4 It is calculated as the inverse of Mean Depth (Fig. 3-5). “Theoretically, the integration measure shows the cognitive complexity of reaching a street, and is often argued to ‘predict' the pedestrian use of a street”.5 The first intersection segment requires only one turn, the second, two turns and so on. Integration is a measure of how accessible each segment is from all others, in other words it shows the potential of each segment as a destination for movement. “The closer a segment is to all others, 4. http://www.environment.gen.tr/human-settlements/557-what-isspace-syntax.html 5. http://www.environment.gen.tr/human-settlements/557-what-isspace-syntax.html

SPACE SYNTAX

3.2

MD = 2.5 I = 0.4

1

x Every axial line 0

2

2 3

2

3 3

Max value

1 1 2

3 3

2 3

the more promise it offers as a destination”.6 Connectivity is a value that represents the number of spaces that a particular space intersects (Fig. 3-6). Connectivity and Integration are the two measures that deal with human movement. Connectivity is a property that can be seen from each space. Wherever one is in the space, one can see how many neighbouring spaces it connects to. Integration, on the other hand, cannot be seen from a space, since it is a value that is determined based on the entire network, most of which cannot be seen from one space. An intelligible system is one in which well-connected spaces also tend to be well-integrated spaces. An unintelligible system is one where well-connected spaces are not well integrated so that the visible connections are misleading about the integration of that space in the system as a whole. By plotting the integration and connectivity values of all spaces of a network, a trend line can be extracted, the slope of which (R2) represents the intelligibility of the network.

1

don’t show the areas that are the most integrated, but rather, the areas that were most affected by the new network. Impact maps will become an integral part of our research as we aim to implement new networks into an existing city.

Impact maps will be used in order to ensure that the interventions that we suggest are actually positively affecting the existing urban environment.7 Designing with these measures

“Because these techniques allow us to deal graphically with the numerical properties of spatial layouts, we can also use them creatively in design. For example, extensive research has shown that patterns of movement in urban areas are strongly predicted by the distribution of integration in a simple line representation of the street grid”.8 By using these measures in urban design simulations, it is possible to gain more insight into urban patterns that are not clear to intuition. For example, we are able to determine areas that are segregated within the global network and need to Impact Maps be integrated or areas of high traffic flow that need more = 0.4 can be used in urban design connection. ThisI potential I = as 0.4 When adding a new network to an existing city map such in order to examine the effect of adding infrastructural a metro or bus service, there is an effect on the integration networks to the urban network. By adding another network of the original axial map. This effect is known as impact and to an existing one, the spatial configuration of all other can be visualised through the use of impact maps. Rather elements in the system will change and space syntax can than simply measuring the integration of the streets a secbe used as an evaluation tool to examine the effects of ond time after the implementation of the new network, an different networks on the urban network. impact map shows the change in integration. Impact maps

3-5 (Top) Integration (r = n) analysis definition The number of iterations is the same number of streets in the axial map to analize.

Segment Evaluated

3-6 (Bottom) Connectivity analysis definition

7. Gil; 2012; p.13.

6. Hillier; 2007-8; p.2.

8. Hillier; 1996; p.98.

33

NETWORK GRAPHS

3.3

Gabriel graph

Relative neighborhood graph

Pair of points evaluated A

Successful connection

dab

Failed connection

A

C

B

C B

A

B

Connectivity Graphs

Minimum Spanning Tree

When generating networks for a given set of points, there are a number of different graphs that can be generated: There is the Delaunay Triangulation, the Voronoi Diagram, the Gabriel Graph (GG), the Relative Neighbourhood Graph (RNG), and the Minimum Spanning Tree (MST) to name a few. The three graphs that we will use to analyse and evaluate our experiments are the GG, RNG, and the MST.

The Minimum Spanning Tree (MST) is a sub-graph of the Relative Neighbourhood Graph. The MST is the network with the shortest possible length that connects all points in a set without creating any closed loops (Fig. 3-11). The MST is a highly important graph in analyzing networks. It speaks to the efficiency of a network in terms of material used. It is used frequently in communications industries.

For example, cable companies will use the MST in order to lay the least amount of cable needed in order to connect the whole network.

Gabriel Graph The process for computing the Gabriel Graph is as follows: For a set of points, two points (a,b) are considered to be connected if no other point (c) in the set is contained within a circle which passes through a and b and whose diameter is the distance between a and b(dab) (Fig. 3-7 and Fig. 3-9).

Relative Neighbourhood Graph The Relative Neighbourhood Graph (RNG) is a sub-graph of the Gabriel Graph, meaning that all the lines in the RNG are present in the GG. The process for computing the RNG is as follows: for two points (a,b) circles are drawn centred on the each point with a radius equal to the distance between a and b (dab). Points a and b are considered to be connected if no other point (c) is contained within the overlapping area of these two circles (Fig. 3-7 and Fig. 3-10).

34

C B

A

Success/failure rules for Gabriel graph and Relative Neighborhood graph.

B

A

C

A

3-7

B

Application The reason for using these graphs is that they make potential suggestions of which points in a network should be connected. Each graph has its own set of rules for determining this which results in a variation of the number of connections. By comparing these connectivity graphs to a designed network, it is possible to evaluate whether or not the network adheres to the same rules. It can also be determined whether or not the network design is actually a sub-graph or a super-graph of one of the connectivity graphs.

NETWORK GRAPHS

3.3

3-8 Initial set of random points

3-9 Gabriel graph

3-10 Relative neighborhood graph

3 -11 Minimum spanning tree graph

35

APPLICATION OF THE METHODS

3.4

LAGOS (NIGERIA)

GLOBAL SCALE

REGIONAL SCALE

EXISTING CONDITIONS

TRANSPORT NODE

NETWORK

ADAPTATION OF FABRIC

Optimized path

LINES & STATIONS

EXPECTED USERS

STATION PLACEMENT

USAGE CHARACTERISTICS

36

RESULTING NETWORK

Flow distribution

DENSITY DISTRIBUTION

LE

NG RK

tion

Y ION

APPLICATION OF THE METHODS

3.4

LOCAL SCALE CULTURAL IDENTITY

PROGRAM DISTRIBUTION

Sectional typologies

MORPHOLOGIES

RESULTING URBAN SPACE

RESULTING BLOCKS

Design strategies

Design strategies Climatic strategies

Methods in the design process

3 -12

The process is developed in three scales that tackle the improvement of Lagos’ situation from different perspectives.

Research Workflow

In the global scale the new transport network is implemented. Slime Mould simulation is used to connect different areas in the city and the output is evaluated with space syntax analysis.

In the regional scale we deal with the impact of the implemented network in the different stations product of it.

Again, Slime Mould simulation is run to simulate the optimal route to and from the transport nodes accommodating the increase of flow in the area. Besides, the characteristics required for the morphologies design are determined and measured.

Local scale corresponds to the design of the system for urban morphologies and proposes a suggested design. The system translates flow values, a product of the regional scale development, into built volumes.

37

38

4.0

SITE: LAGOS (NIGERIA) 4.1 Characteristics 4.2 Locating activity nodes

39

CHARACTERISTICS

4.1

LAGOS (2025) MEXICO CITY

SEOUL

TOKYO

NEW YORK

PARIS

POPULATION = 1 Million

% USING PUBLIC TRANSPORT

100%

50%

75%

0%

BUSIEST STATIONS

0%

CUATRO CAMINOS

= 11,130,567

DAILY

TASQUENA

= 10,034,507 DAILY

INDIOS VERDES

= 10,391,215 DAILY

40

100%

100%

100%

100%

57%

63%

0%

0%

GANGNAM

SHINJUKU

= 125,810

DAILY

JAMSIL

= 3.64 MILLION/ DAY

IKEBUKURO

= 96,216 DAILY

= 2.71 MILLION/ DAY

SILLIM

SHIBUYA

= 95,467 DAILY

= 2.18 MILLION/ DAY

100%

40%

75%

0%

42 STREET- TIMES SQ

= 189,4256 DAILY

42 STREET

= 147,644 DAILY

34 STREET HERALD SQ

= 121,081 DAILY

0%

GARE DU NORD

= 1.80 MILLION/ DAY

GARE SAINT - LAZARE

= 147,644 DAILY

CHARACTERISTICS

4.1

Current population (millions) 16

1 Current Population Current 1 (millions) Population (millions)

14 16 12 14 10 12 8 10 6 8 4 6 2 4 0 2

Lagos

London

Lagos

London

0 80

Mexico City Mexico City

New York

Paris

Seoul

Tokyo

New York

Paris

Seoul

Tokyo

Mexico City Mexico City

New York

Paris

Seoul

Tokyo

New York

Paris

Seoul

Tokyo

% population using public transport

2 % Population Using Public % Population 2 Transit Using Public Transit

70 80 60 70 50 60 40 50 30 40 20 30 10 20 0 10

Lagos

London

Lagos

London

0 4,000,000 3,500,000 4,000,000 3,000,000 3 Busiest 3,500,000 2,500,000 Station 3,000,000 3 from Busiest 2,000,000 daily 2,500,000 1,500,000 Station passengers 2,000,000 1,000,000 from daily 1,500,000 500,000 passengers 79,894 During our initial discussions, we focused on cities with a 1,000,000 high projection of population growth because we felt this 500,000 79,894 Busiest would inherently require public transit to accommodate Lagos the growth. The three sites that were considered were Beihai Busiest Lagos (China), São Paulo (Brazil), and Lagos (Nigeria). The decision to use Lagos was made after studying further the infrastructure networks that existed in these locations. In São Paulo, the transport infrastructure has already been developed to a degree that any design intervention would act as an addition to the multiple methods available. Neither Beihai nor Lagos had a major transportation network in place, which made both ideal sites for further study. However, we selected Lagos because of its particular background.

Case Studies To contextualize our study, we began by looking at ratings of public transportation networks that were considered effective. The initial research in evaluating public transport networks helped to develop a vocabulary of terms for evaluation of networks in cities. The Green City Index provided

3,640,000 3,640,000

57,000

189,4256

1,800,000

1,130,567 189,4256

1,800,000

1,130,567

125,810

a basis for comparing different cities in the world in which included within42itsStreet study. From this index, informa57,000 Lagos 125,810 Waterloo Cuatro Gare Gangnam Shinjuku tion was provided populations, ofTokyo netLondon Caminos on the Du percentage Grand Seoul Mexico Nord Central works to area, and existing modesGare of transportation.

It also Cuatro Waterloo 42 Street Gangnam Shinjuku City Paris New York Caminos Du London the Grand Seoul Tokyo contains cities’ plans for developing their infrastructure.

Mexico Nord Central To compare the cities, we combined the information from City Paris New York the Green City Index, with the data from previous research on the commuting percentages of the populations and produced a matrix of networks (Fig. 4-1).

4-1 (Opposite page) Comparison of city infrastructures 4-2 Population and percentage of population using public transport in different cities.

The research showed that in terms of population, density, and commuters, Mexico City, Seoul, Tokyo, New York, and Paris were most similar to Lagos. Two cities were then selected for further study and we chose Mexico City and Tokyo. London was also added to our case studies because the information was available in surplus and we have a direct experience with the stations and the network.

The city that had the closest population conditions to Lagos from our matrix was Mexico City. The official population is estimated at about 8 million and 20 million unofficially. In 41

CHARACTERISTICS

4.1

16 14 12 10 88

2010 2025

C (E airo gy pt )

Casablanca

La (N gos ig er ia)

Ki (C nsh on as go a )

Tunis

Cairo

Algiers Alexandria

16

Dakar

14

Lagos

Addis Ababa Yaoundé

Abidjan Accra

Kinshasa

12 10

Nairobi Dar es Salam

Luanda Pretoria Johannesburg

Maputo

2010 2025

2010 2025

Cairo (Egypt)

LAGOS (Nigeria)

2010 2025 Kinshasa (Congo)

Durban Cape Town

4-3 (Top) Location of Africa's current biggest cities 4 -4 (Bottom) The three major African cities' population and expected in 2025

Lagos the official population is estimated at 10 million with an unofficial of 25 million. These numbers address the large percentages of the populations that exist in informal living conditions. In terms of transportation networks, this is important as the development areas of these informal settleBadagry ments are usually adjacent to the city districts or in close proximity to them. The Green City Index states that, “Africa has the highest proportion of city dwellers in informal settlements in the world”.1 The methods that these populations use to get to work are a mixture of local methods with public and private vehicles. These methods of transport are the Okada, Taxi, Danfos, Keke Marwa and bus. The Okada, Danfos and Keke are all local methods that transport one to three people and have the flexibility to move around congestion within the traffic. (Fig. 4-2).Since congestion is a common problem due to poor infrastructure, flooding, and rush hours, these methods serve large portions of the population that commute. The lack of public transport also creates a need for the public to find another method of transportation. To address this concern, the Lagos govern-

ment has proposed new transportation methods to accomEpe modate theIkorodu city’s projected population. In 2008, the Lagos Master Plan was initiated to bring a bus rapid transit (BRT) system to the public. Mexico City has a similar BRT in place that runs on dedicated lines throughout the city. Therefore, Ibeju Lekki for an estimate on the amount of passengers that can be carried by new systems put into place, we used an amount similar to that of Mexico City.

Lagos Growth Statistics Lagos is currently the 18th most populous city in the world and is expected to be 11th by 2025.2 The population is conservatively projected to grow to more than 25 million by 20253 and Lagos will be the 7th fastest growing economy in the world and the most populous city in Africa (Fig. 4.3 and Fig.4-4). There are 20 local government areas (LGA’s) but only 16 make up what is considered the metropolitan Lagos area (Fig. 4-5). In this area, there are 8 million people ac2. Siemens, p. 6

1. Siemens, p. 4 42

3. World Bank, 2011

CHARACTERISTICS

4.1

LAGOS

Ikorodu

Epe

Ibeju Lekki

Badagri

15,9 MILLION

10,5 MILLION

1910

5 MILLION

1952 1978

99.690

272.000

1910

1952

2010 2025

1978

2010

2025 4-5 Lagos densely-built areas expansion over time 43

CHARACTERISTICS

4.1

4-6 Existing Architecture in Lagos 44

CHARACTERISTICS

4.1

4-7 Traffic Conditions in Lagos 45

CHARACTERISTICS

4.1

BRT Route 1 BRT Route 2 BRT Route 3 BRT Route 4 BRT Route 5 BRT Lite Route Lagbus Priority Bus Scheme Route Bus corridor Pilot Bus Route BRT know bus stop Railway station Airport

4-8 Existing means of transportation 46

cording to a 2006 census, over an area of 999.6 km2, with an average density of 7941 ppl/km2. The population increase is estimated at 275,000 people per year, a rate of 6%.3 “In the last twenty years, explosive urban growth has continued and has primarily occurred in the southern parts of the city included westward in Ojo, eastward in Eti-Osa and increasingly now in Ogun State, north of the city (Fig. 4.5).4 Effects of growth in Lagos The inefficient urban plan in Lagos has provoked the city’s own particular characteristics. The historical areas have become highly dense with the result that open public spaces are non-existent. This is quite remarkable in a city where the use of streets is a common phenomenon. In these areas the urban tissue is configured with low-rise buildings mostly built during the 50s and 60s. Only certain areas present high-rise towers like in Lagos Island and the Financial District. A high percentage of the existing buildings have risk of collapse and the council of Lagos has initiated a process 4. Oloto, E.N. and Adebayo;The new Lagos.

CHARACTERISTICS

4.1

Traffic stopped

Normal pace

to demolish all the buildings under that risk, affecting to all sorts of programs such as towers, markets, housing, etc.

(Fig. 4.6).

The increase in population produced urban sprawl that lead to the formation of numerous slums throughout the city.

Accompanying this growth there is a lack of urban infrastructures in these areas such as sewage system or transport that connects to the other parts of the city.

In this situation the question is how can this city, in its current state, absorb the expected influx of population and at the same time improve living conditions. The low quality of the existing architecture and urbanism suggest the implementation of a new model to drive the new population increase (Fig. 4.7).

Existing Modes of Transit Currently in Lagos the modes of transit are the Okada, Taxis, Danfos, Keke Marwa and Keke Napep, BRT and Lag buses, ferry, lorries, and trailers (Fig. 4-8), all of them road

transport.5 As of 2000, the transport infrastructure and services were at levels that could support a population of no more than six million.3 The conclusion from this information is that the efficiency and productivity in the metropolitan area have been adversely affected by the growing weakness in the physical infrastructure required to support basic needs of the population. The current road network has a density at about 0.4 km/1000 population. 3 The conclusion is that due to the lack of infrastructure and projected population, the city will continue to experience large areas of congestions without an intervention (Fig. 4-9).

Aim of Network Design In our design we researched for the local modes of transportation. The existing system is a collection of informally organized means of transport that can adjust quickly to environmental conditions, such as rain which causes heavy flooding and traffic delays (Fig. 4-10). We are proposing an organized network system because the existing modes 5.  pau|ipopo.hubpages.com/hub/Transportation-in-Lagos-Nigeria

4-9 Congested Routes of Lagos 47

CHARACTERISTICS

4.1

48

CHARACTERISTICS

4.1

13%

81%

Buses and mini-buses (Danfos)

Taxi, private cars

1%

5%

Motorcycles (okada)

exacerbate the problems of the infrastructure. We have assumed that the projected growth will add to these issues and by suggesting a network design that accommodates this growth, the distribution of development will act in tandem with the network. Therefore, an array of growth and system nodes will result in which we can test the limitations of our system by the capacities at which it can handle.

Railway

4-10 Distribution of usage across different means of transportation

We are also suggesting that with this network, the areas that would normally be separated would be redistributed in the urban fabric through means of connection. There is known to be areas of great disparity throughout Lagos where the urban meets the rural and peri-urban conditions.

These peri-urban areas currently act as boundaries to the urban areas. Connecting these peri-urban areas would mean decreasing the “peri-urbanisation which leads to increasingly complex disparities.”6 Therefore the social structure of the households and local neighbourhoods would become integrated with the development of connections to a public network system.

6. Dupuy; 2008; P 211.

49

LOCATING ACTIVITY NODES

4.2

1 2 4

5 7

6

8

3

12 9

14

10 11 13

15

16

17

20 18

4-11 Position of activity centres in Lagos

1 - Otta 2 - Agege 3 - Ikorodu 4 - Ayobo 5 - Iyana / Ipaja 6 - Ikeja 7 - Egbeda 8 - Alimosho 9 - Igando 10 - Ikotun 11 - Isolo 12 - Oshodi 13 - Mowe / Ibafo 14 - Bariga / Oworo 15 - Alaba 16 - Festac 17 - Mile 2 18 - Ajegunle 19 - Apapa 20 - Lagos Island 21 - Obalende / Ikoyi 22 - Victoria Island

Node Placement In order to begin to generate a new network in Lagos, we first needed to define nodes that would drive the development of the network. In order to do this, we researched various aspects of the city in order to locate areas that are vital to the city’s operation. First, we looked at the existing public transportation networks in the city. In the case of Lagos, this consists of a Bus Rapid Transit system and other privately owned bus services. From this information, we were able to see which areas were not well connected by the existing transportation network. We also looked for areas where other modes of transportation converge such as the Nigerian Railway Corporation, Murtala Muhammed International Airport, or the ferry services that serve Lagos Lagoon. When considering the placement of nodes for our network, it was crucial that they be in close proximity to these other modes of transportation in order to better connect the existing fabric to the new network.

Another criterion that we looked for in the city was cultural and economic centres. Lagos has several large scale markets which are highly important to the city. For example, the Alaba International Electronics Market is the largest importer of electronics in Africa. Up to fifteen shipping containers of discarded electronics from Europe and Asia arrive every

19

21 22

day”.1 Other important cultural and economic areas such as religious sites, universities, and the central business district were also considered as places that would need to be well connected (Fig. 4-11).

Node Ranking In order to choose the right activity points it was important to consider their development according to the evolution of the city. As a result we analysed the growth and the development of the city from 1900 to 2020. The urban growth from 1900 started from Lagos Island where the central business district is currently located. As the city continued to grow, the central business district became a fixed location.

The city expanded towards Lagos Mainland from 1900 to 1960 where the Nigerian Railway Corporation constructed its first rail line. The growth of the urban area with its corresponding activity points are also shown from 1960 to 1980 and from 1980 to 2012.

In order to evaluate the activity points for their relative importance, we used a method of evaluation that had been used in a previous study of London.2 The purpose of this study was to push for a stronger integration between geo1. http://www.moneyweek.com/news-and-charts/economics/ global/a-recycling-fraud-56116 2. Chiaradia, Alain; Law, Stephen; Schwander, Christian. 2012

50

LOCATING ACTIVITY NODES

4.2

1900 Rank

1900-1960 Rank

1960-1980 Rank

1980-2012 Rank

2012-2020 Rank

20

13

13

12

5

21

12

12

5

7

20

14

14

8

19

20

13

12

21

17

11

2

18

17

14

19

8

4

21

18

6

8

7

13

6

10

20

22

6

1

20

11

9

10

2

17

4

18

16

9

1

21

19

16

21

15

15

3

22

19

3

22

metric analysis and geographical analysis. The nodes were ranked according to their closeness centrality (distance to the geometric centre [GC]) of the urban boundary at that time period. In our evaluation the geometric method was coupled with neighbourhood density (Fig. 4-13) and thereafter the nodes were ranked accordingly in five different time periods from 1900 to 2020 (Fig. 4-12). The simulation for future urban growth was taken from a previous study that was done for Lagos using a dynamic spatial model prototype.3 Conclusion Both similarities and greater variances in ranking of the nodes between the time periods were observed. The nodes in Lagos Mainland have the highest rank due to their density and centrality to the GC of the boundary at each period. However, the nodes in Lagos Island have a lower rank which was expected due to their distance to the GC of the urban boundary. The ranking was also done for the simulated urban boundary for 2020 which to some extent shows similar results. In addition it takes into account the urban growth and gives higher ranking to the nodes that are expected to become more important both geometrically and demographically.

