DUNES | Dynamic Urban Nodes Emotion Simulator - A support tool for people-centered urban design

DU NE S DYNAMIC URBAN NODES EMOTION SIMULATOR

SURAYYN UTHAYA SELVAN Areti Markopoulou Thesis Studio Master in Advanced Architecture 02

DUNES

Master in Advanced Architecture 02 2018 / 2020

DYNAMIC URBAN NODES EMOTION SIMULATOR An investigation of the quantification of citizens’ emotions using emotional analytics to support the planning and design of dynamic urban environments through the development of a simulation tool

author Surayyn Uthaya Selvan thesis advisor Areti Markopoulou faculty assistants Nikol Kirova Eduardo Chamorro Martin

Thesis presented to obtain the qualification of Master Degree from the Institute for Advanced Architecture of Catalonia

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DUNES

Barcelona September 2020

DUNES | Abstract

abstract

FIGURE 1: Conceptual visualization of the emotion heatmap of Parc de la Ciutadella in Barcelona, Spain Source: Surayyn Selvan (2020)

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abstract

In designing and planning urban environments that respond to the impacts of urbanization, it is important to implement holistic strategies that address, not only the environment and the physical well-being of citizens, but also their mental well-being.

Studies have proven that the risks of mental health disorders are higher in urban environments and are projected to rise in relation to the increasing urban population. The integration of mental health into urban interventions lies in the engagement of the perception of citizens about the urban environmental quality. However, there is a disconnect with current urban design methodologies with regards to utilizing qualitative data to inform the decision-making process.

This research proposes an integrated system of artificial intelligence processes that extracts and repurposes emotional data from texts on review-based social media platforms for the quantitative evaluation and digital simulation of emotions (safety, comfort, and health) in dynamic urban environments.

The quantification methodology is weighted on literature reviews that identify causal relationships between urban indicators (accessibility, visibility, circulation, and infrastructure) and the emotions as well as an interactive public web survey that trains an emotion classification and regression machine learning model. Through natural language processing techniques, polarity values of text data from public reviews are correlated with the results of the emotion classification model that result in geo-localized weighted emotion sentiments. Interpolated emotion heatmaps are generated to visualize areas of negative emotion sentiments. In parallel, the results of the surveytrained regression model are integrated into a digital workspace where the manipulation of urban indicators generates potential emotion landscapes alongside design and planning proposals.

Using various urban infrastructures in the city of Barcelona in Spain as experimental use case studies to validate the methodology, the Dynamic Urban Nodes Emotion Simulator (DUNES) design support tool provides decision-makers with the ability to evaluate and simulate emotions in existing or proposed urban environments for the mental well-being of citizens.

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DUNES | Abstract

KEYWORDS People-centered urban design ; Emotional analytics ; Geo-localized emotion mapping ; Responsive urban design ; Data driven urban design ; Mental health strategies ; Predictive Modeling

DUNES | Glossary Definitions and pronounciations retrieved from the Merriam-Webster online dictionary

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DYNAMIC / dī-ˈna-mik /

marked by usually continuous and productive activity or change

URBAN

/ ˈər-bən /

of, relating to, characteristic of, or constituting a city

NODE / ˈnōd /

a point at which subsidiary parts originate or center

EMOTION / i-ˈmō-shən /

a conscious mental reaction subjectively experienced

/ ˌsim-yə-ˈlā-shən /

the imitative representation of the functioning of a system or process

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DUNES | Glossary

SIMULATION

DUNES | Table of Contents

chapter

title

page

chapter 0

preface

1

chapter 1

introduction

5

Shifting urban intervention strategies to address the impacts of urbanization through holistic and people-centric design and planning

chapter 2

people, place & perception

13

Understanding the potential of utilizing user perception to analyse the quality of dynamic urban environments

chapter 3

2.1 Citizen Engagement

15

2.2 Urban Environmental Quality

17

2.3 Citizen Perception of Urban Environments

21

2.4 Volunteered Geographical Information

25

2.5 Summary

27

quantifying qualitative data

29

Extracting quantitative information from emotion-based data through artificial intelligence methodologies

chapter 4

3.1 Emotions

31

3.2 Emotional Analytics

35

3.3 Natural Language Processing

37

3.4 Machine Learning Models

39

3.5 Summary

41

DUNES

43

Defining an integrated system of processes for the evaluation and simulation of urban emotions through a design support tool

vi

4.1 Data Input

47

4.2 Data Synthesis

48

4.3 Data Evaluation

49

4.4 Data Simulation

58

chapter chapter 5

title

page

case studies

59

Applying and validating DUNES on dynamic urban infrastructures of varied typologies and scales

chapter 6

5.1 Transit Infrastructures ( Metro Stations in Barcelona )

61

5.2 Urban Parks ( Parc de la Ciutadella, Barcelona )

83

5.3 Summary

149

discussion

151

Addressing the challenges of extracting and translating qualitative data to inform design decisions

153

6.2 Data Ethics and Privacy

154

6.3 Automating Design Processes

154

conclusions

155

Highlighting the viability of integrating emotional analytics into a design support tool to create holistic cityscapes

7.1 Replicability of DUNES

158

7.2 Future Developments

158

list of figures

159

references & bibliography

163

appendices

167

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DUNES | Table of Contents

chapter 7

6.1 Bias

chapter 0

DUNES | Preface

preface

FIGURE 2: Comparison heatmap of ratings and review sentiments in Parc de la Ciutadella in Barcelona, Spain Source: Surayyn Selvan (2020)

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chapter 0

“Cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.�

- Jane Jacobs, The Death and Life of Great American Cities (1961)

The research for the Dynamic Urban Nodes Emotion Simulator (DUNES) began with the curiosity to understand the workings of the human psyche in space and how designers could potentially curate dynamic public spaces with regards to user perception.

City life has made it impossible to ignore our feelings, whether it is walking through a dark alley or sitting on an uncomfortable bench next to a busy road. As the need to design better cities becomes more important, there is a necessity to integrate design processes with people-centered data. Much like the urban fabric, the human experience is complex, multidimensional, and varies for one individual to another. The objective of this study is to identify a methodology and develop a tool that supports the design of public spaces for the collective urban experience without compromising the individual.

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DUNES | Preface

As technological innovation advances, opportunities to extract subjective data becomes much more accessible and this research project embraces that. Using a multitude of technologies such as artificial intelligence complemented by in-depth literature reviews on the topic of emotions and the urban environment, DUNES offers a workflow that is able to extract subjective data and quantify it with scientific evidence. This integrated system of processes highlights the importance of human-centered data-driven design. Acknowledging the papers and initiatives that have proven parts of the methodology were successful, the research project takes it one step further by integrating an urban simulation platform, primarily used for quantitative analysis, that is able to predict the effects of design on emotions. The results of this research proves the potential to evaluate and simulate emotion landscapes in urban environments.

DUNES | Preface

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Stepping foot into the realm of urban design and data analytics was a tremendously tireless journey but most definitely, a fulfilling one. Having been able to connect with individuals who believe in the potential of DUNES, has brought so much validation for the direction of this research and its current state in this submission. DUNES has piqued interest and will continue to develop its methodology until it is robust enough for the world to experience it. I would like to take this opportunity to express my gratitude to the people who have provided motivation and inspiration to see DUNES to its best potential.

Firstly, I would like to thank my family and friends in Malaysia for being the backbone in my architectural journey as well as providing me with all the emotional support and more. I would also like to thank my thesis advisor, Areti Markopoulou for her wisdom and knowledge in the field of architecture and urban design which gave me courage to take on risks, and with her guidance, having it pay off. I would also like to express my greatest appreciation to Nikol Kirova and Eduardo Chamorro Martin, for supporting my thesis journey with their enthusiasm and knowledge. I would like to thank Mathilde Marengo for supporting the academic documentation of this project through research and methodologies as well as providing constructive feedback throughout DUNES’ journey. I would also like to thank Gonzalo Delacamara for his wonderful insights through the lens of economics, adding rigidity to the research process. I wish to express my gratitude to Diego Pajarito Grajales for supporting DUNES with his technical knowledge on urban data analytics. I would like to thank my colleagues in the MAA02 programme for continuously inspiring me with their novel research projects and hardwork. DUNES’ identity would not have been possible without Roy Green’s creative insights in marketing and advertising. He has also provided me with an abundance of emotional support and keeping me together. Finally, I would like to thank the Institute for Advanced Architecture of Catalonia community for providing me with the resources to foster innovation and creativity.