4-12 Node ranking

Geometric Method C = Geometric center of urban fabric Di = Distance from node i to C Rank Di

Geometric Method coupled with neighbourhood density C = Geometric center of urban fabric Di = Distance from node i to C Ri = Radius of Node i (proportional to its density) Rank Di / Ri

4-13 Methods for ranking nodes

3.  Barredo I. Jose; Demicheli Luca. 2003 51

52

5.0

NETWORK DEVELOPMENT 5.1 Slime mould. Physical testing 5.2 Slime mould. Digital simulation 5.3 Historic underground network 5.4 Generation of the network 5.5 Node ranking 5.6 Space Syntax 5.7 Line division 5.8 Station placement 5.9 Evaluation

53

SLIME MOULD. PHYSICAL TESTING

5.1

90

5-1

90

Test slime mould growth. The slime is positioned in the centre and the food in the edge of the petri dish. It is monitored daily until the network is fully mature. Then the slime pattern is compared with the Gabriel graph (left) and Relative neighborhood graph (right)

60

60

30

30 15

15

0 mm

0 mm

Day 01

Day 03

Day 05

Method

5-2 (Opposite Page) First experiment results. The top set shows the result of the 8 samples tested. At the bottom the evaluation diagrams of each once a mature slime is grown.

54

In order to better understand the growth of Physarum Polycephalum, we carried out several physical experiments using a kit for developing Physarum cultures. The experiments included a pre-grown culture of plasmodium which was used to subculture more plasmodia sample tests. The samples were monitored daily in order to understand the plasmodium’s foraging behavior and also to calibrate its growth behavior to a mathematical model using network graph analysis (Fig. 5-1). Each sample was prepared in a 90 mm petri dish. Non-nutrient agar was first poured into each petri dish. After the agar was dried, sterile oatmeal flakes were placed on top of the agar surface for each petri dish.

A 1 cm2 block of agar on which a piece of plasmodium was present was then cut from the pre-grown culture and placed, with the plasmodium side down, on to the nonnutrient agar plus oatmeal flakes.1 Experiment 1 The first experiment included eight samples (Fig. 5-2). The observation from this experiment was that during plasmodium’s foraging process the cell senses the existing food sources and makes a decentralized route towards these food

1.  Bozzone; 2004.

SLIME MOULD. PHYSICAL TESTING

5.1

1 - Sample producing sporangia due to lack of nutrients

5

2

3

6

7- Network extends into 3D.

1

5

4- Mold escaped the petri dish.

2

6

3

7

8

4

8

Relative Neighborhood Graph Gabriel Graph 55

SLIME MOULD. PHYSICAL TESTING

5.1 5-3

Experiment 2: Map of Tokyo underground with highest flow stations called out to be used as food sources

C-19 H-21

M-25

Y-09

A-20 N-10 I-10

S-01 E-27

F-16

T-23 G-09 Z-01

5-4

Station No.

Opening Date

~ passengers a day

Station Rankings

F-16

1885

~ 580,367

Z-01

1885

~ 580,367

M-25

1903

~ 470,284

Y-09

1903

~ 470,284

C-19

1943

~ 433,614

H-21

1896

~ 287,488

T-23

1958

~ 271,057

G-09

1934

~ 241,513

N-10

1928

~ 166,452

S-01

1885

~ 133,104

A-20

1960

~ 92,984

E-27

1885

~ 65,607

I-10

1972

~ 62,097

56

SLIME MOULD. PHYSICAL TESTING

5.1

sources with a protoplasmic tube.2 It was also observed that the morphology of the protoplasmic network continuously changes and never reaches a fixed, stable point.3 Because this network doesn’t reach a stable configuration, for the next culture, the sample was monitored daily and compared to mathematical graphs such as the Gabriel Graph (GG) and the Relative Neighborhood Graph (RNG) (Fig. 5-2).

This was done in order to extract a generalized network from our experiments. We were aiming for a behavior closer to the RNG since the GG tends to gives a more redundant network. By comparing the experiment with the GG and the RNG, it was observed that the protoplasmic network is a super graph of the RNG and a sub graph of the GG. Experiment 2 After sub culturing the plasmodium and observing the continuous evolution of the plasmodium network in order to maximize its access to the available food sources, we narrowed our experiments to replicate transportation networks.

A case study was carried out on the Tokyo subway system.

Each line in the network was analyzed and areas of high traffic flow were extracted from the lines which would be represented by food sources in our experiment (Fig. 5-3 and Fig. 5-4). The experiment was again done in a 90 mm petri dish with non-nutrient agar and oat flakes. After the agar was poured and dried in the petri dish, oat flakes were arranged in the pattern of busiest stations in each line of the Tokyo subway system. The 1 cm2 plasmodium was placed on the oat flake which represented Shibuya station which has the highest flow and is one of the oldest stations in the subway system.

The experiment captures the basic dynamics of a network with an end result that is comparable to the Tokyo subway system. The plasmodium started to consume from its closest food source which is the Shibuya station and the protoplasmic network then started to propagate from Shibuya station towards the North and North-West. After monitoring the experiment for four days, it was observed that the protoplasmic network approximates the Tokyo subway system which validates our method (Fig. 5-5).

In general, it was observed that the structure of the protoplasmic network from the experiments does not depend significantly on the size and shape of the container, nor the amount of agar that is poured after the container is covered, but mainly on the configuration of sources of nutrients.4 Experiment 3 The final experiment was carried out using our site in Lagos, Nigeria as the template for placing the food sources. The test was done in a rectangular container 140 mm ⨯ 100 mm, fully covered in agar. A schematic map of the previously defined 22 activity points within the urban boundary was produced in order to set up the experiment. Oat flakes were arranged on top of the agar surface in the pattern of the activity points and the plasmodium was inoculated in Lagos Island. This was done because the island is where the urban growth started in 1900, and the urban boundary evolved from there. Even though this experiment was carried out using simple assumptions, the protoplasmic network is capable of reproducing the dynamics of network formation that we are interested in through foraging and adaptation.

5-5

2. Adamatzky; Jones; 2010.

3. Adamatzky; Jones; 2010.

Experiment 2 results

4. Adamatzky; Jones; 2010.

90 90 90

60 60 60

30 15 0 mm

30 30 15 15 0 mm 0 mm

Relative Neighborhood Graph Gabriel Graph 57

SLIME MOULD. PHYSICAL TESTING

5.1

5-6 Physical experiment within metropolitan Lagos.

Activity nodes

5-7 Network pattern from slime mould connections

58

SLIME MOULD. PHYSICAL TESTING

5.1

5-8

Node and Line Abstraction

Network Connection Patterns connecting disconnected node

The abstraction of the connections between the hubs was viewed as any line that the plasmodium created on the agar.

There was visually a difference in the dominating connections between the hubs with thicker and stronger growth that we abstracted as the main paths (Fig. 5-6). After assessing these paths, the lines between the oats were shown and connected. From these results, the connections were abstracted to straight lines to show basic ideas of connection (Fig. 5-7). The resulting network had all nodes connected except for one. This node was reconnected to the network manually (Fig. 5-8). This resulting network will be used in a later chapter for the purpose of evaluation.

Results This process gave us a method that would be effective for using the physical slime mould tests in order to compare them to later network designs. Through abstracting the connections shown in the network into straight lines, the networks can easily be compared to connectivity graphs and network design solutions. Therefore using the physical experiments at the scale of the city will provide a powerful comparison and evaluation tool for our further studies.

59

SLIME MOULD. DIGITAL SIMULATION

5.2

90

60

30

15 0 mm

5-9 Test slime mould growth sample and the distribution of “food nodes” 60

SLIME MOULD. DIGITAL SIMULATION

5.2

5-10 The sensing distance responds to the maximum reach to locate food of each cell that forms the slime mould.

SENSING DISTANCE

5-11 The sensing angle is the range of vision of each cell that forms the slme mould.

SENSING ANGLE < 360

Extracting Parameters During the physical slime mould experiments, we observed the growth and foraging behaviour of the mould in order to extract parameters that could be translated into algorithmic instructions (Fig. 5-9). These parameters would later serve as the guiding forces for creating a bio-mimetic algorithm based on the behaviours of the slime mould for generating networks. The first important parameter that we observed was a sensing distance restriction. The indirect growth of the network branches suggests that there is a limit to the distance at which the mould can sense food sources (Fig. 5-10). If there were no restriction on the sensing distance, all of the network branches would be straight lines

connecting food sources. The second parameter that we observed was a restriction on the angle that the mould can sense. If the mould had a perfect 360 degree sensing angle, the growth of the network would be unidirectional towards the closest food source. However, the fact that the mould begins its growth radially suggests that there is a restriction on this angle which limits the sensing capability of the mould (Fig. 5-11).

61

SLIME MOULD. DIGITAL SIMULATION

5.2

Computational Adaptation of Slime Mould Logic

Food Source Behaviour

After extensive study, the slime mould foraging behaviour was abstracted into an agent-based computational simulation. The plasmodium was abstracted into a collection of individual “cells” (in this case a 3D point in Rhino). Food sources were marked as circles of varying radii which would determine the number of cells spawned from each node (Fig. 5-12). The cells and food sources were both coded to have a primitive intelligence about their surroundings and how they should behave.

Each food source also has a primitive intelligence, which is much less complex than that of the cells. The food sources simply are able to keep track of how many times a cell has passed through it. The purpose of this is so that when we analyze the network that results from the simulation, we are able to see which nodes were used the most, and therefore define those nodes as hubs of the network.

Cell Behaviour

The first experiments were done in a digital “petri dish” with food sources placed in the same pattern as was used in the physical experiments. The purpose of these initial experiments was not only to ensure that the cells were behaving properly, but also to find the parameter settings that gave the most well defined and well distributed networks. These networks were then compared to the following proximity graphs: Gabriel Graph, Relative Neighbourhood Graph, and the Minimum Spanning Tree (Fig. 5-15), which were described in an earlier section. The performance of the networks (P) was calculated as the length of the minimum spanning tree divided by the total length of the network.

Each individual cell has a built-in intelligence. This can be broken down into two parts: a sensing behaviour and a movement behaviour. Each cell begins with an initial start vector from the centre of the node from which it is originating. A random percentage of the cells are assigned a vector towards a major “destination”. In the case of a city, this destination could represent the main direction of travel. The two most essential parameters which influence the sensing and movement behaviours are the sensing angle and the maximum sensing distance. The sensing angle (SA) defines a field of “vision” for each cell. If a food source is within this field of vision, the cell registers it as a “visible” food source. From the list of its visible food sources, the cell then selects the closest one and will then test it against the maximum sensing distance. The maximum sensing distance (SD) represents the proximity within which a food source must be in order to affect the cell’s motion. What this means is that even if a food source is visible to a cell, the cell will continue along its initial vector until it is within the maximum sensing distance. Once this is the case, the cell begins to move towards the food source (Fig. 5-13). If there are no food sources within the sensing angle, the cell has two alternatives. The first is that it will check to see if there are any other cells in its field of vision. If there are, it will create a vector to the centre point of these neighbouring cells. If there is absolutely nothing within its field of view, the cell will rotate at a random angle and continue its search (Fig. 5-14).

62

Digital Experiments

The performance of the network is evaluated by comparing the total length of the network to the total length of the minimum spanning tree graph. The performance ratio is a number between 0 and 1(in a fully connected graph), 1 being the best possible performance. It is possible for this value to go above 1, but what this means is that not all of the nodes in the graph are connected. Graphs that result in a performance ratio that is greater than 1 either need to be modified or disregarded. In order to modify the graphs, lines can be taken from the other connectivity graphs in order to make a complete network.

Results By varying the sensing angles and sensing distances, we were able to determine which changes in these parameters would lead to which changes in the overall pattern of the result-

SLIME MOULD. DIGITAL SIMULATION

5.2

5-12 Node division and Spawning of Cells

5-13 Behavior of a particle when a food source is present within the cell area of vision.

Geometric centre

5-14 Behavior of a particle when no food source is within the cell area of vision.

63

SLIME MOULD. DIGITAL SIMULATION

5.2

5-15 Connection graphs

Gabriel graph

Relative neighborhood graph

Minimum span tree

SD : 70 SA : 30 P : 0.513

SD : 70 SA : 45 P : 0.552

SD : 70 SA : 90 P : 0.566

SD : 70 SA : 150 P : 0.747

SD : 70 SA : 170 P : 0.787

SD : 70 SA : 180 P : 1.335

SD : 35 SA : 45 P : 0.502

SD : 35 SA : 90 P : 0.561

SD : 35 SA : 150 P : 0.724

5-16 First set of experiments

5-17 Second set of experiments

64

SLIME MOULD. DIGITAL SIMULATION

5.2

ing networks. First, the sensing distance was kept constant at 70 units. The sensing angle was the only parameter that was varied. The results showed that smaller sensing angles result in networks that are difficult to discern or are overly connected. This means that points are connected to more nodes than are necessary, resulting in a redundant and wasteful network. A sensing angle of 30 degrees resulted in a network in which almost every node had at least 3 lines coming out of it, resulting in a low performance ratio. An angle of 45 began to refine some of the nodes, but was still not very coherent or performative. As the sensing angle is increased the graphs move closer and closer towards the previously mentioned proximity graphs. A sensing angle of 170 degrees led to a very close approximation of the minimum spanning tree and a high performance ratio. Angles past 170 degrees lead to networks that were disconnected.

For example, a sensing angle of 180 degrees produced a result that very well defined, but not fully connected which resulted in a performance ratio over 1 (Fig. 5-16).

In order to provide more direct pathways, the sensing angle was lowered to 150 degrees. This resulted in a network that closely approximated the relative neighbourhood graph which allows for more connections than the MST. This is advantageous because the MST is not always the most logical path for moving through a network. In Fig. 5-18, if a person wanted to go from point B to point D, he would want to go directly there, not from point B to point A to point C and then finally to point D. Allowing for more connections provides a more appropriate network for being applied to transportation.

In the next experiment, angles 45, 90, and 150 were tested with a sensing distance of 35 (half that of the previous experiment) (Fig. 5-17). This had a major effect on the coherence of the resulting networks as well. At 45 degrees, the network created a central, emergent node. This node was not one of the predefined nodes, but rather it was created by the agents of the system. The network showed a similar performance ratio to the previous test. At 90 degrees, the network also showed a similar performance. At 150 degrees, emergent nodes appeared along with a similar performance.

In order to link this sensing distance to any network, the

relationship we used is Ds = Davg/3 where Ds is the sensing distance and Davg is the average distance between points.

B

E

D

Conclusions By varying the main behavioural parameters of the computational system, we were able to determine which settings would produce the most effective network solutions.

A sensing angle of 150 degrees will be used in the further experiments as it produces networks that perform well, but also provides enough connection so as to provide sensible routes through the network. Also, the chosen sensing distance has great potential as it allows for emergence within the system. Even though the initial nodes are placed by a human user, the system itself creates its own new nodes based on the rules governing its behaviour.

F A

C G

Relative Neighborhood

B

E

D

F A

C

Gabriel

G

B

E

D

F A

C G

Minimum Span Tree

B

E

D

F A

C

More logical paths

G

5-18 Network connection methods

65

HISTORIC UNDERGROUND NETWORKS

5.3

5-19

Before 1863

1930

1863 (inauguration)

1940

1870

1950

1880

1960

1890

1970

1900

1980

1910

1990

1920

2000 until present

From top to bottom and left to right, diagrams of the evolution of London underground network with the redistribution of population density.

Persons per Km2 Under 100 100 - 1000 1000 - 5000 5000 - 10000 Over 10000 66

HISTORIC UNDERGROUND NETWORKS

5.3

London and Tokyo were our two case studies of focus for existing networks. They are analyzed in terms of spatial implications of the city growth and connectivity within it.

London was the first underground network in the world.

Tokyo’s underground started with London as a reference but presents a different result. In both examples, the networks present high efficiency and deal with very large populations.

First it must be said that the underground as a transport system cannot be observed as independent from other means of transportation in the city. The presence of bus or railway modifies the flow of users within the system increasing the use of certain lines or hubs. Therefore, an efficient design of the network should have dealt with this dynamic.

London The London underground commonly known as “the tube” opened in 1863 becoming the first underground line built in the world. The initial construction was conceived to connect Euston, King’s Cross, Paddington, London Bridge, Bishopsgate, and Waterloo stations which were the major terminal railway stations within the city. This new means of transportation would relieve the intense traffic in the city from travelers coming to London. As soon as it was inaugurated, the underground was a complete success. As private companies owned the underground, competitiveness soon arose and the network lived through a big boom of development.

London’s underground transport system spreads over the territory in all directions as no geographical barriers are

present. The technology to open tunnels under the river was researched in an early state of the network, so the river does not form a barrier for the network or the city layout.

In regards to the spatial evolution over time (Fig 5-19), it is seen that the initial linear construction between stations leads immediately to a spread over the territory. It is interesting that as branches spread outside the city centre an inner circle line is built. This loop line works as an interchangeable line for all the others, which converge in the city centre.

5-20 Diagram of London tube network with stations in their real relative position

The links between lines happen with two different mechanisms: single point intersections and overlapping lines. With the former, some stations can have a degree of connectivity greater than two, meaning that more than two neighbouring stations can be reached from that station. The latter happens both in the city centre, along the inner loop and along some branches. This could be a way to enhance connectivity in areas far away from the city centre where more lines converge, thus introducing redundancy in the service. This redundancy is necessary to solve accidental breakdowns in the network or increasing the options to arrive at the destination.

The changes of population density with the spatial layout of the network over time show that the network develops and disperses the population throughout the area (Fig. 5-19 and Fig 5-20), as travel through the territory becomes more accessible. Therefore, the city centre dissipates its density and thus increases the quality of life.

67

HISTORIC UNDERGROUND NETWORKS

5.3

1940

1980

1950

1990

1960

2000

1970

2012

5-21 Evolution of Tokyo underground network 68

HISTORIC UNDERGROUND NETWORK

5.3

Tokyo Currently, Tokyo is considered one of the most efficient cities in terms of public transport with a population of 36.5 million inhabitants in 2009.1 The car was implemented quite late in the city and as a result, the street layout was not designed for them. Therefore, crossing the city is easier and faster using the train or underground than car. An interesting fact is that the population in the city changes by almost 2.5 million people between day and night, and the average number of commuters per day is 8.7 million people. The transport system is achieving dangerous levels of congestion which in some stations goes up to 199 percent of their design capacity.

In comparison to London, Tokyoâ&#x20AC;&#x2122;s directional growth is constrained because of its proximity to the Tokyo bay, a fact which results in difference in connectivity regarding London (Fig. 5-21 and Fig.5-23).

The most populated areas in the city form a ring surrounding the geometrical centre, along the bay waterfront and

along lines that branch out from the city. On the one hand, the centre of the city contains most of the transport stations in the metropolitan area and, on the other hand, the denser areas that branch out from the city coincide with the underground and the train railway. This highlights a greater integration of areas involving the network due to the influence of this means of transportation.

Connectivity between lines is different than the case of London. Comparing Fig 5-24 and Fig 5-25, it is seen that the denser areas present a higher number of stations. Most of the flow of users comes from the outskirts of the city meaning that branches play an essential role in the commuting to the city centre. The connectivity between lines is not like it is in London, where lines overlap. In Tokyo, the response to connectivity is done by an increase of stations in the most trafficked areas such as the city centre. This effect is similar to New York (Fig 5-22). Both examples involve high densities and constrained space. In Tokyo, more loops are present in the network formed by different lines, a fact that increases redundancy within the system.

5-22 Underground map of New York

1.â&#x20AC;&#x201A; United Nations DESA Population division; 2009; p.6.

5-23 Diagram of Tokyo tube network with stations in their real relative position 69

HISTORIC UNDERGROUND NETWORKS

5.3

70

HISTORIC UNDERGROUND NETWORKS

5.3

Conclusions

5-24

Both networks present a high level of use despite a different arrangement in space and connection between lines. Success of the network in both examples lies in the connectivity with the hotspots and the ability to change overtime.

Changes may happen both in the use of a particular hub, mainly due to change of programs or attraction points in the city, and by extensions over time. The former is based on the ability to change service in the network and changes in the hubs to accommodate a bigger or smaller number of users. The latter is done by the extension of the branches.

Redundancy allows different choices of paths which enhances possibilities of mobility.

Map of the metropolitan area of Tokyo showing the gradiation of densities. Maximum in the city centre (purple) and minimum (white).

5-25 Diagram showing the location of stations in the area of the city of Tokyo.

Another issue shown within these two networks is the level of congestion. Usually, the network is designed expecting the increase of users for 20 or 30 years. In both examples, the system runs at over 100% capacity. This is translated into significant reduction of comfort in its use or delays in the system. The question is how can a network change its service to address major changes in use without compromising comfort?

71

GENERATION OF THE NETWORK

5.4

5-26 Minimum spanning tree graph

Minimum Spanning Tree Performance Factor: P = lenminspan / lennetwork

5-27 (left) Network 01 5-28 (right) Network 02

P = 0.853377968776 Disconnected Nodes

P = 0.735245379795 North Not Well Connected

P = 0.630844436567

P = 0.670234679234

5-29 (left) Network 02-a North / south Option 1 5-30 (right) Network 02-b North / South Option 2

72

GENERATION OF THE NETWORK

5.4

Generating the Lagos Network In order to create a network for implementation in Lagos, it was first necessary to determine what the major nodes in the city are. Through our research we were able to designate 22 activity points within the city. A circle was placed at each one of these points on the map. The radius of the circle was proportional to the density of the specific neighbourhood in which the node fell. This affected the number of cells that would be spawned at each node as was described before. From these 22 nodes, we ran an initial simulation using the slime mould algorithm we had developed using the parameters that we had extracted from the generic digital experiments.

Emergence in the System This initial simulation led to a well-defined network, but also showed evidence of emergent nodes developing.

Despite the limited intelligence of the individual agents in the system, they occasionally create new and unexpected changes in the system. There was one node in which a clear branching structure had appeared creating a new node. Another anomaly was that one node was connected to the network but the path there was poorly defined.

Our conclusion from this was that we should rerun the simulation but add in new nodes at the branching locations and at these areas of low resolution. We believed that this would produce a similar network, but would be cleaner and better defined. The result of this second run of the simulation confirmed our hypothesis. The resulting network was virtually identical to the previous one, but at the points where the first simulation had suggested new emergent nodes, the network was much cleaner and more legible. The new challenge was extracting an effective network to be evaluated for its performance factor (P) as was done in the earlier experiments (Fig. 5-26).

network, it was important that there be good access to all parts of the network. This network was effective in this respect except for in the North/South direction. It had three clear East/West routes, and two North/South routes which spanned the whole extent of the bounding area.

However, the third North/South route only went halfway up the coast line. In order to address this, we produced two alternate networks with different lines added to complete the third North/South route. (Figs 5-29, 5-30) Each one of these was evaluated and compared. The resulting network (2b) had a P value of .670, all nodes were connected, and all directions in the network were well serviced.

Physical Simulation The last evaluation we ran in order to validate our system was to compare the digital simulation to a physical slime mould test. We prepared a slime mould culture overlaid on a map of Lagos with oat flakes placed at the same activity points as in the digital test. We then brought the network that resulted from this test into the digital environment in order to evaluate it for its P value. Because of the complexity of the slime mould network, we extracted the most important lines from the network. The first evaluation returned a P value of 0.827, but there was one disconnected node. After connecting this node to the network, the P value was 0.724 (Fig 5-31). These similar results validate our algorithmâ&#x20AC;&#x2122;s ability to create comparable solutions to real slime mould simulations.

Evaluating the results Two separate networks were extracted from the simulation.

The first network was the network that resulted at the end of the simulation (Fig 5-27). This network was very well defined but had more or less abandoned several nodes. As a result, when this network was evaluated for its P value, it had an extremely high number (.853). This number is misleading, however, because several nodes are not connected to the network. Because of this, we decided to extract a second network from midway through the simulation. In this network, all of the nodes were connected to the network (Fig 5-28). We then evaluated this network for its P value and got a result of .735. However, since this network was to be used as a transportation

5-31 P = 0.72420912472

Modified slime mould resulting network

73

NODE RANKING

5.5

5-32 Simulated Slime Mould method the number represents the number of times the cells pass through the nodes.

Ranking Comparison As the simulation ran, it took into account every time a cell passed through a node (Fig. 5-32). At the end, the nodes were ranked based on this traffic count. These ranks were then compared to the original ranking based on the geometry of the urban area and the neighbourhood densities (Fig. 5-33). The results showed that the rankings were similar uniformly, except for the nodes on Lagos Island. The digital simulation ranked them much higher than did the geometric/neighbourhood density method. However, these nodes were expected to be anomalies in the rankings because of their isolated location on the island. As the island cannot expand as the urban fabric grows, the island nodes will be further and further away from the geometric centre of the urban fabric. These nodes still remain extremely important to Lagos, however, because the central business district is still located here.

Conclusion By comparing these different methods of evaluation, we were able to validate the strength, accuracy, and potentials of our system. The performance evaluation of both the digital network and the physical slime mould network show that the digital system is able to produce similar network outputs to those from the physical tests. The ranking comparisons show that the agents within the digital system tend to use the various activity points in a fashion that is similar to the rankings of importance from the geometric density method. Based on these findings, the digital output of the system can be evaluated as part of the greater urban fabric of Lagos.

74

NODE RANKING

5.5

5-33 Rankings from the simulation compared to the geometric / density rankings over time

1 2 4 7

E1

5 6

8

3

12 9 10 11

14 13

15

16

E2

17 18 19

20

21 22

1900 Rank

1900-1960 Rank

1960-1980 Rank

1980-2012 Rank

2012-2020 Rank

SIMULATION Rank

20

13

13

12

5

20

21

12

12

5

7

12

20

14

14

8

21

19

20

13

12

13

21

17

11

2

5

18

17

14

11

19

8

4

14

21

18

6

10

8

7

13

7

6

10

20

E2

E2

6

1

17

22

20

11

19

9

10

9

2

17

18

4

18

2

E2

9

E1

16

21

6

E1

E1

16

1

E2

3

19

16

4

21

15

8

15

3

1

22

19

22

3

22

15 75

SPACE SYNTAX

5.6 Integration

0

76

1

SPACE SYNTAX

5.6

5-34 (right)

R2 = 0.0233

Existing Lagos axial map

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Intersection with transport network

Connectivity

Connection Line

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos 5-35 (left)

Integration [Hh]

Integration [Hh] R3

R2 = 0.057

Linking transport network to street layout. In order to make a multi-modal network, the public transport network was linked to the Lagos axial map by finding the nearest axial line to the public transport node.

Integration [Hh]

Lagos is the former political and current commercial capital of Nigeria.1 Lagos’s start came with the boom with the oil industry, in the 1970’s so it has infrastructure that most big cities in Africa are lacking. The contrast is visible in Lagos; it has elements of a modern city but also a very strong presence of the “informal”.2 Lagos is a coastal city with an estimated annual growth rate of 6 percent.3 In 2025, Lagos will be the eleventh largest city in the world.4 Some parts of the city act as self-organizing entities, like the Alaba International Electronics market. There are also some selfgenerated neighborhoods which are referred to as “white spaces”. These are places that are blank on the map, that appear to have no activities, but that are actually busy and productive.5 The combination of Lagos’s huge size, underperforming urban infrastructure, and high levels of poverty 1. Immerwahr; 2007.

2. Felix, Wolting (Producers) & Van der Haak (Director); 2005.

3. World Bank, 2011.

became a compelling reason for us to analyze and enhance the accessibility and integration of neighborhoods into the wider urban fabric and life of Lagos.

Urban development of Lagos dramatically increased during the 1970’s due to the boom of the oil industry. Three main bridges were built in order to connect the Lagos mainland to Lagos Island and Victoria Island. The spatial structure of the city was changed after the development of these bridges became modernized according to a fairly conventional vision of what a modern city should look like in the 1970’s.

However, after the capital of Nigeria was moved to Abuja in 1991 it was almost as if Lagos started developing in reverse of the direction it was intended.6 The focus of this analysis is to understand the movement and accessibility of the street network within the urban form of Lagos. In order to analyze the proposed network and to evaluate its effect on the urban fabric, a model had to be developed to reflect the effect of different degrees of

4. Siemens; p. 6.

5. Felix, Wolting (Producers) & Van der Haak (Director); 2005.

6. Immerwahr; 2007.

5-36 (Opposite page) Lagos existing urban fabric space syntax analysis.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3) 77

SPACE SYNTAX

5.6 Integration

0

1

Network 01 layout

78

SPACE SYNTAX

5.6

5-37

R2 = 0.0803

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 01.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3243

Integration [Hh]

public transportation networks integrated within the urban fabric. For this analysis, the theory of natural movement is applicable to the exploration of the spatial pattern which is analyzed through the use of Space Syntax using Depthmap software.

and attractions such as shops and offices. These attractions are located to take advantage of the opportunities offered by the spatial configuration.8

The theory of natural movement is the relationship between the structure of the urban grid and movement densities along lines.7 The structure of the city itself accounts for much of the variation in movement densities. The colors in the axial map of Lagos represent densities of moving people which is expressed with integration values. In other words, the theory suggests that movement is fundamentally a morphological issue in urbanism. In order to have sufficient and well used urban spaces, local properties of the space such as their form, size, and physical components are not as important as its configuration in relationship to the wider urban system. The configuration of the urban space and its relation to its integrated networks is the main generator of the movement patterns and not the local properties

One of the most effective set of theories and associated tools currently used in a number of locations is Natural Movement Theory and Space Syntax, which are applicable to investigate the relationship between spatial configuration and movement.

7.â&#x20AC;&#x201A; Hillier; 1996; p.120.

Applying Space Syntax

In order to analyze the Lagos urban structure, an axial map of the city was produced. In Depthmap, the interaction of the different mobility networks and their effect on the urban configuration was investigated. Several measures such as connectivity and integration (both global and local) were investigated and the correlation of these measures was compared for different networks to see which network gives a better correlation value and is therefore more intelligible.

8.â&#x20AC;&#x201A; Hillier; 1996.

5-38 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 01.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3) 79

SPACE SYNTAX

5.6 Integration

0

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Network 02 layout

80

SPACE SYNTAX

5.6

5-39

R2 = 0.0815

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3345

Integration [Hh]

The spatial agglomeration of the urban form was evaluated at different scales by calculating integration according to different radii. For analyzing the global integration the radius was equal to n and for the local integration the radius was equal to three. A radius of n means analyzing the whole system where as a radius of three takes everything up to and including three turns from the base line.

Lagos Urban Fabric The distribution of global and local integration in the Lagos axial map is shown in Fig 5-36. It shows that the city expands from the south to the north and then spreads into the self-generated neighborhoods towards the east and west of the city with lower integration and accessibility values.

The white spaces and the self-generated neighborhoods are the most segregated areas. One of the problems that led to this is their isolation as a result of poor street connectivity, which makes these areas not function properly within the urban system. The map also confirms that Lagos has a centralized Business District which is in Lagos Island. There is high integration from Lagos mainland towards the island which shows where the highest flow of movement is concentrated.

The spaces coloured red are the ones that benefit most from spatial agglomeration. The disadvantage of spatial agglomeration is congestion. One factor that causes congestion is the lack of spatial connectivity which concentrates all the traffic into a few routes. The difference in spatial measures of different urban environments is linked to their multi-scale transport systems. For example, connectivity has a high level of correlation at low radius with pedestrian activity and it gets higher as the degree of transportation gets higher, going from cycling to vehicle, to bus, and to tram and metro.

The spatial and functional characteristics of Lagos were also considered. Some parts of Lagos suffer from spatial isolation and accessibility problems and lack the infrastructure to support the rate of population growth. Therefore, we want to propose a new rail network within the urban fabric that will take into account the inner-structure of areas suffering from spatial isolation and integrate them to the surrounding urban fabric. By increasing the connectivity of these areas, it will inherently attract more people and stimulate regeneration as well as easing the stress on the existing transportation infrastructure.

5-40 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3) 81

SPACE SYNTAX

5.6 Integration

0

1

Network 02-a layout

82

SPACE SYNTAX

5.6

5-41

R2 = 0.0824

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-a.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3468

Integration [Hh]

The axial line map reveals that urban street networks usually consist of a very small number of long lines and a very large number of small lines. In the axial line map of Lagos, 33% of the lines are above the average line length and 67% are below the average line length. The most integrated routes are among the 33% and are located in or near the geographical center of the city. The distribution is not homogeneous and shows the strongest and most dense agglomerations in red. Comparing these results to the literature of Lagos confirms the reliability of this approach.

When adding a rail network to an existing urban fabric, the new network will tend to become the most integrated route.

If this network were a road system, this could lead to congestion of traffic, but since it is a mass transit system, this is not a problem because it is designed to attract high usage.

As a result, areas around the network and the hubs have more commercial opportunities that can be created and as a consequence, more movement can be attracted to these areas. Integration is the predictor of movement and an effective criterion in studying the notion of accessibility and spatial isolation. Integrating the network within the urban fabric distributes the integration and accessibility over the whole area and makes connections with other integrated streets.

Construction of the multi-modal transportation model The different networks were produced as individual models, each including the BRT network. The configuration of these networks was then analyzed, using bus stops and the main stations of the rail networks in order to link them accordingly to the urban fabric.

The network models presented can be combined in an integrated multi-modal network model in order to study the interactions of the different mobility networks and their effect on urban configuration and movement levels.

To integrate different layers of transport networks to the urban fabric, an additional layer was added to each transport network which is referred to as the “modal interface” connecting the different modes.9 The “modal interface” is a link between the stop/station of different transportation networks and the axial map. It links the additional networks to the axial line map by finding the nearest axial line (Fig. 5-35).

For the BRT network the links are added at the locations of the 26 bus shelters and for the rail networks the links are added at the location of activity points.

9. Gil; 2012.

5-42 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-a.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3) 83

SPACE SYNTAX

5.6 Integration

0

1

Network 02-b layout

84

SPACE SYNTAX

5.6

5-43

R2 = 0.0848

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3521

Integration [Hh]

Once the multi-modal network has been modeled it can be translated into a graph and be further analyzed. The network was then imported into Depthmap as an axial line map and evaluated according to the measures. The connectivity, global and local integration measures were evaluated for the proposed rail networks for comparison.

Comparing the proposed rail networks From the results it is obvious that by adding another level of transportation to the existing layout of the city, the distances, connectivity and integration measures in Lagos would all change (Figs. 5-38, 5-40, 5-42 and 5-44). Intelligibility (R2 for global integration vs local integration) measures can be obtained from the addition of different networks (Figs. 5-34, 5-37, 5-39, 5-41 and 5-43).From the graphs it can be seen that the correlation between local and global integration of the Lagos urban fabric is 0.057 and with the integration of the BRT network together

with networks 01,02, 02-a and 02-b it increases to 0.3243, 0.3345, 0.3468 and 0.3926 respectively. Also, the correlation between the degree of connectivity and global integration gives similar results. It increases from 0.0233 from the Lagos urban fabric to 0.0803, 0.0815, 0.0824 and 0.0848 for the integration of the BRT network together with network 01, 02, 02-a and 02-b respectively. The results confirm that network 02-b is the more intelligible among the other networks.

Impact Maps In order to further analyze the impact of the different networks on the urban fabric, the difference in integration (impact) of every axial line of the two models was calculated and impact maps were produced. The impact values are scaled between 0-1 for comparison purposes and the differential represents a change in integration ranking with the

5-44 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3) 85

SPACE SYNTAX

5.6 5-45

BRT network

BRT + network 01

BRT + network 02

BRT + network 02-a

Impact maps of all the different case scenarios analysed.

The integration impact shows the difference of integration regarding the existing situation in Lagos.

BRT + network 02-b

Integration Impact

0 86

1

SPACE SYNTAX

5.6

100

5-46 Chart showing the improvement of the overall integration in the different scenarios tested.

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5-47 Chart where the integration impact percentage in the different scenarios is shown for every street group.

50 BRT BRT+ 01 BRT+ 02 BRT+ 02a BRT+ 02b

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introduction of the public transport networks.10 Fig 5-45 shows how the integration impact increases with the addition of the networks starting with the addition of the BRT network, then the BRT network with networks 01, 02, 02-a and 02-b. Fig 5-46 is a chart which shows the percentage of the urban fabric that was improved. Fig 5-47 is another chart which shows the distribution of this change broken into different ranges of percent change. The final network (network 02-b) has improved the integration of 41% of the urban fabric and in comparison to the other networks shows a better impact. Therefore network 02-b was chosen for further analysis.

of the performance evaluation that was done against the minimum spanning tree, similar results can be observed.

Network 02-b in the performance evaluation gave the best result. In the DepthMap evaluation, network 02-b gave a better correlation in terms of integration and connectivity and resulted in the most intelligible network. It also proved to have the highest impact on the urban fabric improving its integration by 41%.

After the implementation of the rail network, the isolated neighborhoods become more integrated within the urban fabric and become more accessible allowing for the integration to be distributed within these neighborhoods.

By comparing this method of evaluation with the results 10.â&#x20AC;&#x201A; Gil; 2012.

87

LINE DIVISION

5.7

1.- 1.Main 1.Main Main nodes nodes nodes connection connection connection

Connection of main nodes

2.- 2.Branching 2.Branching Branching andand line and line line overlapping overlapping overlapping

I branches 4.I branch I bran 3.- 3.New 3.New New lines lines lines connected connected connected to toto4.- 4.close, close, close, linklink line linl 1.- Main nodes 2.Bran destination destination destination stations stations stations connection overlap

Line overlapping and branching

New lines connected to destination stations

6.- 6.Big 6.Big area Big area area without without without 8.- 8.Temporary 8.Temporary Temporary connections connections connections9.- 9.Lines 9.Lines Lines in pa in 7.- 7.Stations 7.Stations Stations withwith connectivwith connectivconnectiv4.- I branches 4.- I branches get get 5.- Overlap 5.- Overlap lines lines 3.- New 3.-lines Newconnected lines connected to to 6.- Big area without 7.Stati stations: stations: stations: create create create a line a line a line ity to ityity train toto train train stations stations stations have have have thatthat stop that stop stop in dif in close, close, link lines link lines destination destination stations stations stations: create a line stations ity to tra stations stations more more more lines lines lines connected connected connected branches 4.I branches I branches get getget 5.- Overlap 5.- 5.Overlap Overlap linesli 1.- Main 1.- 1.Main nodes Main nodes nodes 2.- Branching 2.- 2.Branching Branching and and lineand lineline 3.- New 3.- 3.New lines New lines connected lines connected connected to to 4.to I 4.more lin close, close, link close, lines linklink lines lines connection connection connection overlapping overlapping overlapping destination destination destination stations stations stations

If branches go close, overlap lines

Line-ends overlap

Create lines through areas with no connections

8.- Temporary 8.- Temporary connections connections 9.- Lines 9.- in Lines parallel in parallel ctiv10.- Cross 10.- Cross inner circle inner as circle as e that stop thatinstop different in different possible possible 6.- Big 6.- 6.area BigBig area without area without without 8.- Temporary 8.- 8.Temporary Temporary connections connections connections 9.- Lines 9.- 9.Lines inLines parallel in parallel in parallel 7.Stations 7.-stations 7.Stations Stations withwith connectivwith connectivconnectivstations stations: stations: stations: create create create a line a line a line ity toitytrain ity to train to stations train stations stations havehave have that that stop that stop in stop different in different in different stations stations stations moremore lines more lines connected lines connected connected

Add lines to station with train connectivity

Temporary connections

10.- 10.Cross 10.Cross inner Cross inn c possible possible possible

Lines that run in parallel stop in different stations

5-48 Line interconnection mechanisms found as a result of the analysis of London tube's network evolution

Network Division A basic network morphology must be broken down into individual lines in order to develop it fully into an urban transportation network.

Case Study References First, the case studies offer insight into how the shape and layout of the different lines affect the connectivity between them. As observed in the evolution of the London network, or any other underground network, lines are always subject to the cityâ&#x20AC;&#x2122;s unpredictable evolution with no fixed rules determining its growth. Nevertheless different mechanisms can be observed guiding the growth of the network.

In London, tube lines were first built to link stations and to diminish the road traffic of travelers inside the city. It is observed that in the first section that was built, many lines

88

One line every direction. The movement between terminal stations inside the network is analysed. 2 is the maximum to achieve. i

TEST 1

LINE DIVISION

d

5.7

TEST 2 a

a

g e h One line every direction.bThe movement between terminal stations inside the network is analysed. 2 b is the maximum to achieve.

h

f TEST 1 a

TEST 2 line every direction.

One a j a b c d e f g h i j a b b 2 c 1 2 d 3i 2 3 e 3 2 3 3 f 2j 3 2 2 2 g 3 2 2 2 1 2 h 3 2 3 3 1 2 1 i 2 1 2 1 2 2 2 2 g i h j 2 1 2 2 2 3 1 2 1d

c

j b

i

d c

j

terminal stations inside the network isganalysed. 2 is the maximum to achieve. i e h d TEST 2

c

h

2 3 3 2 2

5-49 Test 1 using simple e lines

f e

a b c d e bf g h i j

f g h i j

c

Modification 1: Increasing the lines in not-so-well connected nodes.

g

a b 2 2 direction. c 1every One line d 3 2 3 j e a3 b2 c 3 d3 e f g af 2 3 2 2 2 bg 23 2 2 2 1 2 ch 13 2 2 3 3 1 2 di i 32 2 1 3 2 1 2 2 e 3 2 3 3 j 2 1 2 2 2 3 3 2 2 1 1

2 2 3 2 2

2 2 3 1 2

2 1 1 2 g 2

Modif

d

f

a One line every direction. h

c

f

c h i j

1 2 2 1 2 1

d

2 2 1 2 2 2 3 1 2 1

g

h

e

a b c d e f g h i j

e

a f b 1 2 lines in not-so-well connected nodes. c 1 the Modification 1: Increasing d 2 2 2 a be c2 d2 e 2 f 2g h i j a f 2 2 1 2 2 b 1 g 2 2 2 1 1 2 c 1 2h 2 2 2 2 1 2 1 d 2 2i 2 1 1 2 1 2 2 2 2 e 2 2 2 2 j 1 1 2 2 2 2 1 2 1 f g h i j

2 2 2 1 1

2 2 2 1 1

1 2 2 2 2

2 1 2 1 2

2 1 1 2 2

2 2 1 2 2 2 2 1 2 1

5-50 Test 2. Line overlapping increases connectivity in the network

f Modification 1: Increasing the lines in not-so-well nodes. in a similar fashion as a circle loop. The criteria chosen to overlap due to the increase of travelersâ&#x20AC;&#x2122; demand and busi- connected evaluate the line layout are based on the number of lines ness opportunities. This initial line layout was extended b c new d lines. e f Lines g h eventually i j used to go from every terminus station to every other. A and modified rather than aadding a limit of two is imposed as observed in mature underground branch out and connect more areas of the city. Interestingly networks such as London and Tokyo. enough, the branches bfirst1 spread out from the city rather 1 surrounding 2 than inside the city. Acloop the city centre was 2 circle was built, new lines Modifying The Lines d 2the2 inner very soon finished. After e 2 increasing 2 2 2 interchangeability in intersected the inner circle The first analysis reveals a required number of trips above 2 2 1Remarkably, 2 2 the centre part of thefnetwork. the line overthe maximum (Fig. 5-49). Thus, the network connectivity 2 2from 2 the 1 1city2 centre. When lapping is also presentgaway must increase in order to improve. The way of doing this 2 grow 2 2 and 2 1get2closer 1 they share branches of different hlines consists of extending branches of existing lines and overthe same pathway. (Fig. i 5-48) 1 1 2 1 2 2 2 2 lapping them where connectivity is weak. These extensions j 1 1 2 2 2 2 1 2 1 are placed in the destination lines which serve areas whose Division of Lines expected future density is increasing considerably. The second iteration shows successful results and this line layout is Based on the London case study, we used similar strategies selected for further development (Fig. 5-50). to divide the network into individual lines (Fig. 5-49). Instead of a single circle line as seen in London, our network presents three different independent cycles. This functions 89

STATION PLACEMENT

5.8

1 1

2 2

5-51 (Left) Equal Distribution and Density Method Station spacing based from district density. Assumes station count necessary from density in population.

5-52 (Right)

District

100%

23%

Equal Distribution and Density Method

80 %

The estimated total population in 2020 is at 25 million.

The a percentage of this population to use transit is assumed to be 80 percent.

Of this 80%, 23% is assumed to use the rail network.

The total population that could be moved of 2.8 million for the whole system

+

30 %

Station Placement

Equal Distribution and Density Methods

Our aim was to define methods of spatial organization based on networks. By allocating stations we wanted to develop a relationship between the network and an achievable passenger rate of the population in each district. With these relationships, we could then focus on how to address the integration of the system into the urban fabric in terms of time relative to how people might use the network.

The method applied designs for a network that is based off neighbourhood density and train capacity. This would allow for a reasonable flow at stations while still dispersing different concentrations of stations throughout Lagos.

The study of station placement methods provided design methods that held to set relationships within a network. In the method used, the assumptions are from existing charts that relate total train length, line capacity and speed. The guidelines used were from the Rail Transit Capacity Guidelines, Part 3, a basic spread sheet was provided that allowed for easy calculation. The total track length is not considered as it is not a factor in the relationship of capacity at this stage.