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DUNES | Preface

Muchísimas gracias a todos!

chapter 1

DUNES | Introduction

introduction

FIGURE 3: High frequency crowd and vehicular movements in Times Square, New York City Source:

Oto

Godfrey

(2015).

Retrieved

from

https://

internationalbusinessguide.org/25-largest-consumers-marketsoutlook-2015/

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chapter 1

Shifting urban intervention strategies to address the impacts of urbanization through holistic and people-centric design and planning The Effects of Urbanization on Population Growth Since the boom of the industrial revolution in the 18th century, cities have become hubs of transformation and innovation.

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DUNES | Introduction

The perceived quality of life in the city when compared to rural environments is deemed higher. This is because of the access to resources, services and infrastructures that are able to provide opportunities to those who seek a modern and fast-paced lifestyle. However, the increasing density of cities, maintained only by the continual in-migration of rural citizens, has resulted in poor living environments (Torrey, 2004). Continuous overconsumption of natural and non-natural resources have negatively impacted the overall health of citizens as well as the quality of the global environment. With the projected rise of the urban population expected to reach 68% by 2050, bringing another 2.5 billion people into city environments, it is essential that governing policies and planning strategies be adapted to reap the benefits of urbanization by ensuring shared and inclusive access to infrastructure and social services (United Nations Department of Economic and Social Affairs, 2018).

DUNES | Introduction

Urban Intervention Strategies for Urbanization There are a great number of urban intervention strategies across a variety of scales that have been implemented to improve the city quality of living. For example, at the neighborhood scale, one of the most notable government initiatives is the Superblock Model in Barcelona, Spain. This initiative is under The Urban Mobility Plan (PMU) 2013-2018 which seeks to address Barcelona’s lack of green infrastructure, high levels of pollution, degradation of the environmental quality, as well as high rates of accidents and sedentarism. Based on the six principles established by the plan which includes, sustainable mobility, revitalization of public spaces, integration of stakeholders in the governance processes as well as the promotion of the urban social fabric and social cohesion, the Superblocks seeks to regain control of public spaces for the pedestrians by reducing the number of square meters dedicated to private vehicles. Roads are then transformed into recreational and social spaces for the locals while the circulation for motor vehicles are redirected around the planned blocks (Ajuntament de Barcelona, 2016).

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FIGURE 4: The pedestrianized Sancho de Avila street in Barcelona, Spain Source: Š Twitter_Col.SuperillaP9 Retrieved from https://www.publicspace. org/works/-/project/k081-poblenou-ssuperblock

FIGURE 5: The proposed master plan for The Great City in Chengdu, China Source: Adrian Smith + Gordon Gill Architecture (2012) Retrieved from http://smithgill.com/news/ great_city_press_release/

While the previously mentioned strategy adapts to pre-existing conditions, The Great City in Chengdu, China is a master plan proposal for a city to be built from scratch. The radical proposal

by Adrian Smith + Gordon Gill Architecture would house 80,000 people in a city that is 78 million square foot in size. The masterplan proposal was designed to limit the environmental impact of its citizens by reducing energy and water use by 18% and 58%, respectively while producing 89% less waste. As the city center would be within a 15 minute walk from the perimeter and public parks would be a 2 minute walk from residential areas, the use of private vehicles would be greatly reduced. This offers opportunities for the transportation network to prioritize public transit and dedicate half the space on road networks for non-motorized traffic (Business Insider, 2012).

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DUNES | Introduction

Scaling up into citywide strategies, The Green Network in Hamburg, Germany is an incredibly respectful initiative.

Foresighted by Fritz Schumacher and Gustav Oelsner in 1919, the axial planning of green and public open spaces that radiate from the city centre connects the countrysides, public parks and the city (Hamburg, 2020). Current developments of The Green Network take into account Schumacher and Oelsner’s ideas and adapt it to the current living conditions of the citizens. Wellconnected spaces through a network of closed walking and cycling paths that are isolated from vehicular traffic promote recreation as well as outdoor activities. Not only does this strategy activate the city, it responds to effects of climate change by absorbing water from rain or in extreme cases, flooding (Lavars, 2014).

DUNES | Introduction

The Effects of Urbanization on Mental Health These strategies showcase approaches that improve the quality of urban living by focusing primarily on the environment as well as the physical health of their citizens. In order to design more holistic cityscapes, another vital component to take into consideration is mental wellbeing. The adaptation of mental health strategies into urban policies and governance has recently emerged because of the current widespread awareness of its effects and importance. When Peen et al.

(2010) conducted a study, it was found that urban areas have a higher pooled urban prevalence rate with mental disorders, mood disorders and anxiety disorders being 37%, 39% and 21% higher, respectively, compared to rural areas. Mechelli (2020) concluded that depression rates are 20% higher in people who live in cities than those who live in rural environments.

In addition, the risk of developing psychosis is 77% higher for urbanites. Their studies also found that the longer an individual inhabits an urban environment during childhood, the higher the risk of developing mental illness into adulthood. In accordance to the Mental Health Global Action Project, the World Health Organization (2003) developed the Mental Health Policy and Service Guidance Package to address mental health issues in city governance. The report described that mental disorders account for nearly 12% of the global burden of diseases and that by 2020, it will account for nearly 15% of disability-adjusted lifeyears lost to illness. The most surprising find was that resources were severely underfunded with up to 28% of countries that do not have separate budgets for mental health creating a huge discrepancy between the burden of mental health disorders and the resources required to access these services.

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INCREASE OF MENTAL HEALTH DISORDERS IN GLOBAL BURDEN OF DISEASES - WORLD HEALTH ORGANIZATION, 2003

20% HIGHER DEPRESSION RATES IN PEOPLE WHO LIVE IN CITIES - MECHELLI, 2020

39% INCREASE OF MOOD DISORDERS IN URBAN ENVIRONMENTS THAN RURAL AREAS - PEEN ET AL., 2010

68% FIGURE 6: Summary of mental health risks statistics and population growth in cities

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INCREASE OF GLOBAL POPULATION IN URBAN AREAS BY 2050

- UNITED NATIONS, 2018

Source: The Centre for Conscious Design Retrieved from https://theccd.org/ conscious-cities/

People-Centered Initiatives as Holistic Urban Design Strategies This newfound interest calls for the fundamental right for citizens to live in holistically designed cities. While there are Smart City initiatives that focus on integrated communication technologies to provide innovative solutions for the improvement of the management and the efficiency of the urban environment (European Commission, 2019), movements that foster human-centered design and holistic city planning have started to pave their way into the urban sphere. Such an example would be Conscious Cities, initiated by architect Itai Palti and neuroscientist Professor Moshe Bar for the Manifesto of Conscious Cities in 2015 (Centre for Conscious Design, 2015). A conscious city is an environment that is created for and responds to its users through the use of technology, artificial intelligence, and most importantly, science-informed design. A conscious environment is dynamic and therefore has to be designed and planned using people-centered strategies. It is also built on research that maximizes the user potential while meeting and adapting to the users’ needs over time. Through empathic approaches that revolve around user, community and, societal needs, multidimensional strategies that support health and wellbeing can be created. With regards to urban design and mental health, the Centre for Urban Design and Mental Health (2015), is an organization that empowers all the stakeholders and disciplines involved to build inclusive cities through smarter urban design. They provide a platform that extracts insights from research on mental health and urban design in order to identify gaps. This in turn creates an interdisciplinary dialogue on mental health in cities around the world by showcasing research as well as practical and evidence-based ideas.