Track length was only considered in the distance between a single track lengths between stations. Our method provides for a design of a fully mature system that is expected to be reached 10-15 years after completion of construction.

Achievable Capacity The inputs for this method used the maximum train length, which we calculated from the rail capacity manual as 200 metres. We used the default loading level of eight square metres per passenger. This estimation relates to the levels which measure the pedestrian spatial requirements in relationship between the density of groups of people and the speed with which they can move or circulate.1 This is the typical assumption used when designing for a rail network.

Passenger Capacity per line From this we set the operating margin, dwell time and sig1.â&#x20AC;&#x201A; Network Rail; 2011; p. 12.

90

100%

STATION PLACEMENT

5.8

Ifako-Ijayi 32,468

+1

Agege 28,464

Ikorodu 34,994

+8

Kosofe 57,206

Ikeja 23,766

+1

+10

+4 Alimosho 133,377

Shomolu 28,358

+8

Oshodi Mushin 38,866 Isolo 39,076 +4

+4

+1

Ojo 45,383

+0

Surulere 34,013 Amuwo Odofin

+1

Ajeromi

42,625 +3

nalling headway at the minimum defaults to get the maximum capacity of the network. These inputs provided a total of 59,800 passengers per peak hour direction and a total of 46.8 trains per hour. The total headway between trains at peak hours was 77 seconds. This relationship provided us with the total people and trains per line which we then used to determine the station spacing.

System Capacity We then applied the total passengers to each of our six lines to get a total population that could be moved of 2.8 million for the whole system. This was calculated by taking the projected population for 2025 and then finding the 80% that will be taking public transport (Fig 5-54). From this 80% we then apply the number the system can handle and get the percentage of people the rail system could accommodate at full maturity. We found this number to be 23% of the projected population (Fig 5-55).

+2

Lagos Island 15,892 +2 Apapa

24,143

+5

Lagos Mainland 27,678

16,494

+2

Eti-Osa

38,550 +3

Station Placement

5-53

Using the single line capacity relationships, we assumed a maximum distance between the stations as 2.4 kilometres.

With this we were then able to assume the speeds and time between trains at the peak hour. Then to relate the stations to the density of the areas in which they were located, we categorized the stations based on the populations of the neighbourhoods (Fig 5-53). For example, we took the red line and measured the segments of each portion that fell into each of the districts. We then counted the number of stations that were in each area. Using the same ratio as before to assume the projected population that would be using the rail, we divided this amount into each station. This was then reduced down to the rush hour count which we assumed to be eight hours a day from 6am-10am and 4pm â&#x20AC;&#x201C; 8pm. These numbers were necessary to calculate in order to begin to design the proportions of the pedestrian pathways to accommodate daily flows.

Proposed network shown over Lagos district map indicating populations and additional stations along new rail lines.

91

STATION PLACEMENT

5.8

5-54 Expected daily ridership of each designed line in a day 329,362 users / day 645,722 users / day 302,560 users / day 635,426 users / day 222,277 users / day 571,811 users / day

Conclusion of Method

Station Placement Summary Conclusion

We found this equal distribution and density method to provide results for station platforms and flows that fell within the guidelines of standard practice. Our aim was to reach an achievable capacity to use for pathway design at the nodes that would compensate for speed and frequency, which then could be understood at a detailed level in relation to the district densities.

The methods applied assumed relationships currently used by engineers in the infrastructure design of stations. By applying these methods to develop a network, we found that our yellow line with a capacity of 645,722 passengers daily would have the same flow as the northern line in London with 567,950 passengers daily (Fig 5-54-55). Within the network, we also found that our busiest station with 79,894 passengers daily had similar flows as Waterloo station in London with 57,000 passengers daily (Fig 5-56). These results validated that our methods predicted for a rate of passengers that is attainable in similar megacity networks.

The areas for further refinement are the levels of spatial congestion. Applying the default criteria as an assumption works based on Western standards of congestion. The site condition of passengers in Lagos was not considered as it is unknown. This relationship could be explored with further information regarding the type of users, which transportation mode they are connecting to, and the acceptable cultural proximities of passengers. Based off of site images, we can assume that the criteria used is generous and less area per person would be assumed as the culture appears to accept a much smaller personal space.

92

We found the application of these techniques a success as they could feasibly accommodate for flow in a successful way in order to design for a network. The achievable capacity method in conjunction with the district densities provided the desired output, which was an estimation of the projected population within the capacity of current transportation means. We see the potential of this method to explore the conditions at each node within the system and its affected urban tissue based on passenger flows.

STATION PLACEMENT

5.8

London Underground Northern Line

Lagos Network Yellow Line

16 14

1 Current Population (millions)

12 10 8 6 4 2 0 Lagos

London

Mexico City

New York

Paris

Seoul

Tokyo

80 70

2 % Population Using Public Transit

60 50

+ 58 km line length + 50 stations + 567,950 daily passengers

40 30 20 10

+ 28 km line length + 27 stations + 645,722 daily passengers

0 Lagos

London

Mexico City

New York

Paris

Seoul

3 Busiest Station from daily passengers

3,640,000

3,000,000

5-56 Busiest stations from daily passengers

2,500,000 189,4256

2,000,000 1,500,000

1,800,000

1,130,567

1,000,000 500,000

Comparison of the Northern Line of London with the proposed yellow line for Lagos

Tokyo

4,000,000 3,500,000

5-55

79,894

57,000

Busiest Lagos

Waterloo London

125,810 Cuatro Caminos Mexico City

42 Street Grand Central New York

Gare Du Nord Paris

Gangnam Seoul

Shinjuku Tokyo

93

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5.8 Integration

0

1

Network 02-b layout plus all stations

94

STATION PLACEMENT

5.8

5-57

R2 = 0.0853

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b plus all stations.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3926

Integration [Hh]

The network was again analysed for integration including the 79 stations (Fig. 5-58). Intelligibility measures can be obtained from the graphs in Fig 5-57. It can be observed that the correlation between local and global integration of Lagos urban fabric coupled with the network and its 79 stations is 0.3926. From Fig 5-57, the correlation between the degree of connectivity and global integration is 0.0853.

These values confirm that the intelligibility of the network increases with the placement of the 79 stations.

5-58 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b plus all the stations.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3) 95

STATION PLACEMENT

5.8 5-59

Impact map for BRT + network 02-b + all stations

Integration Impact

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5-60 Chart showing the improvement of the overall integration in the different scenarios tested.

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With the addition of the 79 stations, the network was again analysed for its impact (Fig. 5-59). The impact is very similar with the addition of the stations, improving the integration of 41% of the urban fabric (Fig. 5-60). However, the distribution of this change increases compared to the network with just the activity points as seen in Fig. 5-61. In other words, in the case of the 79 stations, the integration of a small percentage of the urban fabric improves by 400%, whereas in the case of only using the activity points as links, the highest improvement was by 350%.

97

EVALUATION

5.9

98

EVALUATION

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BEFORE

81%

13%

5%

1%

Buses and mini-buses (Danfos)

Taxi, private cars

Motorcycles (okada)

Railway

13%

5%

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51% 51%

Network Route

31% 31%

BRT Bus Stop Railway Station Ifako-Ijayi

Activity Zones Buses and mini-buses (Danfos)

Taxi, private cars

Motorcycles (okada)

Agege

Railway

Kosofe Ikeja

# of people on roads before Mushin implementation

Lagos Mainland

Ajeromi-Ifelodun

Urban Population

Shomolu

Surulere

% reduction in road usage

Ajegunle Apapa

27%

Ikoyi Lagos Island

# of people on roads after implementation

Redistribution of Transport Usage In evaluation of the effect of our system we found that 23% of public transport users will use rail. The use of buses and mini–buses (Danfos) currently in Lagos takes 81% of public transport usage. A large portion of the BRT users take Danfo for some parts of their journey as a means of access to the BRT network.1 However after the implementation of the our system, the rail network will be well integrated within the urban fabric and can be used as access points to the BRT network and as a result will decrease the use of mini buses to 50% (Fig. 5-62).

Reduction in Congestion

Victoria Island

BRT network (Fig. 5-64). This shows that the rail network will improve the current congestion in Lagos. Another measure that can be used to identify the improvements of the system in terms of congestion is to see what percentage of road usage will be reduced by the rail network. Since currently all public transportation is on the roads the difference between the number of people on the roads before and after the implementation of the network will give that percentage. According to our design outputs and flow analysis the proposed rail network will reduce road usage by 27 % (Fig. 6-63).

5-62 (Top) Redistribution of usage of different means of transportation before and after the implementation of the network.

5-63 (Bottom) Reduction of Road Usage

Public Transport Comparison

Another method used to evaluate our system was to compare journey times for a typical commute in Lagos. The results indicated that the proposed rail network can decrease journey time by 75% for a personal vehicle and 40% for the

A typical measure for ranking public transportation networks in different cities is the ratio of the lengths of the network to the urban area. The average of this ratio in Africa is 0.07 with Tunis having the highest rank of 0.27.2 Compared to other African cities Lagos is currently below

1. Lamata; 2009.

2. Siemens; p. 31.

99

EVALUATION

5.9

Mile 12 Market

i

Lagos mainland

Lagos Island

5-64 Travel time comparison Personal Vehicle Travel Time : 2 Hours BRT Travel Time : 40-70 min Proposed Network Travel Time : 25-32 min

100

EVALUATION

5.9

Tunis Casablanca

Cairo

Lagos Nairobi

Length of mass transport network in km per km2 of city area [km/km2] 0.35 0.3 Post Implementation

0.25 0.2 0.15 0.1

Average of African cities

0.05 BRT

Cairo

Casablanca

Nairobi

Tunis

Lagos

average having a rank of 0.03 but after the implementation of our network it will be one of the highest ranked in Africa with a rank of 0.29 (Fig. 5-65).

Conclusion Despite these positive results there are remaining aspects with areas for improvement. First, the system reduces the use of informal modes of transportation such as danfos, but even with this reduction, 50 % of the population is estimated to continue using them as per transportation reports. Further reduction in this number would continue to improve the transportation in Lagos as it would alleviate the overload on the existing infrastructure. Second, the journey times that showed the highest reduction in travel were mainly along major commute routes. Further evaluation to ensure that all routes are reduced would increase the effectiveness of the network. Finally, the road usage was reduced by 27%, but this will most likely only be along heavy commute routes. We see the area for improvement in continuing to reduce usage of the roads across the entire city in order to improve traffic conditions.

5-65 Comparison of mass transit across Africa Existing Post Implementation

101

102

6.0

REGIONAL IMPLICATIONS 6.1 Station categories 6.2 Selected nodes 6.3 Intermodal station case studies 6.4 Pedestrian node generation 6.5 Regional network generation 6.6 Population density distribution 6.7 Summary

103

STATION CATEGORIES

6.1

Passenger Operation [km]

Number of stations

Daily ridership / day

Pink Line

28

18

329,362

Yellow Line

38

14

302,560

Green Line

24

23

645,722

Blue Line

35

20

635,426

Cyan Line

15

10

222,277

Red Line

34

21

571,811

79 Stations MIN avg passenger / day = 8,200 MAX avg passenger / day = 80,000

Average passenger / day

Number of lines

Category A: National hubs

62,001 - 80,000

3

Category B: Regional hubs

21,801 - 80,000

2

Category C: Small

6-1

Station Categories

Parameters for defining station categories

Our previous analysis concluded in a network consisting of six lines and 79 stations. The stations were divided into three categories. The average number of passengers per day for each station was calculated according to each stationâ&#x20AC;&#x2122;s associated neighborhood density. From this information the minimum and maximum average passengers per day for the 79 stations were extracted and ranges of passenger flow were assigned for each of the three categories. Fig. 6-1 shows how the categories are divided according to a daily passenger range and the number of lines that go through each station. 1 Implementation Having defined the three categories, the stations were classified accordingly. Fig. 6-2 shows the distribution of station categories across the network (Appendix section 9.2 table 9-7).

1.â&#x20AC;&#x201A; Network Rail; 2011.

104

8,200 - 21,800

1 or 2

STATION CATEGORIES

6.1

6-2 Distribution of station categories across the network 105

SELECTED NODES

6.2

A

6-3

C

B

3 selected test case stations

Category Test Cases In order to better understand the effects of this network on the urban fabric, three nodes were chosen to develop at the regional and local scales architecturally (Fig. 6-3).

These nodes were chosen from three different density areas around the city, each with different urban characteristics and different estimated flows. Station [y-03, C-07, B-20] (Appendix 9.2, Chart 9-6) has three lines passing through it and has a high flow of daily passengers. The Nigerian Railway Corporation (NRC) also stops at this location and the Oshodi market is 3 km north from the station. Station [B-03, P-15] has two lines passing through it and is located on Lagos Island near the central business district with a medium flow rate. Station [G-03, P-03] also has two lines passing through it and has a low flow rate (Fig. 6-4). It is also nearby the Alaba International Market. The Alaba International Market is the largest importer of electronics in Africa and it is estimated to have an annual turnover of two billion dollars.2 2.â&#x20AC;&#x201A; Felix,Wolting (Producers), & van der Haak, Bregtje (Director);2005.

106

SELECTED NODES

6.2

500 m

A Y 03

C 07

B 10

NEW DENSITY 116,095 ppl / sq km FLOW 56,354 ppl / day

POPULATION Area to intervene: 0,78 sq km Area density 2006: 36,213 ppl / sq km Area density 2020: 116,095 ppl / sq km

x 3,20 FLOW Average passengers / day = 56,354

B B 03 P 15

NEW DENSITY 73,819 ppl / sq km FLOW 40,953 ppl / day

POPULATION Area to intervene: 0,78 sq km Density 2006: 24,182 ppl / sq km Density 2020: 73,819 ppl / sq km

x 3,05 FLOW Average passengers / day = 40,953

C G 03 P 03

NEW DENSITY 7,476 ppl / sq km FLOW 20,988 ppl / day

POPULATION Area to intervene: 0,78 sq km Density 2006: 2367 ppl / sq km Density 2020: 7476 ppl / sq km

x 3,15 FLOW Average passengers / day = 20,988

6-4 3 test stations and their urban context 107

INTERMODAL STATION CASE STUDIES

6.3

6-5 Amsterdam Central Station

To understand the effect of transport nodes on their surroundings, we examined two case studies with different characteristics: one is an intermodal station that has increased its importance overtime and deals with millions of daily passengers. The other is a new design for an international train station that assumes its importance by including major program developments to the station and is also expected to deal with millions of users daily.

From these studies we abstracted main concepts of connections to the urban fabric and station design that we could use within our network. An area of particular interest was how these stations integrate into their surrounding urban fabric and how they allow for passengers to transition between the station and the urban environment. Stations function better when there is a buffer between the city and the station rather than throwing disoriented passengers directly into the city.

This is shown in different strategies such as the creation of public â&#x20AC;&#x153;piazzasâ&#x20AC;? common in Italy, or by integrating public green space into the surrounding area.

108

Stations that were successful in their designs had methods of connecting to pedestrian paths. These paths can act as an infrastructural axis within the city, and as a result, this connection can be more accessible to passengers. Coupling our network lines with paths to transfer to other modes of transportation provides a high degree of connectivity and therefore increases the flow of the station. At these connections it is critical to determine a correct distance for transfer, as too much distance can cause confusion and limit navigation.

INTERMODAL STATION CASE STUDIES

6.3

Dutch Renovation Approach We examined how the Dutch treat their stations and how the new designs were dealing with connecting to the urban fabric. For Amsterdam Central Station (Fig. 6-5), the placement of the station was an important strategic move in the development of the city (Fig. 6-6). The objective of this design was to “maintain the centre of the country as the green heart of the Netherlands.”1 In Rotterdam Central Station, the station was renovated to have a public square at the front of the station to connect to the city. The station also developed connections with bridges surrounding the station.

At Utrecht, the master plan proposed buildings designed to accommodate the speed of uses in program as well as pedestrian path connections. The conclusion from these studies was that all of the stations developed connections to the existing paths for biking and walking and take into account the specific needs in the area of intervention.

6-6 Plan of the Amsterdam Central Station. Coloured lines represent different means converging in the transport node.

1. Amsterdam, NL.p 2.

109

INTERMODAL STATION CASE STUDIES

6.3

6-7 Shibuya Station aerial view.

Keio Railway

Railway

6-8 Section of the different buildings that form Shibuya station.

6-9 Plan diagram of Shibuya train station with the immediate surroundings. Coloured lines represent different means converging in the transport node.

Bus stop 110

Ginza metro line Toyoko Railway

Metro

Metro

INTERMODAL STATION CASE STUDIES

6.3

Shibuya station Shibuya station is located in the city of Tokyo, and is the fourth busiest station in Japan. In 2004, it held over 2.4 million passengers per week day (Fig. 6-7). It has terminal stations of 3 metro lines and 5 railways apart from other road services. Shibuya station is named after the area in which it is built: a ward which is a major entertainment and shopping centre in Tokyo. The massive use of the station is not only due to the areaâ&#x20AC;&#x2122;s high population, but also people that commute from the city centre to the southern and western suburbs.

6-10 Crowds outside of Shibuya Station

Regarding the station morphology, it consists of a main building and a western building which are connected above ground level (Fig. 6-9). The transport lines that converge at the station do not meet in one point, but stop on different levels within the station (Fig. 6-8). This fact considerably reduces the area needed for the station compared to other cases. Also, it helps reduce its footprint in an already saturated area such as Shibuya. The footprint of the station does not affect the intense traffic flows in the area consisting of both private and public road transport.

Many developments have been built surrounding the station while Shibuyaâ&#x20AC;&#x2122;s importance increased over time. The high density in the area is architectonically depicted as high-rise buildings that house mainly offices and retail spaces. The potential benefit of the increasing flows of commuters in the area attracted retailers increasing even more the affluence of people creating positive feedback (Fig. 6-9).

111

INTERMODAL STATION CASE STUDIES

6.3

6-11 Rendering of Proposed Design for West Kowloon Terminus

6-12 Section diagram showing the location of platforms in the building.

6-13 Plan diagram of West Kowloon project with the immediate surroundings. Coloured lines represent different means converging in the transport node.

Bus stop

112

Regional Shuttle Metro Lines

Long-Haul High speed Lines

INTERMODAL STATION CASE STUDIES

6.3

West Kowloon Terminus The new West Kowloon Terminus proposal has been praised for its integration into the urban fabric and design of public space (Fig. 6-11). The station has an expected date of completion of 2015. The high speed rail terminus will consist of 15 tracks that connect Hong Kong to major mainland cities in China. The 15 tracks consist of regional shuttle trains and long-haul high speed trains (Fig. 6-12 and Fig. 6-13). It is to become the largest multi-level underground high speed rail station in the world with a footprint of over 10 hectares.2 The layered structure of the station allows for uninterrupted movement of the massive flows and provides a new destination for public use. After completion, the station is to reduce travel times by 50 per cent.3 The site is in close proximity to the future West Kowloon Cultural District and Victoria Harbour. Therefore, it requires a design that will integrate the surrounding urban context to the station. The landscaped paths which make up the roof of the rail station emerge from the surroundings. These paths act as the connective fabric between the station, the adjacent public transportation, the surrounding

urban district, and the waterfront. The landscaped paths together with the central plaza will act as green, open spaces and allow for pedestrian circulation.4 (Fig. 6-14)

6-14 Landscape paths on station roof structure.

The prevision of public spaces is not the only characteristic of the project that aims to attract flows of people from the city. Apart from the typical retail programs included in the station, the design incorporates three high-rise towers that consist of a combination of housing and office spaces. This typology resembles the scale of the surrounding buildings and suits the scale of the open public spaces projected in the area. In this particular case the absence of a historical background in the site allows more freedom in the design.

Nevertheless, we can observe again the exploitation of the expected affluence of people with the introduction of varied programs.

From these case studies we were able to abstract existing methods of implementing stations into cities. We used these methods to understand the important surrounding areas of infrastructure. The programs that connected to these areas were helpful in determining points of influence in our proposal.

2. http://www.aedas.com/Express-Rail-Link-West-Kowloon-Terminus-Hong-Kong 3. SPECTRUMAsia. 2011

4. SPECTRUMAsia. 2011 113

PEDESTRIAN NODE GENERATION

6.4 6-15

Axial map of urban context with 500 m radius around the station

6-16 Depthmap space syntax analysis for integration

6-17 Pedestrian nodes placed on streets in the top 35% of integration levels

114

PEDESTRIAN NODE GENERATION

6.4

Integrating to the Urban Context After laying out the locations of all 79 stations and breaking them into their 3 separate categories, we developed a system for integrating an individual station into its surroundings. A more localised Space Syntax analysis was carried out on a case study area for each category. From this information we placed nodes within a 500 meter radius of the station. These nodes would be placed at the most integrated streets in the area. This is because these streets are used more frequently, so people both arriving at and leaving the stations will most likely be coming from or going to these areas. These nodes will serve as transition points in order to create a spatial diffuser between the station and the urban fabric surrounding it.

Generating Pedestrian Nodes In order to place nodes, we developed an algorithm that would examine Space Syntax data for a given area (Fig. 6-15) and assign nodes to streets based on their integration values (Fig. 6-16). The integration values are first scaled between 0 and 1. Then, nodes are placed at the midpoints of each street as a circle with a radius that is proportional to its integration. An important parameter of the algorithm is the minimum value (minVal) that will be considered as meriting the placement of a node. For example a minVal of 0.5 will mean that any street whose scaled integration value is between 0.5 and 1 will be considered a node. This gives the user control over how many nodes will be placed in the system. A smaller minVal results in a larger number of nodes.

In the end we used a value of 0.65 as the minVal meaning that only the top 35% of the streets were considered nodes (Fig. 6-17). This was because in the case of a minVal of 0.5, many of the nodes were overlapping and were very jumbled, and with a minVal of 0.9, there were many cases

where there were no nodes in the immediate area of the station. The value of 0.75 resulted in all stations having at least one node, while not creating large amounts of overlapping nodes.

Generating Pedestrian Networks After defining these nodes, they were fed back into the network generation algorithm. On this more localized scale, the aim was to create a network of pedestrian paths connecting the station with its surroundings. This network will be used primarily as pedestrian routes from major streets in the area to the station. It will serve to both better embed the station into its surrounding context and to increase the integration values of the surrounding areas.