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DUNES | Introduction

FIGURE 7: City chapters for the Centre for Conscious Design

DUNES | Introduction

Scientific Interest As there are emerging platforms with the desire for holistic design strategies that respond to the mental well-being of citizens, the necessity for tools that are able to provide opportunities for decision-makers to inform design using subjective data is prominent. There are a multitude of research projects that investigate the role of urban design on user perceptions but none that are able to predict emotional outcomes of design decisions in an integrated workflow. The Dynamic Urban Nodes Emotion Simulator (DUNES) research project addresses the following questions:How do we create a methodology that is able to quantify subjective data in a way that is scientifically viable?

How do we create a tool with an integrated system of processes that is able to extract non-intrusive emotional data from citizens and repurpose it to evaluate and simulate emotional landscapes of design interventions?

How do we create a tool that is not only economically viable, but also accessible to all the stakeholders involved in the urban design and planning phases?

Research Aim DUNES aims to provide decision makers with the opportunity to integrate emotional data into design and planning processes through the development of a simulation tool. Besides that, the research intends to create a methodology that is able to quantitatively evaluate subjective information extracted from citizens. Most importantly, the project aspires to highlight the importance and the potential of taking into account user perception in design processes.

Hypothesis DUNES investigates the hypothesis that if emotional analytics (qualitative data) is correlated with urban metrics (quantitative data), user perception (emotions) in dynamic urban environments are able to be digitally evaluated and simulated.

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Expected Results Through the investigation of current artificial intelligence technologies and literature reviews on user perception of the urban environment, relationships can be identified between emotions and urban elements in order to map and predict the emotional landscape of case studies. This urban metrics, being quantified data, can provide numerical information on emotions resulting in a qualitative data set that is quantitative. This data can then be manipulated in a digital workspace to simulate the potential emotion landscape of the urban environment.

Methodology The research begins with an in-depth literature review on various strategies and technologies that are able to derive both quantitative and qualitative urban environmental information directly from citizens.

The following chapter is a literature review that focuses on the study of emotions and its various perspectives as well as the technologies that are able to extract subjective data for a better understanding of user perception in space.

Next, the findings of the previous chapters are analysed and critiqued to methodologically generate phases of the research project that includes the extraction, analysis and simulation of data.

This methodology, up to the data evaluation and analysis phase, is applied onto an urban scale to test the validity of the process and its results. After, it is experimented on an urban park case as a use case study. A comparative study between the existing plan and a small-scale redesign is conducted to generate visualization of the emotional outcomes in a digital workspace.

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DUNES | Introduction

Finally, the results of the research project are concluded and aspects of the methodology are discussed for future developments.

chapter 2

DUNES | People, Place & Perception

people, place & perception

FIGURE 8: Locals relaxing in an open space in an urban park in Amsterdam, Netherlands Source: Nu.nl (2020). Retrieved from https://www.nu.nl/wonen/5787408/ amsterdam-plek-gestegen-op-lijst-met-leefbaarste-steden-ter-wereld. html?redirect=1

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chapter 2

Understanding the potential of utilizing user perception to analyse the quality of dynamic urban environments The key to design cities that take into account the holistic wellbeing of their citizens is to understand people at an individual as well as the community level. Most urban policy evaluations occur at town hall meetings or public surveys that are conducted

This chapter is an analysis of existing strategies as well as methodologies that are people-centered in order to create a workflow that is well-integrated and efficient. The role of the citizen and their potential to aid in the decision-making process is studied to generate better engagement strategies.

Then, the importance of city indicators in analysing the urban environmental quality is highlighted as well. Besides that, in addition to sensor data retrieved from the urban environment, evaluation on the quality of the urban environment are studied as well. Some of which include strategies where citizens are immersed in the data collection process. Various methodologies to extract citizen perception of the urban environments are also investigated to determine the most viable method, both economically and socially. Finally, the potential of volunteered geographical information is explored in the decision making process.