Implementation The following pages show this process for every station within the network. Each particular pedestrian network is evaluated for its P value in the same manner as the global network (length of the minimum span tree/total network length): P = lenminspan/lennetwork

115

PEDESTRIAN NODE GENERATION

6.4

R-07, G-13

R-12

P = 0.847663547891

R-08

R-13

R-09

C-04, Y-05

P = 0.640448454447

R-14

P = 0.681712850961

R-10

R-11

P = 0.725040789842

P = 0.720590204284

P = 0.511840566716

B-01

P = 0.588418383774

P = 0.840823243279

C-07, y-03, B-10

P = 0.699579157324

P = 0.964214738936

C-10, y-06

C-06, y-02

C-02

P = 0.703278751894

C-09, y-05

C-05, y-01

C-01, G-08

P = 0.681699600448

P = 0.658047466492

P = 0.96213820137

P = 0.722754479563

C-08, y-04

P = 0.768423948316

P = 0.747089571539

P = 0.771529006222

116

C-03

P = 0.657384970259

B-02

P = 0.50401282519

PEDESTRIAN NODE GENERATION

6.4

Observations Looking at the evaluations of each station and its newly generated pedestrian network, differences in the values for each station can be seen (Fig. 6-18). In terms of the performance value of each network (P), the majority of the values fall somewhere between 0.6 and 0.75. There are values in the extremes of the range of 0-1, but only a small amount.

This is because the algorithm that generates the networks has been designed to produce networks with a high performance. Therefore, the majority of the solutions will have a relatively similar P value.

6-18 (Opposite Page) Pedestrian network generation for individual stations. Partially shown, remainder in Appendix section 8.3 table 8-11 P = Performance Factor 500 m radius 117

PEDESTRIAN NODE GENERATION

6.4 Integration

0

1

Network 02-b layout

118

PEDESTRIAN NODE GENERATION

6.4

6-19

R2 = 0.1516

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network, the network 02-b, the connection to all stations and the local pedestrian pattern in every station.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Integration [Hh] R3

R2 = 0.4822

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

6-20 (Opposite page)

The network was evaluated once more for its integration, this time with the pedestrian networks (Fig.6-20). Intelligibility measures can be obtained from the graphs in Fig. 6-19. From Fig. 6-19 (Top) it can be observed that the correlation between local and global integration of Lagos urban fabric integrated with the network and the pedestrian networks is 0.4822. From Fig. 6-19 (Bottom) the correlation between the degree of connectivity and global integration is 0.1516. These values confirm that the intelligibility of the network increases with the addition of the pedestrian networks.

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b, the connection to all stations and the local pedestrian pattern in every station.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

119 119

PEDESTRIAN NODE GENERATION

6.4 6-21

Integration impact map of all the local modifications of the stations compared with the preexisting situation.

Integration Impact

0 120

1

PEDESTRIAN NODE GENERATION

6.4

100

6-22 Chart showing the improvement of the overall integration in the different scenarios tested.

50

+

+

La

pe de ria st

02

n

a

b

02

02

01

ns

+

+

+

+

io at st

s

s

s

s

s

go

go

go

go

go

La

La

La

La

100

6-23 Chart where the integration impact percentage in the different scenarios is shown for every street group.

BRT

50

BRT+ 01 BRT+ 02 BRT+ 02a BRT+ 02b BRT+ 02b + All stations

0

0

45

0-

40

0

35

0-

30

0

30

0-

25

0

25

0-

20

0

20

0-

15

15

0-

10

00

-1

50

50

0-

BRT+ 02b + Stations + Pedestrian nodes

The newly generated pedestrian networks were inserted into the model and the network was again analysed for its impact, which showed a marked improvement (Fig. 6-21).

This network improves the integration of 56% of the urban fabric (Fig. 6-22). Also, the distribution of this change increases, which means that the integration of a small percentage of the urban fabric improves by 450% (Fig. 6-23).

Currently, our system is only affecting 44% of the urban fabric. If this number could be increased, the integration of the urban fabric could be further improved.

121

REGIONAL NETWORK GENERATION

6.5 B

B 03 P 15

NEW DENSITY 73.819 ppl / sq km FLOW 40.953 ppl / day Integration 1

0

6-24 (Left) The area to intervene covers a circle whose radius is 500m.

The category A node represents the intersection between the blue and pink lines.

In order to integrate our proposed network into the urban fabric, regional arrangements must be modified to begin to accommodate new flows and populations, as is seen in node [B03, P15]. This node is the connection point of the Blue and Pink lines on Lagos Island. We addressed the design of this node with the same rule set as used in our global network design. Therefore, we have used a fractal approach of network design for our local development.

6-25 (Right)

Integration of the axial map

Step 1 The street pattern within the range of intervention is analysed with space syntax.

In the first step we ran the network generation algorithm on the nodes developed based on the space syntax analysis (Fig. 6-24). While running the algorithm on these nodes, the nodes were again ranked based on how many cells passed through each node. This ranking suggested which paths emanating from the station would have the highest pedestrian traffic.

Integration 0

1

Existing Urban Territory In the next step, the pathway intersections into the existing urban fabric were considered. The lines were articulated at 122

three different levels to adjust to the existing street patterns (Fig. 6-25). The highest level of articulation indicated that the streets had a high amount of adjustment to the existing street pattern still connecting the points in the simulation.

The lowest level of articulation indicated that the level of articulation was the closest to the simulated lines and not adjusted to the existing fabric. The pathways cut-through the blocks more frequently as a result of the low level of articulation. The articulation level was determined through two means of evaluation. The first method evaluated for the P values of the newly articulated lines. The second method evaluated the total square area of the existing fabric affected by the pathways intersections in the fabric. After applying the articulation on three nodes, the level shown to correspond best was the middle level of articulation. The simulated lines were adjusted at the medium articulation levels into the surrounding fabric.

As some of the newly adjusted lines intersected the existing blocks, it was necessary to develop a method to address this intersection. This method used the flow of the total amount of people estimated to use the pathways as a means of

REGIONAL NETWORK GENERATION

6.5

Line 02 20% Line 01 6%

Line 03 9%

Line 04 25% Line 05 9% Line 06 8%

Line 09 11%

Line 07 6% Line 08 6%

generating the width of the path (Fig. 6-25). This flow was represented as a percentage determined by the simulation counts from the nodes. This percentage represented a ratio between the total amount of population from the global network to use the node and then the regional node counts of that population into the urban fabric (Fig. 6-26). The existing fabric block indicated by the streets was preserved to allow existing pattern to remain. The only area of complete removal of block was at the station. In this location, all the blocks intersecting the station node were removed.

The extents of these removed blocks created the boundary for the urban plaza surrounding the station.

Path Development To determine the width of the pedestrian paths a similar method to designing for railway station platforms was applied. This method accounts for the maximum flow of people moving through the highest congested section of the platform during peak rush hours. The pedestrian paths were assigned widths based on their amount of expected population flows. We assumed a minimum path width for

each line with a flow that didnâ&#x20AC;&#x2122;t require at least three meters distance. As the lines originated from one point there was overlap that occurred along some lines. To address this, additional width was provided by an offset from the initial three metres for each overlap which would allow multiple flows to have more space to move through (Fig. 6-26).

6-26 (Left) Network lines with associated flow percentages

Pedestrian Network Conclusions Based on our calculations, the category B node could accommodate a daily flow of 40,953 people. In the development of the lines, the simulation counts provided different densities per line. These densities were represented as pedestrian flow rates per line. We found these results acceptable as they accommodated for the flow successfully throughout the fabric.

6-27 (Right) Network lines with associated flow percentages

123

POPULATION DENSITY DISTRIBUTION

6.6

Rail Line

6-28 Density concentrating near node and along network lines

B B 03 P 15

NEW DENSITY 73.819 ppl / sq km FLOW 40.953 ppl / day

6-29 Population distribution at node 124

Min height

Station Node

Max height

Rail Line

POPULATION DENSITY DISTRIBUTION

6.6

Density Distribution An inevitable result of introducing a major transportation system to a city is that the distribution of population densities will have a marked change. Transportation nodes reshape their surroundings in two major ways: by increasing the flow of people moving through the area on a temporary basis, and by increasing the population density of the area.

Since the area becomes better connected to the urban fabric, more and more people will tend to flock to the area.

Based on the case study of the Tokyo metropolitan area, we can estimate how this population distribution will happen.

The population growth is most concentrated around the major nodes of the network, with growth also spreading out along the lines of the network (Fig. 6-28).

for the pedestrian network. This time, the populations are distributed to all volumes closest to the pedestrian lines at any point along the line. This is because, unlike the rail lines, these lines can be accessed at any point along their length, not just at a single point. The result is an urban tissue with density gradients derived from both the existing urban situation and the proposed modification of the tissue. These volumes will be used in the morphological development of each block in order to determine the amount of housing space that will need to be provided at each block in order to accommodate for the increased population that is expected (Fig. 6-29).

This distribution is carried out in a three-step process. First, a script distributes the expected population throughout the node based on the existing street pattern. Population is represented by a 10m x 10m footprint with a varying height.

The initial volume heights are calculated based on their proximity to existing attractor points nearby to the node.

These can be markets, transport centres, or central business areas. This creates a base urban tissue, which is then modified to accommodate for both the rail network, and the newly generated pedestrian paths. An algorithm removes any volumes that are within a given distance to the rail line.

The populations associated with these volumes are then redistributed to the volumes that are closest to the station node. The station is given priority at this stage, because it serves as the only pedestrian access point to the rail network. This process is repeated in order to adjust the fabric 125

SUMMARY

6.7 B

B 03 P 15

NEW DENSITY 73.819 ppl / sq km FLOW 40.953 ppl / day

Line 2 21% total flow

Line 1 6% total flow

Line 3 9% total flow Line 5 9% total flow

Line 9 11% total flow Line 6 8% total flow

Line 8 6% total flow

126

Line 7 6% total flow

Line 4 25% total flow

SUMMARY

6.7

Summary of Regional Implications By articulating the flows of the global network at the individual nodes, it is possible to transition from global network to the regional scale. This transfer of information creates a bridge between the two scales and allows for the regional variation across the city to affect the distribution of these flows and how they begin to suggest architectural morphologies (Fig. 6-30). These nodes developed into categories of flow that showed characteristics of each neighborhoodâ&#x20AC;&#x2122;s population density and the proposed flow in public transportation that would be resultant of these densities. By linking these flows and population densities to morphological sections, it is possible to address specific conditions in the urban fabric and the localized flows that emerge. In summation, the refinement of the flows from global to regional scale allows for the next development to focus on the urban fabric at a detailed level, and how these flows can begin to suggest local architectural morphologies.

6-30 (Opposite page) Population distribution at node with network flow percentages 127

128

7.0

URBAN MORPHOLOGIES 7.1 Market characteristics 7.2 Morphology and flow 7.3 Morphological refinement 7.4 Evaluation

129

MARKET CHARACTERISTICS

7.1

Mile 12 Market Mushin Market

Balogun Market 7-1 Selected case-study markets

Morphology Development In order to begin developing nodes architecturally, an analysis of the existing conditions was necessary. This study focused on the programs and morphologies that were dominant to the site cityâ&#x20AC;&#x2122;s culture, in order to be able to suggest potential applications. We used the flow in population from our regional network nodes and the population density of the district area, as the two driving factors in developing morphologies. The methods we used to complete this development applied the existing cultural programs of the site to inform sectional qualities.

Programmatic Development The public market culture in Lagos is one of high densities spread within the existing urban fabric in the form of rentable shop areas and informal stands. From the highly dense areas, there are uncountable markets that spring up as well as some that are constant and well known, such as the Alaba Electronics Market. In this market, the daily amount of visitors can be estimated to be around 300,000 people. The market spreads over a two kilometre by one kilometre area and has around 3,000 shops that are registered as vendors.

To proceed with the existing typologies of these markets, we looked into three existing conditions of market throughout varying locations of Lagos (Fig. 7-1). These areas had neighbourhood densities that provided variation of urban fabrics. The information available for even the more popular markets, such as Alaba, did not have consistent street views and aerial images developed from which we could observe the market conditions. Our methods of observation combined street views, aerial images, and first-hand accounts of the conditions of the markets.

130

MARKET CHARACTERISTICS

7.1

Balogun Market condition Balogun Market is located on Lagos Island which has an existing density of 24,182 people per square kilometre. The market exists on the Island Axis, along Marina and Broad Street, which are the two most widely used avenues.1 Even though these streets appear to have a two-car lane width, the market spills out onto the street causing blockage to the traffic (Fig. 7-2). The side streets continue with narrower sections of market and from this extension, the height at which vendors’ shops extends increases. In the street views,

1. Gray, Kimberly. “Some Common Markets In Lagos, Nigeria”.

Online Article Accessed 22.01.13. http://www.articleclick.com/ Article/Some-Common-Markets-in-Lagos-Nigeria/1575414

it is possible to see the building heights to which people are selling goods (Fig. 7-3). We observed that a height of around two to three levels continues throughout the market in this area. From further research we were able to abstract that the condition of increased height occurs at higher levels in the Idumota Market, also located on Lagos Island.

This market is the oldest and largest in West Africa.1 These markets’ growth have occurred partially due to the existing building heights, but also, the shift of program from this growth from the private apartments above the markets into retail space for vendors. Due to these market conditions, it appears that areas of increased density with restricted boundary conditions, such as that of Lagos Island, allow for the market space to increase in height if the existing buildings can accommodate for this growth.

7-2 (Top) Aerial view Balogun Market

7-3 (Bottom) Street view Balogun Market

131

MARKET CHARACTERISTICS

7.1

7-4 Aerial view Mushin market

7-5 Street view Mushin market

Mushin Market condition Mushin market is located in the district of Mushin which has a density of 36,213 people per square kilometre, an area with higher density than Lagos Island. The market characteristics are that of larger street widths and single to three level buildings with visible vendor store fronts on the facades (Fig. 7-4). From the street perspectives, the condition of a raised street level on the faรงade with a protected overhang at the top most level could be seen (Fig. 7-5). From this market and the others, we abstracted that it appears a necessity to provide shading devices. This can take the form of a simple solution as an umbrella, or a more permanent addition to the building faรงade to add to the circulation of the markets.

132

MARKET CHARACTERISTICS

7.1

7-6 Aerial view Mile 12 market

7-7 Street view Mile 12 market

Mile 12 Market Mile 12 Market is located in the Kosofe district that has an existing density of 8,174 people per square kilometre.

The existing building heights of the area do not appear to go above a single level and the side streets are barely viewable in the aerial image (Fig. 7-6). The market takes over an empty parking area that continues into the side streets of the surrounding buildings. The conditions observed were that of the edge condition between streets and building that is built up with vendors with a large free area occupied as informal market. The density of people was held to the edge condition and then pushed into the narrow streets of the fabric. What was also observable from the aerial view was that there appeared to be a system of buildings that may have been established first with the markets infilling in areas in between (Fig. 7-8). We concluded that this condition is a response to the need for more market space when there isnâ&#x20AC;&#x2122;t any height to expand into.

7-8 Diagram showing buildings to street relationship for Mile 12 market.

Existing buildings Overfilled market Open space market

Open space edge and main pathways Secondary paths 133

MARKET CHARACTERISTICS

7.1

13 m

8m

12 m

9m

10 m

14 m

6m

11 m

18 m

14 m Roadway

Balogun Market

Mushin Market

7m

Highway

40 m

Mile 12 Market

HEIGHT INCREASES - MARKET DENSITY INCREASES

Height Increases - Market Density Increases 7-9 Variation in market heights Outside retail space Inside retail space 134

7m

Highway

MARKET CHARACTERISTICS

7.1

7-10

Maximum = 5 levels Maximum Height =height 5 Levels

Maximum market height

Characteristics of Markets From the market studies we abstracted three traits that the local typologies and existing conditions of growth suggested. Markets in areas of high density will develop in height if the existing buildings can accommodate this growth, even if the programs of these buildings are not considered for retail space. With this increase in height, paths and overhangs are two characteristics that seem prominent in the building systems. In areas without the ability to continue in raised levels of buildings, the markets become more densely packed with vendors (Fig. 7-9).

In terms of development for future growth, the vertical condition of growth is where application of the markets could address the rapid changes in an effective manner and remove the conditions of congestion. With this assumption, we applied a constraint of five levels to our sectional development, as this was found from our research to exist as a building height with market (Fig, 7-10). We also assumed the articulations of path and sun shading devices to be coupled to these sections.

Low density = 1 or 2 levels Low Density 1 - 2 Levels

Medium density = 2 or 3 levels Medium Density 2 - 3 Level

Development of Morphologies in Section We decided to develop the project through the use of section as it had been the useful method in describing the existing conditions. The representation of height, depth and articulation of pathway were used to specifically inform the sections. These inputs allowed for outcomes that related the conditions density through physical attributes (Fig. 7-11).

Medium/High density = 3 or 4 levels Medium - High Density 3 - 4 Levels

Allocation of Sections to Nodes The representation of density was articulated through levels of height in section. To develop unique conditions at the nodes, ranges of sectional heights were indicated at each node. For each node, the section would have a total range difference of two levels, for example at the highest density node we had a variation of from five to three levels in height that could exist at this node (Fig. 7-12).

High density = 4 or 5 levels High Density 4 - 5 Levels

7-11 Categorization of market heights for allocation of densities at nodes.

135

MORPHOLOGY AND FLOW

7.2 A

C

B Y 03

C 07

B 10

NEW DENSITY 116.095 ppl / sq km FLOW 56.354 ppl / day

4-5 Levels

High density 4 or 5 levels

7-12 Selected nodes and associated densities

136

Medium - High

3-4 Levels

G 03

P 15

P 03

NEW DENSITY 73.819 ppl / sq km FLOW 40.953 ppl / day

Section Density Ranges: High

B 03

NEW DENSITY 7.476 ppl / sq km FLOW 20.988 ppl / day

Section Density Ranges: Medium - High 3-4 Levels

Medium/High density 3 or 4 levels

Medium

2-3 Levels

Medium

Medium density 2 or 3 levels

2-3 Levels

Low

1-2 Levels

Low density 1 or 2 levels

MORPHOLOGY AND FLOW

7.2

Required Market Density (MD) = %Flow x Market Range

7-13 Market Density formula developed as the product of the flow at a pedestrian line and the range of the market at the node.

Required Required Marke M MD =MD % =flow% xflow Ra

%Flow

Low Low

NEW DENSITY 73.819 ppl /Medium sq km Medium

Sectional Sectional Density Density Sectional Sectional Density Medium density LowDensity density 1-2 Levels 1-2 2Levels 2-3 Levels 2-3 Levels or 3 levels 1 or 2 levels Node Node densitydensity determines determines sectional sectional range range levels levels

Development of Section to Node Population Flow In order to allocate the flow of each pedestrian line to the section, we developed a method that would link the height levels to the percentage of flow from the node. With the range provided at each node, the percentage of flow of the line is applied through a formula that finds the Market Density (Fig 7-13). This is the product of the flow and total range of the node which is then added to the start of the range of the node. The aim of this formula was to place the flow percentages within the ranges at the node. The result is a percentage that represents the flow within the range of density to the node (Fig 7-14). The final step in the process involves the articulation of the Market Density percentage into a measurable area represented in section. This was de-

Pedestrian Pedestrian path line pathatline node at node specifies specifies percentage percentage of flow. of flow.

veloped as a ratio of two areas, a maximum of what could be existent if a full five levels was articulated to an actual area of what would provide the percentage of the market density to be achieved (Fig 7-15).

Calculation of Market Section Area The last step of the development in the section requires a method to find the areas of the cross section. The first step is to find the area of the ground level market; this is done by multiplying the width by the height (Fig 7-16). This area is then multiplied by five to calculate the total area that could exist if the full five levels of market were developed (Fig 7-17). The Market Density is then found by taking the area of the actual section divided by the total of five levels (Fig 7-18).

The Market The Market Density Density perc of theofsectional the sectional areas.are

7-14 Left: Node density determines sectional levels Right: Pedestrian path line at node specifies percentage of flow

137

MORPHOLOGY AND FLOW

7.2

Required Market Density (MD) MD = % flow x Range

7-15

Market Density (%) =

The Market Density percentage represents the ratio of the sectional areas.

Market Density xx%

e at node specifies 7-16 (Left) .

Step 1 Initial section provides First Level Area and total of section provides Sectional Area.

The Market Density percentage represents the ratio of the sectional areas.

Calculation of the Section Area of Market

7-17 (Centre) Step 2 Find Total Area from First Level Area multiplied by five, which represents maximum floor level.

W

h street

street block

7-18 (Right)

First Level Area Section Area

Step 3 Market Density is the Sectional Area divided by the Total Area.

Total Area = First Level Area x

5

Market Density (MD) = Section Area / Total Area

10

7-19 Worked example of a three level section.

From the calculations, the Market Density of the section is 20%.

138

9m 3m First Level Area = 30 sq m Section Area = 90 sq m (all 3 levels)

Total Area = 150 sq m Total Area = 30 sq m x 5

MD = 60% MD = 90 sq m / 150 sq m

MORPHOLOGY AND FLOW

7.2

Worked Example: Calculation of Market Section Area The first level area as shown is 30 square meters, a product of the total height of three levels by the depth of 10 meters. The sectional area is 90 square meters, as a product of the first level applied three times. In the second step the 30 square meters is then multiplied by five which provides a total of 150 square meters. In the last step the section area is divided by the total area and provides a percentage that represents the market density. The result is that the area of 30 square meters divided by 150 square meters is 60% market density (Fig 7-19).

Worked Example: Calculation of Market Density at Nodes In the worked example we chose the node with medium density to develop for clarification. The ranges of this node were from medium to medium high which represents a change from two to four levels or from 26% to 75% market density. The first step takes into account these levels, which indicate the start range and total range. The next step applies the flow of the node to this range. The percentage of

flow at the selected pedestrian path is 55%. This value is multiplied by the total range of the node and then added to the start range. The result is 52% market density. In the third step we take the market density and find a square area that represents this amount in ratio. The method used to calculate the sectional area is used in this step. In this equation we take the total area which is given from the chosen section and multiply it by 55% to find the area of the actual section which should be 250 square meters for this specific example. (Fig 7-20).

Design Decisions in Market Density Section The market density ratio allows for an infinite number of possibilities on how to articulate the cross section profile.

Therefore, the selection of the profile requires a design decision on the attributes to apply in the morphologies. The first profile characteristic was a step back profile which allowed for more height of the section to be achieved. The widths at which the steps were held, was a minimum pathway at three meters to allow for circulation to develop up the faรงade. Another method used to create more variation within the sections was implementing a circulation path in the middle of the depth of the block (Fig 7-21) and also at the ground floor and interior side of the section.

Example: For a flow of 55% at a medium / medium-high density node MD is 52%

Flow at a node

Node

Section from flow and density Total area = 480 sq m (100%)

Area = 234 sq m (62%)

Medium - high 3-4 levels

First Level Area = 96 sq m

Medium 2-3 levels

% Population flow

Medium 2-3 levels Range 25-50% Start range = 26%

Medium-High 3-4 levels Range 50-75% Total range = 49%

Medium Density Node

MD = 52 % MD = (% flow * Total range) + Start range MD = (55% * 49) + 25 Pedestrian Path Line at Node

MD = 52 % MD = 234 sq m / 480 sq m Market Density Sectional Area

7-20 Worked example of Market Density developed at medium density node.

139

MORPHOLOGY AND FLOW

7.2

7-21 Sectional development of markets and circulation.

7-22 Emergent pathways as a result of morphological generation

As an example in the lowest density node, the circulation was placed mid-block to allow for a new edge condition to evolve in between the market areas. In this step, the decision regarding which section to apply to the node and how the percentage is articulated was necessary to be made. The designs we developed used combinations of circulation on the front façade, interior of the section and at the back of section to provide a variation. Our aim was to then develop conclusions on the forms after application to the site.

Therefore we can estimate that the development of the urban tissue reflected cultural aspects of the city. Certain fabric conditions such as block lengths that extended for greater than 440 meters in length were indicative of an unplanned fabric (Fig 7-24-25). This length that exceeds those typically found in Western cities, allows for the development of porosity along the block in the form of openings, displayed by façade shops and increased amount of access into the building.

Application of Section to Existing Urban Fabric

The method for applying the section to the site focused on the corners and creating streets to bring connections to the street into the block. As a result of this application, the interior of the block could be developed in future steps. As well as the section depth which was held at a constant and left open to design after the first application could be analysed.

From observation of various urban tissues, there did not appear to be an existing urban grid structure in any part of Lagos. After further research we found that the grid pattern on Lagos Island was an extension of the indigenous settlements that existed before Europeans arrived (Fig 7-23).1 1. Akin L. Mabogunje. “The Evolution and Analysis of the Retail Structure of Lagos, Nigeria.” Econmic Geography, Vol.40, No. 4 (Oct., 1964), p.304.

140

The housing space was allocated on a block to block basis.

In order to determine the required height for a block to be able to accommodate its expected population, we refer to the previous population distribution algorithm. For a given

MORPHOLOGY AND FLOW

7.2

7-23 (Top Left) Historical development of central business district on Lagos Island.

7-24 (Top right) Lagos built up areas in 1850 shown on current street map.

7-25 (Bottom right) Central Business District of Lagos in 1960. Proposed Lagos Island node shown in grey.

7-26 (Bottom Left) Central Business District of Lagos 1960's study of areas in relation to building heights.

7-27 (Bottom Right) Downtown Broad Street on Lagos Island, photo date estimated as around 1950's.

141

MORPHOLOGICAL REFINEMENT

7.3

Block Geometry

7-28 Calculation of required height for housing volume

Population Distribution ÎŁ Volumes = Housing volume to distribute

block, a simple algorithm calculates the average height of all volumes from the distribution which fall within it. This height is then applied to the housing portion of the crosssection. Depending on the market cross-section below, the housing tower may either be represented as one tower or, if the market cross-section is wide enough, it may be split into two towers.

Housing to Market Volumes The distribution of housing densities was then coupled to the market volumes (Fig. 7-28). The variations of these two sections produced an array of relationships that started to display the characteristics of each node. Areas where further development could occur became more apparent.

142

Suggested housing distribution

MORPHOLOGICAL REFINEMENT

7.3

High density node High Density Node

Medium density node Medium Density Node

Low Density Node Low density node

7-29 Catalogue of variations in housing to market volumes.

143

MORPHOLOGICAL REFINEMENT

7.3

7-30

High Density Node

Medium Density Node

1

1

2

2

3

3

4

4

5

5

6

6

7

7

8

8

9

9

Sectional Series at Nodes

Sectional series taken at each proposed node.

Proposed network

A sampling series was taken extending from north to south at each node in increments of 100 meters. The intent was to review the fabric densities compared to the lines of transit and distributions in the neighbourhoods. The results show the relationships that support the characteristics apparent at the nodes.

The highest density node, due to lack of existing fabric, developed condition where all housing distributions were in a surplus height of fifty meters to accommodate projected populations. This is visible in (section 1) with a limited frequency of streets in the existing grid and in (section 8) with an increased frequency. The distribution of the density throughout the node was affected as the clustering around areas intended for growth, such as the station, was not ap-

144

parent, (see section 5). This resulted in the edge condition where the cross section of the market was not at the same density as the housing, and the miss-matched volumes had flows that would suggest contradicting populations, as in (section 8) on the right half. To address these issues in future applications, steps to develop a system to address the lack of existing fabric around suggested areas of growth would be necessary for improving articulation.

The medium density node developed an equal relationship of grid to the proposed housing and market developments and this is clearly seen throughout the (sections 4-6). The heights were intended to decrease away from areas of growth and density and therefore should be the lowest at the edges of the node, see (section 4) on the left hand side.

MORPHOLOGICAL REFINEMENT

7.3

Low Density Node

1

2

3

4

5

6

7

8

9

The coupling of heights around areas of station and path is also clearly distributed as shown in (section 7) in two instances and in (section 5).

The lowest density node shares similar issues to the highest density node in that the lack of existing grid affected the distribution throughout the network and which didnâ&#x20AC;&#x2122;t display the intended characteristics for growth, as seen in (section 6 and 7) as limited areas of development. However in the case of this node, the existing fabric was isolated to the northern part of the node, and the proposed station didnâ&#x20AC;&#x2122;t suggest for new nodes to extend into the southern area, see (section 2 and 8). This isolation needs further refinement to address how to suggest new nodes in areas where the integration does not allocate new local nodes.

145

MORPHOLOGICAL REFINEMENT

7.3

High Density Node MD

Variations

Medium Density Node

Low Density Node 7-31 Developed sections of market to housing shown on left, in relation to the outcome of these sections applied to the grid shown right..

Section referenced in following relationship to flow analysis 146

MORPHOLOGICAL REFINEMENT

7.3

A

Y 03

Density of housing corresponding in the block = Building houses 1347 people (15 sq m/person)

100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10

Population flow

C 07

B 10

NEW DENSITY 116.095 ppl / sq km FLOW 56.354 ppl / day

Population flow

Market density Market density L1 L1

L2

L2 L4 L5 High L6 Density L7 L8 Pedestrian lines Pedestrian lines

L3

L3 L9

Relationship of densities to flow The application of varied sections related the density of the node to the flow of people at the path, in the form of a square footage per person in each volume.

In the highest density node the housing for one specific block accommodates 1347 people at about 15 square meters per person (Fig. 7-32).

100

7-32 Sample building (High Density Node)

147

MORPHOLOGICAL REFINEMENT

7.3 B

B 03 Density of housing corresponding in the block = Building houses 371 people (14 sq m/person)

P 15

NEW DENSITY 73.819 ppl / sq km FLOW 40.953 ppl / day

100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10

Population flow Population flow

Market density Market density L1 L1

7-33 Sample building (Medium Density Node) 148

L2 L2

L3 L4 L5 L6 L3 L4 L5 L6 Pedestrian lines Pedestrian lines

L7 L7

L8 L8

L9 L9

In the medium density nodes housing block for the chosen block housed 371 people which came to around 14 square meters per person. What was visible at this node was the averaging of the population flow per pedestrian path. The double tower condition was achievable in this market density section as the width into the block allowed for the space between the two (Fig. 7-33). The chart shows the relationship of the flows in populations and the market densities with housing.

100

MORPHOLOGICAL REFINEMTENT

7.3

C G 03 P 03

Density of housing corresponding in the block = Building houses 211 people (14 sq m/person)

NEW DENSITY 7.476 ppl / sq km FLOW 20.988 ppl / day

100 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10

Population flow Population flow

Market density Market density L1 L1

L2

L2 L4 L5 L6 Pedestrian lines Pedestrian lines

L3

L7

L8

L3 L9

In the lowest density node the housing blocks achieved the most proportionate relationship however the length of the blocks allowed for extremely long stretches of sections. These would need refinement of the block edge patterning to allow for connection to the above housing density.

7-34

100

Sample building (Low Density Node) 149

MORPHOLOGICAL REFINEMENT

7.3 A

Y 03

C 07

B 10

NEW DENSITY 116.095 ppl / sq km FLOW 56.354 ppl / day

As shown in the high density node A, the large area internal to a block could be used for informal markets.

7-35 Aerial view of high density node

7-36 (Top) Street view of high density node

7-37 (Bottom) Section at high density node 150

MORPHOLOGICAL REFINEMENT

7.3

151

MORPHOLOGICAL REFINEMENT

7.3 B

B 03 P 15

NEW DENSITY 73.819 ppl / sq km FLOW 40.953 ppl / day

In the medium density node B, the open space internal to a block was much smaller and therefore could be suggested to be green space, a program that is currently lacking in the urban fabric.

7-38 Aerial view of medium density node 7-39 Street view of medium density node

7-40 Section at medium density node 152

MORPHOLOGICAL REFINEMENT

7.3

153

MORPHOLOGICAL REFINEMENT

7.3 C

G 03 P 03

NEW DENSITY 7.476 ppl / sq km FLOW 20.988 ppl / day

Within the lowest density node C, the ratio of block size had the largest variations between internal spaces.

7-41 Aerial view of low density node

7-42 Street view of low density node

7-43 Section at low density node 154

MORPHOLOGICAL REFINEMENT

7.3

155

EVALUATION

7.4

7-44 High density node with the generated optimal network.

7-45 Sections of streets not facing generated network.

MD =edge condition -

MDlowest = Edge condition crossection Lowest cross section

65 65

156 156

156 156

269 269

534534

The volumes had been developed that shared an adjacent relationship to the paths of flow. However, in block conditions where the paths were not along the block (Fig. 7-45) the lowest cross section of the nodal range was applied. The result was edge conditions that werenâ&#x20AC;&#x2122;t necessary to accommodate high densities of housing that did not have a market density proportionate to this housing density.

156

1035 1035

1600 1600

623 623

1503 1503

1985 1985

1450 1450

850 850

EVALUATION

7.4

Existing Fabric Block Area

10,000 square metres Area Occupied

9,000 square metres Population

650 people Public Open Space

0

Suggested Tissue Block Area:

10,000 square metres Area Occupied:

7,000 square metres Population:

1,800 people Public Open Space

3,000 square metres

End Evaluation of Morphological Development When comparing the existing tissue of one block to that of the new proposal, drastic differences become apparent. The existing tissue is densely covered with low-density buildings. This means that even though the majority of the land is covered, there is a relatively low population for the block. The proposed system is able to house approximately three times as many people as the existing tissue while occupying a smaller footprint. Also, the amount of open space is increased greatly with the new system. Open space is a

rare commodity in Lagos, and the new system is capable of generating large amounts of it in every block. The third major improvement can be seen in terms of circulation.

The existing tissue affords little access to the inner parts of the block. The majority of the circulation happens around the edges on the main streets, with occasional opportunities to cut through blocks. The proposed system creates secondary paths at every block that give access to the interior open spaces of the block and allow for more efficient navigation of space.

7-46 Comparison of existing urban tissue to proposed tissue

157

158

8.0

CONCLUSIONS 8.1 System potential 8.2 Further refinement 8.3 Conclusions

159

SYSTEM POTENTIAL

8.1

8-1 Areas most suitable for application

8-2 Most African cities present markets in their streets.

8-3 Street life and markets usually happen in Asian cities when climate is appropiate. Night markets are also common.

160

SYSTEM POTENTIAL

8.1

2240 2111 2485

1752 518 1475 571

341

1021 2474

413 810

257

Conditions for Application of the System The application of the system is contingent on several aspects which make it suitable for only certain cities with specific conditions. The projected population growth is the first condition. The city growth should be estimated to be developing at an intense rate, in a short period of time.

These conditions are important as they reflect the type of programs that will develop during this period and the densities of population. These programs can be estimated to be those of high concentrations for public use and congregation. Therefore, the cities for future application are those that have projected growth rates that qualify them as emergent.

The second condition is the need for a publicly organized transportation infrastructure. One of the main conditions assumed in our implementation was that the populationâ&#x20AC;&#x2122;s dominant means of transport was that of the individual vehicle privately organized. Through the implementation of our proposed network, the means of transport was assumed to shift and accommodate the population growth through an organized means of public transport. This is critical to the development of the nodes and urban tissue, as it restructures the flows of the populations. Therefore, the potential to apply this scenario to other cities is reliant on the increased use of public transportation in the proposed network.

The third condition is that this city must have an existent urban fabric that has developed without planning. The core concept of our proposed system is that development occurs in relation to growth and densification. The population growth will generate the new city structure. In cities where the fabric has been previously planned, application of a system to optimize the growth would not be the driving structure behind the city.

1597 2458

1611

2287 1597 1478

1411

2601

tense rates and current established levels of planning and infrastructure are lacking. The refinement of our system will be to provide new connections associated with areas of activity in the existing fabric and to efficiently develop ways in which to address the existing morphologies that accompany large congregations of public use.

8-4 Local network generation

Review and Potentials of Connecting to an Existing Network In future applications of the system, the same aspects as considered for Lagos will be accounted for. In specific steps of your method, the information of the city could potentially allow for new development to occur. These are in the aspects of the city that require adjustments to the existing network, urban fabric and existing morphologies.

Existing Activity Points The global network simulation was influenced by a series of existing activity points of Lagos. These varied from informal markets, bus stops and shopping centres. In future applications of the system, we can assume that initial activity points would be of similar programs.

In the development of the regional network, the existing activity points at a more local scale to the station node were considered. For example, if a bus stop fell within the 500m radius of the new station, it was considered a local node to that station and as a destination where the population would travel (Fig 8-4). In future applications the proposed network could develop from these existing locations and will increase the connections between activity areas and transport. The potential for unique connections between activity points with the station could emerge and create integrated areas of programs that would not normally be coupled together.

Possibility of Connection to Existing City Fabric

Application in Other Cities

Existing Grid and Morphology Considerations

Therefore, the potential applications of this network are currently projected to be in cities of Africa, Asia, and South America where the population is expected to grow at in-

In summary of the methods we used to analyse the existing morphologies, the aim was to preserve the patterns 161

SYSTEM POTENTIAL

8.1

8-5 Existing street block pattern with proposed housing and market volumes.

8-6 Existing street block pattern.

440m

8-7 High density node street pattern with existing fabric condition.

162

SYSTEM POTENTIAL

8.1

and proportions of the facades that were inherent to the city streets accompanied to markets. In Lagos the development of the grid was not planned. It was formed irregularly within the boundary conditions that began on Lagos Island.

The existing buildings, aside from a few from the 1950â&#x20AC;&#x2122;s with Portuguese influence are comprised of a temporary construction that does not have a long life span. The construction methods have evolved from the need for quick installations of accessible materials to accommodate the rapid increase of people into the city. As these construction means are quick, they typically are not above a single storey in height. Therefore, the horizontal growth of the city is increasing and buildings are placed at any location in which there is space. Therefore, indigenous morphologies of the city and buildings are not a sustainable method to organize the development for future growth.

However, as the blocks of the city relate to the scale of the streets and the developments that have emerged to hold large congregations of people, the relative nature of this scale to the morphology was an important aspect (Fig. 9-7).

Rather than impose a new grid that would remove the block lengths that exist, the decision was made to keep the existing pattern and adjust the connections to the nodes in order to maintain a cultural relationship between market front to street flow (Fig. 9-6). The proposed morphologies developed based on an abstracted density to height relationship that could be found in existing areas of Lagos. Therefore, we used this as the basis for our generation of future volumes to handle increased growth within the fabric (Fig. 9-5).

the developed streets taken into account. The local programs associated with growth will also be informed by the existing culture.

In evaluation of our current proposal for Lagos, the morphological development allows for many possibilities of combinations. As our process uses a generative design approach, the system has an internal logic. Therefore, the results need to be evaluated with criteria that are not a part of the generative strategy, as a check against the initial method. One process we could use to develop this would be to clearly describe the morphology of the city and its built form against the proposed. This would begin to clarify the conditions unique in our application and verify that it is not another application that could be placed anywhere in the world. As was previously mentioned, the buildings themselves are not sustainable. The pattern of the street and existing square areas associated to these patterns is one quantity that could be measured. The development of a method that could account for the existing area of market and the associated housing in relation to this could provide this back test criteria necessary to check the methods. However, due to the lack of accessible information, the existing conditions in Lagos are up to speculation from photographs and aerial images. Therefore, we see this method of back testing as a way to develop future applications, as a review of the total proposal and provide perspective on the conditions that make the morphologies unique to that city.

In future applications in other cities, the scale and pattern of the grid will directly affect the outcome of the morphologies and developments of the urban fabric. These existing characteristics will make the applications unique to the city at each new node. The transition between the scale of the existing urban fabric block and the proposed morphologies is an aspect that could be further developed to have an informed relationship.

Potential for Morphology Development and Back testing The future application of the system will need to develop in a similar method as applied to Lagos. The potential for application in another city would develop from the cultural morphologies of that region with the scale and pattern of 163

FURTHER REFINEMENT

8.2

8-8 MD =edge condition -

Disproportionate lowestsections crossection

8-9 Phasing of system application Nodes immersed in city fabric Nodes currently out of Lagos congested zones

164

65

156

156

269

534

1035

1600

623

1503

1985

1450

850

FURTHER REFINEMENT

8.2

Edge Conditions One aspect of our system that was undeveloped was that the border condition between existing tissue and the newly proposed urban fabric often lacked definition or proportion. One reason for this is that the system for applying sections was dependant on having an estimated flow for the streets or paths adjacent to each block. In cases where there was no assumed flow, the cross-section with the lowest Market Density(MD) value was the default. This often resulted in cross-sections in which the housing was far disproportionate in relation to the market section below (Fig. 9-9).

A contingency needs to be developed in order to address this issue, allowing for a more appropriate morphology to develop in these areas. This could potentially be an opportunity to begin to integrate new programmatic morphologies that are not as dependent on flow. Not only could this begin to resolve the morphological issues, it may also lead to the emergence of new spatial and programmatic hierarchies within the city.

further as this happens. With a more intelligent system in place for this development, the result would be a far more stable urban fabric than that which exists currently.

Phasing and Priority Obviously, the major restructuring of urban fabric which we are suggesting cannot happen overnight. The process would have to be broken into phases and certain areas would need to be tackled first. The first step would be to start with the areas in the city that are the most chaotic and most in need of help. These areas are highly trafficked and they are in a constant state of renewal. Imposing a sense of structure on these areas would help to stabilise them and better prepare them for an influx of population and commuters. Once these critical areas have been developed and stabilised, development can begin on the less vital parts of the city. This diffusion will eventually lead to an urban fabric that is more structured and better equipped to handle the drastic changes in population and flow that Lagos is expecting.

Another opportunity for improvement of the border condition lies in the transition. Currently, the line between new and old is very distinct and abrupt. The low-rise existing tissue gives way quickly to the high-rise morphologies of the new proposal. This is really a result of the very nature of Lagosâ&#x20AC;&#x2122; current architectural identity. The buildings in Lagos are, at best, low quality buildings from the 1950â&#x20AC;&#x2122;s-60â&#x20AC;&#x2122;s or very low quality structures with a very short lifespan.

Because of their low quality, they are constantly being modified, fixed, or completely replaced. Our proposed morphologies represent a more lasting suggestion for how to develop Lagos. The process of constant rebuilding already takes place in the city in a very chaotic fashion, and our system acknowledges this and adds structure to it. Certain areas such as our three major nodes or other important parts of the city would serve as seeds for a greater restructuring of the city. Changes that begin in these areas would quickly diffuse into the city and the process could change and evolve 165

FURTHER REFINEMENT

8.2

Urban scale

Street orientation regarding prevailing winds

Block scale

Minimize solar radiation within every block's buildings

Minimize solar radiation within several blocks

Building scale

Cross ventilation

Openings % 8-10 Climatic considerations 166

Material and color

FURTHER REFINEMENT

8.2

Climatic evaluation The outcome of our generative system presents a solution for an increase of urban density in certain areas within a city. The morphologies developed up to this point respond efficiently in quantifiable terms to the expected programs and flows, but these morphologies can be further evaluated and developed.

Initially, the morphologies look like a valid design for any city. In order to tackle the evaluation of the morphologies, a climatic analysis could be done. This could become a valid evaluation criterion as these are two fields that have not been related in the system. It will possibly lead into a more site-specific design avoiding the generic appearance of the output.

A climatic analysis involves every possible agent in the climate such as wind, sunlight, solar radiation, and temperature. Lagos is in a tropical climate meaning that the atmospheric conditions are humid and warm without a drastic variation throughout the year. It also rains very frequently in this city with a slight decrease of rainfall from June to August. These factors can affect different scales and different elements in the urban context. We suggest a classification of this analysis in urban scale, block scale, and street scale.

First of all, in the urban scale, the orientation of the streets could be designed to address prevailing wind directions in order to benefit of the urban context. The ability to take advantage of, and channel the winds allows for the dispersion of accumulated heat from streets and public spaces and would be beneficial for Lagos. Apart from that, the distribu-

tion of the housing blocksâ&#x20AC;&#x2122; heights can be further modified to control direct solar radiation both in the interior of the blocks and the streets to keep temperatures close to comfort levels.

Streets in Lagos are the main urban space for social interaction. Therefore a huge amount of people are going to inhabit these urban spaces. With tropical climate, rainfall and direct sunlight access need to be controlled and avoided as much as possible.

Lastly, regarding the block scale, the proposed morphologies generate public spaces in the interior of the resulting blocks.

This creates the potential for green open spaces or parking space in a very dense city fabric. Further climatic evaluation should be done as well to test the characteristics of these areas.

The characteristics of the housing volumes should also be based on a climate-efficient design. For example, the width of the volumes was determined with the premise of assuring cross ventilation by reducing the width to the minimum. The layout of the dwellings should allow for this cross-ventilation. Also materials and their colors can also be considered in order to optimize thermal behavior. Also, a determined percentage of openings in the buildings can be suggested to control the levels of solar radiation on the interior.

The climatic evaluation of the morphologies would lead to a variation in the design. This would introduce site-specificity in the output of the system and the ability to be applied in any location of the world.

167

CONCLUSIONS

8.3

London New York Mexico City

Tokyo Lagos

Mexico City

New York

London

Tokyo After Implementation Before Implementation

Lagos 0

6

8-11 World Map showing studied cities regarding length of mass transport networks in km / km2.