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DUNES | People, Place & Perception

at key locations in the cities but these procedures are dated and are not able to keep up with the dynamic characteristics of the urban environment. More often than not, town hall meetings, while it offers the opportunity for decision-makers to have close interactions with people, are conducted at longer intervals of the planning and implementation phases whereas public surveys require prompts and motivation for citizens to interact with. Citizen feedback is important in the decision making process but requires a more accessible and frequent form of communication that is also integrated into a feedback loop so as to gain insights from design policy implementations and adapt when necessary.

DUNES | People, Place & Perception

2.1 CITIZEN ENGAGEMENT Before any urban intervention proposals are implemented, it is vital to take into consideration the reaction of the local communities. Citizen feedback is important to ensure that the decision-making process is both sustainable and well-received.

An important example to highlight is The Glories Commitment established in 20017 by the Barcelona city council in collaboration with the neighbourhood entities. Glories is a neighborhood in the edge of the district of Eixample that is strategically located and complex in its essence, with it being a transitional space rather than a meeting place. This initiative developed during the economic crisis of Spain, was criticized due to the scale and speculated impact of the project on the existing social and urban fabric. This led to the formation of architectural dialogues that involved citizen groups and local communities in order to promote architecture as well as social cohesion within the city. Projects initiated by El Globus Vermell and “We Construct the Glories that We Want� have received recognition by local authorities because of their immersive roles in shaping the future of the city’s public spaces (Ehrmann, 2015).

However, around 620 kilometres away from the city of Barcelona, into the city central of Madrid, urban regeneration initiatives received backlash because of the disregard of citizens located on the outskirts of the city. Residents outside the central circle had felt that they were abandoned by the council and accused the government of benefiting the richer areas with little attention being paid to lower-income areas. The pedestrianization of the city was conducted with such hastiness that residents in the city centre were unable to experience the benefits.

Furthermore, significant data with regards to pollution and mobility were inconclusive because of the short amount of time.

In this instance, all the stakeholders involved in urban planning processes need to be aligned to achieve a common goal that is inclusive and carefully considered (Gonzales, 2019).

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Source: Elena Guim (2015) Retrieved from https://www.toposmagazine. com/in-transition/#Photo-6%C2%A9ElenaGuim-631x440

In some cases where government funding for urban projects is lacking or unavailable, Spacehive UK (2011) is an excellent example of where citizens are able to take the reign and create independent funding initiatives. Due to the erosion of government funding for councils, annual expenditures on local civic projects have had a significant drop. In most cases, communities understand the challenges and opportunities in their environments and are motivated to beautify their neighborhoods by contributing money, time and skills. Spacehive provides the technology to integrate not only the local community but local councils as well to collaboratively fund civic projects.

The essence of citizen participation is well integrated into the core of organizations such as PlacemakingX (2019). Originating from the 1960s, the concept of placemaking stems from the motivation for citizens to collectively reimagine and reinvent public spaces as the heart of communities. They believe that by strengthening the connection between people and place, the public realm can be moulded and activated to maximize its value for the collective experience. PlacemakingX is a global network of leaders who catalyze placemaking as a way to create healthy, inclusive, and beloved communities.

It is prominent in these case studies that citizen engagement can facilitate the urban design and planning process. Citizens have a sense of belonging in the local community and are highly motivated to improve their environments by contributing their time and skills or in some cases, finances. Initiatives that expedite this motivation are people-driven and it enables a harmonious process in which all the stakeholders involved are coordinated to achieve a common target. Where citizens are disregarded, urban interventions prove to be unsuccessful resulting in negative impacts on the social and urban fabric on top of economic loss. This inefficient workflow can be avoided by ensuring that local communities need to be integrated into the early stages of design and planning proposals.

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DUNES | People, Place & Perception

FIGURE 9: Children taking part in local initiatives for The Glories Commitment

DUNES | People, Place & Perception

2.2 URBAN ENVIRONMENTAL QUALITY Urban design and planning respond to the current state of the quality of the urban environment and therefore, is a key component. City councils have evaluation models that integrate urban indicators as a system to assess the quality of living.