Urban Networks The challenge of accommodating urban inhabitants has become increasingly complex as more people are living in cities. In turn, these cities are developing at intense rates of urbanization and becoming mega cities. Providing sufficient infrastructural networks for these populations is vital to ensuring the survival and continued growth of a city (Fig. 9-11).

Typically, a transportation network such as a public rail system starts small and expands as the city expands. However, in some cases, cities expand so fast and in such an uncontrolled fashion that a transportation system never has time to develop. Implementing a transportation network into this type of city presents a unique challenge and a unique opportunity. In the case of a city in its infancy beginning to grow, it is difficult to make decisions about where major nodes should go, and how people will use the newly developing areas. With an established city, much of this informa168

CONCLUSIONS

8.3

Gabriel Graph

Relative Neighbourhood Graph

Minimum Spanning Tree

Slime Mould Simulation

tion is already known. Therefore, more informed decisions can be made about what parts of the city should serve as major nodes for the implementation of a network.

System Development In order to implement a network in a city, our proposal functions as an adaptive computational system rather than a top-down implementation of a network. Through the power of agent-based computing, we are able to create intelligent network solutions that go beyond a simple â&#x20AC;&#x153;connect the dotâ&#x20AC;? approach. The system utilises simple rules of interaction between individual agents and their environment which lead to emergent behaviours which create robust and efficient solutions which could not be predicted beforehand (Fig. 9-12). This emergence leads to more possibilities than are possible from traditional methods for generating networks such as connection schemes like the Gabriel Graph, Relative Neighbourhood Graph, or Minimum Spanning tree, which are computed solely based on proximity of points.

Implementation In order to test the system, we decided to first apply it to the city of Lagos in Nigeria. The reason for this was that Lagos is projected to become the 11th largest city in the world by the year 2025. Despite its already large population and its projected growth, the city still has no major public transportation in place. There is a Bus Rapid Transit system in place, as well as private bus companies, but the traffic in the city is still a serious problem. Many residents start their morning commutes as early as 4 am in order to get to work on time. After designating 22 activity points within the city to serve as nodes, we applied the algorithm. We analysed several variants of the resulting network using Depth Map Space Syntax software in order to evaluate their effects on the urban fabric. The most effective of the solutions was developed further into a fully developed public rail network.

Final evaluations of the network showed an improvement in integration in 56% of the affected urban area (Fig. 9-13).

8-12 Slime mould simulation with emergent nodes and connection graphs

169

CONCLUSIONS

8.3

8-13 Space Syntax analysis before (top) and after (bottom) the implementation of the network 170

CONCLUSIONS

8.3

Potentials of the System Our experiment in Lagos shows the enormous impact that a network developed by our system can have on a city. Even though many of the inputs to the system were specific to Lagos, this system can be applied to any other city. Lagos was simply the first testing ground for the system. Every city has important areas which could be translated into nodes to be input into the system. The system could then be used to generate possible solutions for the selected city. The parametric nature of the system would also allow for adjustments to be made in order to create different outcomes based on the desired effect for the city.

Limitations of the System Even though the system has been successful to this point of producing effective solutions for network design, it is not without its limitations. The first and most obvious limitation is computing power. The Rhino/Grasshopper/Python hybrid is not the most effective environment for agent-based modelling and is therefore extremely computationally heavy.

One run of the simulation can take upwards of 4 hours to complete. Other scripting environments such as Processing are capable of producing results much faster because they are much more streamlined. Rhino/Grasshopper/Python has its advantages as well as it is very accessible and there is a large support community in place.

would become abandoned because all of the individuals in the system had gotten stuck into certain paths. Something that could be added in order to deal with this problem could be to build in a method of self-evaluation into the system.

This could be in the form of an “off switch” for the system so that when it reaches a certain state, it stops. This could be coupled with an evaluation that will add new agents to the system after a certain amount of time if the state has not been reached.

Agent-Based Computing for Adaptive Network Generation Our system shows the potential that agent-based computing has for the generation of adaptive networks. Traditional methods for generating networks result in static representations of a network. Our system, however, is one that is constantly changing and can be adjusted easily to change the course of the simulation. Our experiments have shown that the networks resulting from the simulation are not only effective in the sense of material used (network length), but they are also highly effective in terms of their impact on the integration of the urban fabric in which they are implemented. We believe that the system is a highly powerful tool, and a clear demonstration of the potentials of agent-based computing for generating network solutions for large urban areas.

Also, the idea of “complete” is quite ambiguous in this system. The system is simply given a time frame in which to run and the results are extracted from a frozen state.

This was apparent in some of our simulations where nodes 171

172

CONCLUSIONS

8.3

Conclusion In a time when the worldâ&#x20AC;&#x2122;s population is flocking to urban areas, emerging cities face enormous challenges in accommodating this influx. Perhaps the most important developments is moving these people through the city efficiently, and designing the morphological aspects of the city to be able to handle these increased flows. Creating a link between global network flows and local architectural morphologies allows for feedback to occur within the city. Changes in the network will affect the local morphologies, which will in turn re-inform the network. By phasing the application of this system, it will allow for a more gradual transition from the current urban model to a new and more efficient one where morphologies are linked to the flows generated by the global network being distributed into local areas. Developing the morphologies to better cope with transitioning into existing fabrics and adjusting to climatic issue will further refine and improve the systemâ&#x20AC;&#x2122;s ability to generate urban solutions.

In the end, this work addresses the complex nature of urban areas and the transportation networks that emerge from them. Through the use of agent-based computing and careful analysis and feedback, the network generation system is capable of suggesting extremely efficient and effective solutions for linking isolated areas and improving the overall integration of the city. As this information filters into the architectural scale, we begin to see suggestions of architectural morphology that arise not solely from the programmatic and cultural requirements of the site city, but also from the data inherent to the system. Flows and population densities become equally important in the generation of urban space. In this way we can strive for a new urban model which is more sustainable and stable in the chaotic realm of emerging cities.

173

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9.0

APPENDIX 9.1 Slime Mould scripts 9.2 Charts and tables 9.3 Station analysis

175

SLIME MOULD SCRIPTS

9.1

176

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9.1

###SLIME MOULD SIMULATION##################### ###CELL CLASS################################# import rhinoscriptsyntax as rs import scriptcontext as sc from operator import itemgetter import random import math ############################################## def cenPt(_ptL): ptL = _ptL xL = [] yL = [] zL = [] for i in range (0,len(ptL)): Xval = ptL[i][0] xL.append(Xval) Yval = ptL[i][1] yL.append(Yval) Zval = ptL[i][2] zL.append(Zval) cenPt = rs.AddPoint([math.fsum(xL)/len(xL),math.fsum(yL)/len(yL),math.fsum(zL)/len(zL)]) return cenPt

#Define Class of a Cell ############################################################## ############################################################## class cell: def __init__(self): ####All of the information you will need from the Class ###You can kind add to this as you go along and realize you need more stuff 177

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self.name = None self.start = None self.cent = None self.coord = None self.dest = None self.neighbors = [] self.visibleNeighbors = [] self.closestNeighbor = None self.startVect = None self.senseAng = None self.foodSources = [] self.pullFood = None self.closestFood = None self.closeIndex = None self.visibleFood = [] self.moveVect = None self.line = None self.eating = 0 self.steps = 0 def getStart(self,_start): self.start = _start cent = rs.CurveAreaCentroid(self.start) self.cent = cent[0] def getCoord(self,_pt):#This is the XYZ coordinate of the Cell Class self.coord = _pt def getName(self):#get the GUID of the class self.name = rs.AddPoint(self.coord) def getNeighbors(self,_list,_index): list = _list index = _index temp = list[:] temp.pop(index) 178

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9.1

self.neighbors = temp def seeNeighbors(self,_angle): angle = _angle for i in range(0,len(self.neighbors)): vect = rs.VectorCreate(self.neighbors[i].coord,self.coord) vect2 = rs.PointAdd(self.coord,self.startVect) ang = rs.VectorAngle(vect,vect2) if ang > float(angle)/2: pass else: self.visibleNeighbors.append(self.neighbors[i]) def getClosestNeighbor(self): distL = [] for i in range(0,len(self.neighbors)): tmpL = [] tmpL.append(i) dist = rs.Distance(self.coord,self.neighbors[i].coord) tmpL.append(dist) distL.append(tmpL) distL.sort(key = itemgetter(1,0)) self.closestNeighbor = self.neighbors[distL[0][0]] def getDest(self,_Boolean): self.dest = _Boolean def getVect(self,_destination):#Initial Vector of movement outward from center Destination1 = _destination if self.startVect == None: if self.dest == 0: self.startVect = rs.VectorCreate(self.coord,self.cent) elif self.dest ==1: cent = rs.CircleCenterPoint(Destination1) self.startVect = rs.VectorCreate(cent,self.coord) else: 179

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9.1

self.startVect = self.startVect def newVect(self,_pt): pt = _pt self.startVect = rs.VectorCreate(self.coord,pt) def getFood(self,_foods):#Get All of the Food sources self.foodSources = _foods def getPullFood(self): distL = [] for i in range(0,len(self.foodSources)): tempL = [] dist = rs.Distance(self.coord,self.foodSources[i].coord) tempL.append(i) tempL.append(dist) distL.append(tempL) distL.sort(key = itemgetter(1)) self.pullFood = self.foodSources[distL[0][0]] def getPulled(self,_factor,_maxRad): factor = _factor maxRad = _maxRad circ = rs.AddCircle(self.pullFood.coord,self.pullFood.pullRadius) if rs.PointInPlanarClosedCurve(self.coord,circ) == 1: pullVect = rs.VectorCreate(self.pullFood.coord,self.coord) pullVect = rs.VectorUnitize(pullVect) pullVect = rs.VectorScale(pullVect,factor*(self.pullFood.radius/maxRad)) self.startVect = rs.VectorAdd(self.startVect,pullVect) else: pass def seeFood(self,_angle):#Break out all the Food Sources the cell can “see” angle = _angle self.senseAng = angle self.visibleFood = [] for i in range(0,len(self.foodSources)): 180

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9.1

foodVect = rs.VectorCreate(self.foodSources[i].coord,self.coord) newVect = rs.PointAdd(self.coord,self.startVect) newLine = rs.VectorCreate(newVect,self.coord) ang = rs.VectorAngle(foodVect,newLine) if ang>float(angle)/2: pass #If outside the angle of view, ignore it else: self.visibleFood.append(self.foodSources[i]) #If inside the angle of view, add to the list of visible food def closeFood(self):#Find the closest visible food source foodSources = [] for i in range(0,len(self.visibleFood)):#Measure distance to each food source, and sort the list according to the distance tmpL = [] num = i tmpL.append(num) dist = rs.Distance(self.coord,self.visibleFood[i].coord) tmpL.append(dist) foodSources.append(tmpL) foodSources.sort(key = itemgetter(1)) if len(self.visibleFood) > 0: self.closestFood = self.visibleFood[foodSources[0][0]] self.closeIndex = foodSources[0][0] elif len(self.visibleFood) == 0: #

foodL = self.foodSources[:] distL = [] for i in range (0,len(self.foodSources)): tmpL = [] tmpL.append(i) dist = rs.Distance(self.coord,self.foodSources[i].coord) tmpL.append(dist) distL.append(tmpL) distL.sort(key = itemgetter(1))

181

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def eatFood(self): if len(self.visibleFood) > 0: circ = rs.AddCircle(self.closestFood.coord,self.closestFood.radius) if rs.PointInPlanarClosedCurve(self.coord,circ) == 1 and self.closestFood.state == 0: self.eating = 1 #1 means it is at a food source self.start = self.closestFood.name else: self.eating = 0 #0 means it is still moving def move(self,_maxDist,_step,_boundary,_obstacles):#Define the movement of the cell step = _step obstacles = _obstacles boundary = _boundary maxDist = _maxDist if len(self.visibleFood) > 0: dist = rs.Distance(self.coord,self.closestFood.coord) if dist < maxDist:#If the food is close enough and visible foodVect = rs.VectorCreate(self.closestFood.coord,self.coord) foodVect = rs.VectorUnitize(foodVect) foodVect = rs.VectorScale(foodVect,step) test = rs.PointAdd(self.coord,foodVect) tmpL = [] for i in range(0,len(obstacles)): tmpL.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) if rs.PointInPlanarClosedCurve(test,boundary) != 0 and math.fsum(tmpL) == 0:

self.coord = rs.PointAdd(self.coord,foodVect) self.moveVect = foodVect self.startVect = foodVect elif rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: while rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: inside = []

182

Ang),[0,0,1])

foodVect = rs.VectorRotate(foodVect,random.randint(-self.senseAng,self.sense-

SLIME MOULD SCRIPTS

9.1

test = rs.PointAdd(self.coord,foodVect) for i in range(0,len(obstacles)): inside.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) tmpL = inside

self.coord = rs.PointAdd(self.coord,foodVect) self.moveVect = foodVect self.startVect = foodVect elif rs.PointInPlanarClosedCurve(test,boundary) == 0: while rs.PointInPlanarClosedCurve(test,boundary) == 0: Ang),[0,0,1])

foodVect = rs.VectorRotate(foodVect,random.randint(-self.senseAng,self.sensetest = rs.PointAdd(self.coord,foodVect) self.coord = rs.PointAdd(self.coord,foodVect) self.moveVect = foodVect self.startVect = foodVect

else:#If the food is visible, but too far away vect = rs.VectorUnitize(self.startVect) vect = rs.VectorScale(vect,step) test = rs.PointAdd(self.coord,vect) tmpL = [] for i in range(0,len(obstacles)): tmpL.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) if rs.PointInPlanarClosedCurve(test,boundary) != 0 and math.fsum(tmpL) == 0:

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect elif rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: while rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: inside = [] Ang),[0,0,1])

vect = rs.VectorRotate(vect,random.randint(-self.senseAng,self.sense183

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9.1

test = rs.PointAdd(self.coord,vect) for i in range(0,len(obstacles)): inside.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) tmpL = inside

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect elif rs.PointInPlanarClosedCurve(test,boundary) == 0: while rs.PointInPlanarClosedCurve(test,boundary) == 0: vect = rs.VectorRotate(vect,random.randint(-self.senseAng,self.sense-

Ang),[0,0,1])

test = rs.PointAdd(self.coord,vect) self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect elif len(self.visibleNeighbors) > 0: pts = [] for i in range(0,len(self.visibleNeighbors)): pts.append(self.visibleNeighbors[i].coord) target = cenPt(pts) vect = rs.VectorCreate(target,self.coord) vect = rs.VectorUnitize(vect) vect = rs.VectorScale(vect,step) test = rs.PointAdd(self.coord,vect) tmpL = [] for i in range(0,len(obstacles)): tmpL.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) if rs.PointInPlanarClosedCurve(test,boundary) != 0 and math.fsum(tmpL) == 0:

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect 184

self.startVect = vect

SLIME MOULD SCRIPTS

9.1

elif rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: while rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: inside = [] vect = rs.VectorRotate(vect,random.randint(-self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect) for i in range(0,len(obstacles)): inside.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) tmpL = inside

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect elif rs.PointInPlanarClosedCurve(test,boundary) == 0: while rs.PointInPlanarClosedCurve(test,boundary) == 0: vect = rs.VectorRotate(vect,random.randint(-self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect) self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect else: vect = rs.VectorRotate(self.startVect,random.randint(-10,10),[0,0,1]) test = rs.PointAdd(self.coord,vect) tmpL = [] for i in range(0,len(obstacles)): tmpL.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) if rs.PointInPlanarClosedCurve(test,boundary) != 0 and math.fsum(tmpL) == 0:

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect elif rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: while rs.PointInPlanarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: 185

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9.1

inside = [] vect = rs.VectorRotate(vect,random.randint(-self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect) for i in range(0,len(obstacles)): inside.append(rs.PointInPlanarClosedCurve(test,obstacles[i])) tmpL = inside

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect #

vect = rs.VectorCreate(self.closestNeighbor,self.coord)

#

vect = rs.VectorUnitize(vect)

#

vect = rs.VectorScale(vect,float(step)/2) elif rs.PointInPlanarClosedCurve(test,boundary) == 0: while rs.PointInPlanarClosedCurve(test,boundary) == 0: vect = rs.VectorRotate(vect,random.randint(-self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect) self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect self.startVect = vect def stepCount(self): self.steps += 1

###SLIME MOULD SIMULATION#################### ###FOOD SOURCE CLASS######################### import rhinoscriptsyntax as rs import scriptcontext as sc from operator import itemgetter import random class snacks: def __init__(self): 186

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9.1

self.name = None self.state = 0 self.radius = None self.circ = None self.pullRadius = None self.pull = None self.count = 0 def getName(self,_guid): self.name = _guid def getCoord(self): cen = rs.CurveAreaCentroid(self.name) self.coord = cen[0] def getRadius(self): self.radius = rs.CircleRadius(self.name) # #

def drawCirc(self): self.circ = rs.AddCircle(self.coord,self.radius) def getPull(self,_radiusScale,_pullfactor): scale = _radiusScale self.pullRadius = self.radius*scale self.pull = _pullfactor def getState(self,_slime): slime = _slime circ = rs.AddCircle(self.coord,self.radius) if self.state != 2: if rs.PointInPlanarClosedCurve(slime.coord,circ) == 1: self.state = 1 #Iâ&#x20AC;&#x2122;m being eaten else: self.state = 0 #Iâ&#x20AC;&#x2122;m not being eaten elif self.state == 2: self.state = 2 def setState(self): if self.state == 0: 187

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self.state = 0 elif self.state == 1: self.state = 2#I canâ&#x20AC;&#x2122;t be eaten elif self.state == 2: self.state = 2 def startState(self,_state): self.state = _state def setCount(self,_pt): circ = rs.AddCircle(self.coord,self.radius) pt = _pt if rs.PointInPlanarClosedCurve(pt,circ) == 1: self.count +=1 else: self.count = self.count

###ADAPTIVE FLUX MORPHOLOGIES### ###Slime Mould Simulation Master Script### ###Main Parameters for change are: #####Sensing Angle #####Sensing Distance #####Number of Cells

############################################# import rhinoscriptsyntax as rs import scriptcontext as sc import math from operator import itemgetter import random import cellFood_2012_8_1GH as cell import snacks_2012_7_30GH as snacks #############################################

188

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9.1

cellPts = [] slimeL = [] FoodL = [] moves = [] FoodCent = [] neighborhoods = [] counts = []

###DETERMINE MAX RADIUS OF NODES##################### maxRad = 0 for i in range (0,len(start)): for i in range (0,len(start)):###finding the biggest radius rad = rs.CircleRadius(start[i]) if rad > maxRad: maxRad = rad ##################################################### ####START CONDITIONS################################# if RESET: reload(cell) reload(snacks) for i in range (0,len(start)): radius = rs.CircleRadius(start[i]) num = numCells*(radius/maxRad) div=rs.DivideCurve(start[i],int(num)) for j in range(0,len(div)): cellPts.append(div[j]) slime = cell.cell() slime.getStart(start[i]) slime.getCoord(div[j]) slime.getName() slime.getDest(random.randint(0,1)) slime.getVect(Destination1) 189

SLIME MOULD SCRIPTS

9.1

slimeL.append(slime) for i in range(0,len(food)):###Makes empty food classes Food = snacks.snacks() Food.getName(food[i]) Food.getCoord() Food.getRadius() Food.getPull(radScale,pullFactor) circ = rs.AddCircle(Food.coord,Food.pullRadius) neighborhoods.append(circ) FoodL.append(Food) sc.sticky[‘points’] = slimeL sc.sticky[‘food’] = FoodL ###RUN THE SIMULATION################################# else: slimeL = sc.sticky[‘points’] FoodL = sc.sticky[‘food’] for j in range(0,len(FoodL)): circ = rs.AddCircle(FoodL[j].coord,FoodL[j].pullRadius) neighborhoods.append(circ) FoodCent.append(FoodL[j].coord) for i in range(0,len(slimeL)): slimeL[i].getNeighbors(slimeL,i) slimeL[i].seeNeighbors(angle) slimeL[i].getClosestNeighbor() slimeL[i].getFood(FoodL) if slimeL[i].steps > minStep: slimeL[i].getPullFood() slimeL[i].getPulled(pullFactor,maxRad) else: pass slimeL[i].seeFood(angle) slimeL[i].closeFood() 190

SLIME MOULD SCRIPTS

9.1

slimeL[i].move(maxDist,step,boundary,obstacles) slimeL[i].eatFood() slimeL[i].stepCount() moves.append(slimeL[i].coord) for j in range(0,len(FoodL)): counter = [] for k in range (0,len(slimeL)): FoodL[j].setCount(slimeL[k].coord) counter.append(FoodL[j].count) counts.append(FoodL[j].count) sc.sticky[‘points’] = slimeL sc.sticky[‘food’] = FoodL endmoves = moves performance),rs.PointCoordinates(pt),200)

191

CHARTS AND TABLES

9.2 Chart 9-1

District Density Method First Step: Chart shows estimated train length in correlation to district populations.

For example we focused on the district area of Agege.

Local Government Area

Agege

Estimated light rail, Train Length passengers per Meters (use peak hour 2012 Railcap sheet) 28,464

150

Type of train control system

Signaling minimum headw Dwell Time seconds

Operating Margin second Ajeromi/Ifelodun Alimosho Amuwo/Odofin

42,625

124

TOTAL HEADWAY second

133,377

229

TRAINS PER HOUR

24,143

50

Passenger per metre Loading Diversity Train Length metres

192

Apapa

16,494

50

Eti-Osa

38,550

125

Ifako/Ijaiye

32,468

71

Ikeja

23,766

62

Ikorodu

34,994

70

Kosofe

57,206

78

Lagos/Island

15,892

83

Lagos/Mainland

27,678

65

Mushin

38,866

114

Ojo

45,383

90

Oshodi/Isolo

39,076

100

Shomolu

28,358

95

Surulere

34,013

115

ACHIEVABLE CAPACITY

CHARTS AND TABLES

9.2

Chart 9-2 District Density Method Second Step: Assuming the total length of the train the Rail Capacity spreadsheet projects the total passengers per peak hour per direction.

ed ngth (use sheet)

Type of train control system Signaling minimum headway

55

Dwell Time seconds

35

Operating Margin seconds

25

124

TOTAL HEADWAY seconds

115

229

TRAINS PER HOUR

31.3

150

50

50 125

Blue indicates entries that can be altered but assume defualt inputs

Passenger per metre

8.0

Loading Diversity

0.8

Train Length metres

150

Train length adjusted to match passenger per peak hour demands from density

30,000

passenger per peak hour direction

ACHIEVABLE CAPACITY

71 62 70 78 83 65 114

90 100

193

CHARTS AND TABLES

9.2 Chart 9-3

District Density Method Third Step: Station Spacing and Count.