Urban indicators are principal components as they provide a general overview of a city to identify intra-urban variations and areas that require greater attention from policymakers.

They can be used as tools to highlight issues that need to be considered, set targets as well as enable authorities to assess the performance of urban policies. However, evidence suggests that classical economic metrics have proven to be insufficient because of the current urban dynamics. This signifies that better informed policies and development plans need to be driven by people-centered and territorialized indicators (Gomez-Alvarez et al., 2018).

Urban indicators are also able to capture trends in city management, service and creative industries, changing demographics, the urban-rural divide, governance as well as environmental impacts. These metrics need to be objective, relevant, measurable and replicable, auditable, statistically representative, comparable and standardized, flexible, potentially predictive, effective, economical, as well as sustainable over time. Subjective indicators on personal well-being would be able to provide useful insights on which values and dimensions of life are more important for citizens (Hoornweg et al., 2007).

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In a study conducted by Yilmaz, Liu, and Burley (2018), indicators such as the perimeter of immediate vegetation, area of pavement, area of humans, and openness were used in image processing.

These metrics were used as scientific measurements to create an environmental quality prediction equation that is able to evaluate environmental quality or land-use change of extensive landscapes in cities. This visual quality equation created by the researchers allowed for the generation of a map of the predicted environmental quality of the landscapes in the city of Michigan, United States.

Urban indicators demonstrate the potential to evaluate cities based on separate factors that fall under common categories.

This assessment approach allows decision-makers to identify discrepancies and outliers in the city so they are able to address these issues and form baseline targets. Indicators can also be adopted into computational processes that are able to provide numerical data to evaluate the overall quality of an environment.

FIGURE 10: 3D printed data cylinders produced from data collection walks Source: David Hunter (2016) Retrieved from http://datawalking.com/ phaseone.html

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DUNES | People, Place & Perception

Data Walking by Hunter (2016) has a more practical approach to understanding the environmental quality by engaging users in the data gathering process. The project explores the potential of walking to gather environmental data and through multiple walking journeys, a layered multidimensional dataspace can be visualized of the path taken. With emphasis on creative data gathering and experimental data visualization, various forms of technologies and tools are created to be used in order to gain insights and share knowledge about the urban landscape. Global Positioning System (GPS) technologies are used where space and time data are necessary and Arduino sensors are integrated due to its accessibility and low cost. In visualizing the data, Processing and digital fabrication technologies are used for visual experimentation to validate arguments, initiate debates, raise awareness, as well as stimulate interest and engagement.

DUNES | People, Place & Perception

Although it may seem that low-cost technologies provide inaccurate results, they can be integrated into larger processes to get better localized information. In a study conducted by Mijing (2020), a practical approach to producing high spatiotemporal resolution maps of urban air pollution is investigated by assimilating air quality data from heterogeneous data streams. Two methodologies are proposed which include an air quality model driven by an open-source atmospheric dispersion model and emission proxies from open-data sources as well as a spatial-interpolation scheme that is able to assimilate observations of varied accuracies. Retina, the project is named, was proven to provide an enhanced understanding of reference measurements.

While understanding the environmental quality is important, urban simulation platforms are able to provide decision-makers with the opportunity to predict the economic and environmental effects of urban planning decisions. Traditionally, urban simulation models and its visualizations aid regional planning agencies in evaluating transportation investment, land-use regulations, and environmental protection policies. The use of behavioral or process modelling of spatial patterns of urban economic agents and objects, which include job, population, housing and land-use, enable quantitative evaluations of population, land prices, and traffic. There are also possibilities to generate predictions of real estate development, prices, and location choices of households and firms ( Vanegas et. al., 2009).