The chart shows the breakdown of districts into densities.

Three categories (A,B,C) are used to distribute the station placements along the lines.

These are applied after ranking the populations of the districts.

Local Government Area

Agege The result was an allocation of the number of stations along the lines based off the range of populations from the districts.

Total Length of Track 7334

Density 2012

Distance

(per km2)

(feet)

Number of stations

84714 A

1430

8

3691

115514 A

1430

3

Alimosho

14594 3143

24006 B

2318

8

Amuwo/Odofin

11473 3546 1306

5979 C

3210

5

Apapa

4474

20591 B

2318

2

Eti-Osa

2769 5417

6682 C

3210

3

660

40687 B

2318

0

7840

17147 C

3210

4

4470

Ajeromi/Ifelodun

Ifako/Ijaiye Ikeja

3688

Ikorodu

3624

2280 C

3210

1

Kosofe

10344 11766

23426 B

2318

10

Lagos/Island

1344 1187

60890 A

1430

2

Lagos/Mainland

4786

47312 B

2318

2

Mushin

788 2445 2924

74031 A

1430

4

0

9562 C

3210

0

Oshodi/Isolo

1934

29074 B

2318

1

Shomolu

628 3508 1408

81487 A

1430

4

Surulere

465

49295 A

1430

0

Ojo

194

CHARTS AND TABLES

9.2

Chart 9-4

Vav Vav average average speedspeed Tst Ttime to time cover to cover singlesingle track track section section st Lst Lst lengthlength of single of single track track section section L train L length train length Ns Ns number number of stations of stations on single on single track track section section td station td station dwell dwell time time vmax vmax maximum maximum speedspeed reached reached ds ds deceleration deceleration rate rate tjl jerk tjl limiting jerk limiting time time tbr tbr operator operator and braking and braking system system reaction reaction time time SM SM speedspeed margin margin tom tom operating operating margin margin tsl time tsl to time throw to throw and lock and switch lock switch

26 26 km/h km/h 16 1029 1029seconds seconds 7334 7334m m 150 150m m 8 8 35 35 seconds seconds 55 55 km/h km/h 1.3 1.3m/s2 m/s2 0.5 0.5seconds seconds 1.5 1.5seconds seconds 1.1 1.1seconds seconds 20 20 seconds 6 6 seconds

Dark blue Darkindicates blue indicates District Density Method output output values values Fourth Step:

16mph mph

Darkindicates grey indicates Dark grey Speed and Time. that entriesentries that can becan be but assume alteredaltered but assume In a spread sheet that output defaultdefault inputs inputs the time and speed estimates of a single length of track, we applied our results.

For a 150 m length vehicle over a 7324 m span of track Grey indicates Grey indicates it will approximately take assumptions assumptions from from 17 minutes.

This results is defualtdefualt entriesentries accurate assuming the length of the tracks and speed however needs refinement to correctly display the distance between the stations.

60

A

55

B

50

45

C

Completely Grade Separated

At Grade with Highway Crossings

Operation on Median Strip 50% Signal Pre-Emption

Average Schedule Speed (KM/H)

40

D

35

Operation on Median Strip No Pre-Emption

30 500 m = 1 640 feet

25

E

20

Mixed Mode or Transit Mall Chart 9-5

15

District Density Method Fifth Step:

10

Re-evaluation based on Speed.

5

0

0

1.2

0.4

1.6

0.8

1.0

1.2

Distant Between Passenger Stops (KM)

1.4

1.6

1.8

To confirm the steps of this method correctly predicted a feasible outcome, we checked the results. This chart shows a ratio of the distance between stations to an estimated speed.

We were able to confirm that our output provided a ratio that fell within this graph.

195

CHARTS AND TABLES

9.2

G 20

R 01

G 19

R 02

G 18

R 03

g 04 g 03

G 17 G 16

R 04

G 15

G 14

R 05

R 06

G 12

G 11

g 02 G 13

R 07 R 08

R 09

R 10

Y 08

R 11

R 12

R 13

R 14

R 15

B 16

R 17

B 17

B 18

B 19

B 20

R 18

R 19

R 20

R 21

B 15 B 14

Y 07

B 13

G 10

B 12 B 11

Y 06 G 09 G 08

R 16

C 02

C 01

C 03

Y 05

C 04

y 01

y 02

C 05

C 06

y 03

C 07

B 10

y 04

y 05

y 06

C 08

C 09

C 10

B 09

G 07 Y 04

B 08

G 06

B 07

Y 03 G 05

B 06 B 05

Y 02

G 04

B 04 G 01

G 02

G 03

P 01

P 02

P 03

P 04

P 05

P 06

Y 01 P 07

P 08

P 09

P 10

P 11

P 12

P 13

P 14

B 03

P 16

P 15 B 02 B 01

196

P 17

P 18

CHARTS AND TABLES

9.2

Chart 9-6 Graphic Network Schematic Line Color

P 04

LinePColor Line Symbol 04 Station # Line Symbol Station #

Pink Line G g Line Pink Green Line Green Line Line Y y Yellow P

P G g Y y B

Yellow Line Line B Blue Blue C Line Cyan Line

C

Cyan Line R Red Line

R

Red Line

Interchange Station Interchange Station Airport AirportNational Rail National Rail Riverboat services Riverboat services

197

CHARTS AND TABLES

9.2 Chart 9-7

All station categories

broken

4

Avg passengars/ daily

Category

Station #

R-01 , G-20

55,022.16

B

R-02, G-19

55,022.16

R-03, G-18

Avg passengars/ daily

Category

C-07, y-03, B-10

68,790.27

A

B

C-08, y-04

45,693.13

B

55,022.16

B

C-09, y-05

45,693.13

B

R-04, G-16

55,022.16

B

C-10, y-06

45,693.13

B

R-05, G-15

55,022.16

B

B-01

32,692.03

B

R-06, G-14

55,022.16

B

B-02

32,692.03

B

R-07, G,13

55,022.16

B

B-03, P-15

40,953.56

B

R-08

19,500.05

C

B-04

32,692.03

B

R-09

19,500.05

C

B-05

36,335.40

B

R-10

19,500.05

C

B-06

36,335.40

B

R-11

19,500.05

C

B-07

36,335.40

B

R-12

19,500.05

C

B-08

36,335.40

B

R-13

19,500.05

C

B-09

22,930.09

B

R-14

25,611.33

B

B-10, y-03, C-07

68,790.27

A

R-15

25,611.33

B

B-11

25,611.33

B

R-16

25,611.33

B

B-12

25,611.33

B

R-17, B-16

51,222,65

B

B-13

25,611.33B-13

B

R-18, B-17

79,894.09

B

B-14

25,611.33

B

R-19, B-18

79,894.09

B

B-15

25,611.33

B

R-20, B-19

79,894.09

B

B-16, R-17

51,222.65

B

R-21, B-20

79,894.09

B

B-17, R-18

79,894.09

B

C-01, G-08

55,022.16

B

B-18, R-19

79,894.09

B

C-02

10,494.13

C

B-19, R-20

79,894.09

B

C-03

23,318.56

B

B-20, R-21

79,894.09

B

C-04, Y-05

46,637.12

B

P-01, G-01

20,988.26

C

C-05, y-01

46,637.12

B

P-02, G-02

20,988.26

C

C-06, y-02

46,637.12

B

P-03, G-03

20,988.26

C

Station #

198

into

Lines

Lines

CHARTS AND TABLES

9.2

Station #

Lines

Lines

Avg passengars/ daily

Category

y-04, C-08

45,693.13

B

C

y-05, C-09

45,526.08

B

10,494.13

C

y-06, C-10

45,526.08

B

P-07, Y-01

20,988.26

C

G-01, P-01

20,988.26

C

P-08

50,183.42

B

G-02, P-02

20,988.26

C

P-09

50,183.42

B

G-03, P-03

20,988.26

C

P-10

50,183.42

B

G-04

10,494.13

C

P-11

21,348.27

C

G-05

10,494.13

C

P-12

21,348.27

C

G-06

27,511.08

B

P-13

21,348.27

C

G-07

27,511.08

B

P-14

8,261.53

C

G-08, C-01

55,022.16

B

P-15, B-03

40,953.56

B

G-09

27,511.08

B

P-16

8,261.53

C

G-10

27,511.08

B

P-17

8,261.53

C

G-11

27,511.08

B

P-18

8,261.53

C

G-12, Y-08

55,022.16

B

Y-01, P-07

20,988.26

C

G-13, R-07

55,022.16

B

Y-02

10,494.13

C

G-14, R-06

55,022.16

B

Y-03

23,318.53

B

G-15, R-05

55,022.16

B

Y-04

23,318.53

B

G-16, R-04

55,022.16

B

Y-05, C-04

46,637.12

B

G-17

27,511.08

B

Y-06

23,318.56

B

G-18, R-03

55,022.16

B

Y-07

23,318.56

B

G-19, R-02

55,022.16

B

Y-08, G-12

55,022.16

B

G-20, R-01

55,022.16

B

y-01, C-05

46,637.12

B

g-02

27,511.08

B

y-02, C-06

46,637.12

B

g-03

62,781.69

B

y-03, C-07, B-10

68,790.27

A

g-04

62,781.69

B

Avg passengars/ daily

Category

Station #

P-04

10,494.13

C

P-05

10,494.13

P-06

199

STATION ANALYSIS

9.3 Table 9-8

Pedestrian network generation for individual stations

B-03, P-15

B-08

P = 0.733282663083

B-04

P = 0.736598536306

B-09

P = 0.611356474015

B-05

P-03, G-03

B-11

B-06

B-07

P = 0.228842037465

P = 0.60068450932

P = 0.709953233937

P-10

P = 0.396945453757

P-06

P = 1.0

P = 0.778516923936

P-09

P-05

P-01, G-01

P = 0.695335331408

P-08

P-04

B-12

P = 0.610478485658

P = 0.510913379274

P = 0.555544757598

P = 0.651477037645

P-07, Y-01

P = 0.621327323944

P = 0.509717454709

P = 0.799289540087

200

P-02, G-02

P = 0.609653293019

P-11

P = 0.666895934201

P = 0.627846437331

STATION ANALYSIS

9.3

P-12

P-18

P = 0.621794838328

P = 0.62029455157

P-13

Y-02

P-14

Y-08, G-12

Y-03

P = 0.375691977684

P-16

P = 0.685996029271

g-03

P = 0.788654576918

P = 0.822509139744

P = 0.66746573792

P = 0.910355138512

g-04

G-06

Y-06

P = 0.877495203736

P = 0.617755945465

P = 0.534669893776

P = 0.646938943467

P = 1.0

g-02

G-04

Y-04

P-17

P = 0.669927281345

P = 1.07546723747

P = 0.640466859255

G-11

Y-07

P = 1.0

G-07

P = 0.751512879274

P = 0.977313139224

201

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205

ILLUSTRATION CREDITS

ABSTRACT 0-1 http://bitacora.citythinking.net/2012_03_01_archive.html

CHAPTER 1: Introduction 1-1 http://www.yannarthusbertrand2.org/index2.php?option=com_datsogallery&func=wmark&mid=2041 1-2 http://bitacora.citythinking.net/2012_03_01_archive.html 1-3 http://www.freefoto.com/preview/23-83-12/Railway-Track

CHAPTER 2 : Domain Title http://www.flickr.com/photos/7186330@N02/3353773931/sizes/l/ 2-2 maps.google.com 2-2 http://www.cittasostenibili.it/urbana/urbana_L_10.htm 2-3 http://www.flickr.com/photos/aganderson/6221646727/sizes/l/ 2-4 http://www.fastcodesign.com/1665884/infographic-of-the-day-could-twitter-help-us-create-smarter-transit-routes 2-5 http://www.lablog.org.uk/category/lab1amb07/ 2-6 http://freeassociationdesign.wordpress.com/tag/landscape-infrastructure/ 2-7 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.160 2-8 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.160 206

ILLUSTRATION CREDITS

2-9 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.147 2-9 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.236 2-10 http://upload.wikimedia.org/wikipedia/commons/6/6f/Piazza_della_Signoria.jpg 2-11 http://www.borebags.com/wp-content/uploads/2011/03/road-signs2.jpg 2-12 http://www.cocogeo.com/wp-content/uploads/2011/03/space_syntax_london.jpg 2-13 http://www.slickscience.com/wp-content/uploads/Physarum_polycephalum.jpg 2-14 Nakagaki, Toshiyuki; Yamada, Hiroyasu; Tóth, Ágota. 2000. Maze-Solving by an amoeboid organism’, Nature, volume 407, p. 470.

2-15 Tero, Atsushi; Takagi, Seiji; Saigusa, Tetsu; Ito, Kentaro; Bepper, Dan; Yumiki, Kenji; Kobayashi, Ryo; Nakagaki, Toshiyuki.

2010. Rules for Biologically Inspired Adaptive Network Design’, Science, volume 327, pp. 440 2-16 Adamatzky, Andrew; Jones, Jeff. 2010. Road Planning with slime mould: If Physarum built motorways it would route M6/M74 through Newcastle’, International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, volume 20, issue 10, paper 3065, page 8.

2-17 Adamatzky, Andrew; Jones, Jeff. 2010. Road Planning with slime mould: If Physarum built motorways it would route M6/M74 through Newcastle’, International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, volume 20, issue 10, paper 3065, page 8.

CHAPTER 3: Methods Title http://www.flickr.com/photos/nkiru/2575487729/ 3-1 http://i.huffpost.com/gadgets/slideshows/195760/slide_195760_452006_huge.jpg 3-2 http://aquariumprosmn.com/wp-content/uploads/2010/01/b2Dedeler-Burak3-09.jpg 3-3 207

ILLUSTRATION CREDITS

http://www.scidacreview.org/0802/html/abms.html

CHAPTER 4: Site Lagos (Nigeria) Title http://www.ynaija.com/fashola-goes-hard-lagos-governor-signs-terminator-traffic-bill-into-law/ 4-4 Siemens. “African Green City Index: Assessing the environmental performance of Africa’s major cities.pdf” 4-6 Top Left: http://upload.wikimedia.org/wikipedia/commons/e/e0/Lagos%2C_Nigeria_57991.jpg Top Right: http://www.trthaber.com/resimler/134000/134402.jpg Middle Left: http://www.naija.fm/latest-news/property-owners-along-lagos-highways-directed-to-paint-them/ Middle Right: http://www.eveandersson.com/photo-display/large/nigeria/lagos-surulere-ikorodu-road-red-building. html Bottom: http://411board.com/wp-content/uploads/2012/07/Lagos-Government-Begins-Demolition-Of-Makoko-Slum. jpg 4-7 Top Left: http://nigerianstrategies.files.wordpress.com/2012/08/lagos-traffic-congestion1.jpeg Top Right: http://www.flickr.com/photos/nkiru/2575487729/sizes/l/ Middle Left: http://blacknetart.com/africanmetropole.html Middle Right: http://www.thelongestwayhome.com/blog/how-to-guides/online-storage-hosting-for-travelers-why-youneed-to-do-it/ Bottom: http://mtalagos.tumblr.com/image/254883044 4-9 Abiri Oluwatosin Niyi. “Vehicle Carbon dioxide (C02) Emissions within the Lagos Road Network Based on Traffic Flow. pdf.» Konsult du Logistics and Transport. Accessed October 6th 2012.

4-10 Buses and mini-buses (Danfos): http://www.channelstv.com/home/wp-content/uploads/2012/07/Lagos-Danfo.jpg Taxi, private cars: http://www.flickr.com/photos/18718768@N07/3186661852/in/photostream Motorcycles (okada): http://www.nairaland.com/495786/lagos-gives-okada-riders-fresh Railway: http://thecitizenng.com/2012/09/01/page/2/

CHAPTER 5: Network Development Title 208

ILLUSTRATION CREDITS

http://thelongestwayhome.zenfolio.com/img/v5/p584465312-4.jpg 5-19 http://spatialanalysis.co.uk/2011/02/mapping-londons-population-change-2011-2030/ 5-22 http://www.google.co.uk/imgres?num=10&um=1&hl=en&client=firefox-a&rls=org.mozilla:en-US:official&biw=1537&b ih=811&tbm=isch&tbnid=6-fUQjNm2gzXzM:&imgrefurl=http://nyc-map.blogspot.com/2012/05/nyc-subway-map-pictures.html&docid=h5RfswTNoPB9GM&imgurl=http://4.bp.blogspot.com/-SgoHehKtg4k/TbdZQKv2XLI/AAAAAAAAA9c/ WoL2MQRyD84/s1600/NYC_Subway_Map.gif&w=748&h=716&ei=jl9LULrqH8L-4QS4poGoDg&zoom=1&iact=hc&vpx= 1099&vpy=172&dur=8333&hovh=220&hovw=229&tx=148&ty=123&sig=102751878842183820961&sqi=2&page=1&t bnh=133&tbnw=139&start=0&ndsp=34&ved=1t:429,r:6,s:0,i:102 5-24 http://www.google.co.uk/imgres?hl=en&client=firefox-a&hs=P4M&sa=X&rls=org.mozilla:en-US:official&biw=1537&bi h=811&tbm=isch&prmd=imvns&tbnid=wghBMmOKyycqlM:&imgrefurl=http://www.evl.uic.edu/davidson/CurrentProjects98/ET_VisualInfo/1st_Principle.html&docid=uTRY_gPXTsit1M&imgurl=http://www.evl.uic.edu/davidson/CurrentProjects98/ET_VisualInfo/EI_TokyoMapp.40.jpg&w=536&h=510&ei=3lJLUJ_dF8yB4AS3joHICQ&zoom=1&iact=rc&dur= 387&sig=102751878842183820961&page=1&tbnh=142&tbnw=150&start=0&ndsp=28&ved=1t:429,r:4,s:0,i:85&tx=93 &ty=88 5-25 Okata, Junichiro; Murayama, Akito. 2010. Megacities: Urban Form, Governance and Sustainabilityâ&#x20AC;&#x2122;. 2011, XIV, 418, p.19 5-62 Buses and mini-buses (Danfos): http://www.channelstv.com/home/wp-content/uploads/2012/07/Lagos-Danfo.jpg Taxi, private cars: http://www.flickr.com/photos/18718768@N07/3186661852/in/photostream Motorcycles (okada): http://www.nairaland.com/495786/lagos-gives-okada-riders-fresh Railway: http://thecitizenng.com/2012/09/01/page/2/

CHAPTER 6: Regional Implications Title http://farm1.staticflickr.com/126/329062128_1423b2d787_z.jpg?zz=1 6-5 http://www.prorail.nl/SiteCollectionDocuments/Publiek/Doc/Projecten/Asd_centraal/Centraal%20station%20overzicht%20bewerkt.jpg 6-7 http://www.bing.com/maps/ 6-10 http://japanesense.files.wordpress.com/2012/09/img_7801-rectfish.jpg 6-11 http://www.aedas.com/Content/images/pageimages/West-Kowloon-Terminus-Wins-Three-Awards-NewsWest-Kow209

ILLUSTRATION CREDITS

loon-Terminus-Wins-Three-Awards-784.jpg 6-14 http://ad009cdnb.archdaily.net/wp-content/uploads/2012/07/1341933398-integration-studies-of-the-station-and-the-proposed-commercial-development-1000x707.jpg

CHAPTER 7: Urban Morphologies Title http://farm5.static.flickr.com/4123/4879340530_dd938f9ff5.jpg 7-2 http://gis.lagosstate.gov.ng/Lagis/WebPages/Map/FundyViewer.aspx 7-3 http://www.nairaland.com/914951/nigeria-tested-rapid-rise-population/3 7-4 http://gis.lagosstate.gov.ng/Lagis/WebPages/Map/FundyViewer.aspx 7-5 http://www.nanngronline.com/picture/preparation-for-the-sallah-festival--2 7-6 http://gis.lagosstate.gov.ng/Lagis/WebPages/Map/FundyViewer.aspx 7-7 http://gis.lagosstate.gov.ng/Lagis/WebPages/Map/FundyViewer.aspx 7-8 http://leadership.ng/nga/articles/12902/2012/01/10/foodstuff_traders_may_soon_increase_prices_association_ chairman_warns.html 7-22 Akin L. Mabogunje. “The Evolution and Analysis of the Retail Structure of Lagos, Nigeria.” Econmic Geography, Vol.40, No. 4 (Oct., 1964), p.304.

7-23 Akin L. Mabogunje. “The Evolution and Analysis of the Retail Structure of Lagos, Nigeria.” Econmic Geography, Vol.40, No. 4 (Oct., 1964), p.304.

7-24 Akin L. Mabogunje. “The Evolution and Analysis of the Retail Structure of Lagos, Nigeria.” Econmic Geography, Vol.40, No. 4 (Oct., 1964), p.304.

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ILLUSTRATION CREDITS

7-25 Akin L. Mabogunje. “The Evolution and Analysis of the Retail Structure of Lagos, Nigeria.” Econmic Geography, Vol.40, No. 4 (Oct., 1964), p.304.

7-26 Akin L. Mabogunje. “The Evolution and Analysis of the Retail Structure of Lagos, Nigeria.” Econmic Geography, Vol.40, No. 4 (Oct., 1964), p.304.

CHAPTER 8: Conclusions 8-1 maps.google.com 8-2 Left: http://shalleecutler.files.wordpress.com/2010/04/109_0924.jpg Right: http://flamingo-africa.com/flamingo-at-work/ 8-3 Left: http://www.worldcoffeenews.com/3745/italian-coffee-company-sees-opportunities-in-chinese-market/ Right: http://www.hotelsawasdee.com/travel-event/wp-content/uploads/2012/11/download-1.jpg

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ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES COVERSHEET FOR SUBMISSION 2012-2013

PROGRAMME: Emergent Technologies and Design TERM: 5th STUDENT NAME(S): Javier A. Cardós Elena Dennis Goff Mary Polites SUBMISSION TITLE:

Adaptive Flux Morphologies

COURSE TITLE:

Emergent Technologies MArch

COURSE TUTOR:

Mike Weinstock and George Jeronimidis

SUBMISSION DATE: DECLARATION: “I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.”

Signature of Student(s):

Javier A. Cardós Elena

Dennis Goff

Mary Polites