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Source: UrbanSim (2017) Retrieved from https://urbansim.com/ urbancanvas-info

FIGURE 12: The interface for Changing Places by MIT using Lego blocks and projections Source: MIT (2004) Retrieved from http://web.mit.edu/jiw/www/ city-simulation/

In a paper synthesized by Adler (2016), the integration of big data and analytics into simulation platforms aid the improvement of democratic deficits in the planning process by integrating new tools that expand the outreach of the data input. This enables citizens that have access to the internet to voice out their opinions online. The paper also notes that there are a multitude of simulation platforms that have emerged due to technological innovations. For example, UrbanSIM, founded by Professor Paul Wadell, allows planners to understand the impacts of street design, mixed use zoning, or policies to promote urban density.

Besides that, CityScope, under the Massachusetts Institute of Technology (MIT)’s Changing Places initiative, integrates Legos with projects and visualization tools to project the impacts of shifting densities. Finally, in Participatory Chinatown from Emerson College, digital simulation through a multiplayer game engages citizens to generate urban planning priorities to guide city officials.

Through the integration of data collection processes that include high-fidelity technology or low-cost sensor data, a higher resolution the urban environment can be visualized.

By aggregating relevant data, results do not have to be compromised. Using the gathered data, information about the urban environmental quality can be assessed through simulation methods. However, these platforms primarily focus on socioeconomic and environmental analysis with no opportunities for the evaluation of personalized data.

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DUNES | People, Place & Perception

FIGURE 13: UrbanCanvas Modeller using the UrbanSim platform

DUNES | People, Place & Perception

2.3 CITIZEN PERCEPTION OF URBAN ENVIRONMENTS As stated by Gomez-Alvarez et al. (2018) and Hoornweg et al., 2007), subjective indicators from citizens can provide useful insights during the decision making process. An example of a subjective indicator would be The World Happiness Report which is a landmark survey to visualize the state of global happiness derived from the subjective well-being of citizens and how social, urban and natural environments play a role in happiness.

In the report by Helliwell et al. (2020), with regards to the social environment, risks such as ill-health, discrimination, low income, and safety in the streets largely contribute to the percentage of happiness. Connectedness and high levels of trust are also highlighted as factors that contribute to the happiness level of the urban environments. The report also states that countries with healthier environments are more likely to have a higher happiness index.

With the recent interest in extracting user perceptions as forms of data, there are projects that have been deployed and investigated. One of the pioneers in subjective evaluation of the urban environments is Happy Maps which proposes a mapping service that suggests routes that are not only the shortest paths but also emotionally pleasant. A crowdsourced platform that visualizes two street scenes, out of a hundred, requires users to vote on which image looks beautiful, quiet, and happy. The results of the votes are translated into quantitative measures of location perceptions that are fed into a graph to identify which locations are considered pleasant. The generality of the methodology was then tested using Flickr metadata to compute the proxies for crowdsourced beauty dimensions which are then evaluated by a number of participants (Quercia, Schifanella and Aiello, 2014).

FIGURE 14: The map interface for Happy Maps in Berlin, Germany showcasing the happiest and fastest routes Source: Querice, Schifanella & Aiello (2014) Retrieved from https://ideas.ted.com/theshortest-paths-to-happiness-literally/

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Source: 300000kms (2018) Retrieved from http://arturo.300000kms. net/#5

Similarly, Chatty Maps is a research project that investigates the sonic qualities of the urban environment and its effect on the human perception of space. Georeferenced social media data was used to map the urban soundscapes and are then related to the mapping of people’s emotions. The methodology included a compilation of a taxonomy of sound-related terms from various online sources. The categorization was determined by correlating the terms with georeferenced Flickr images on how often these terms appear. Through image tagging, detailed soundmaps of the city were produced and relationships between the street type and noise categories were able to be identified.

These urban sounds are then studied through the lens of people’s emotional response throughout the city using the tagged images and a word-emotion lexicon. Using social media data, a location’s expected perception is determined using the sound tags at these locations. Finally, the validity of the data is tested through soundwalks conducted by participants that were required to identify sound sources and report their subjective perceptions (Aiello et al., 2016).

In collaboration with the Cotec Foundation, Arturo by 300000kms (2018) uses artificial intelligence to visualize livable city streets through citizen participation in an online visual preference survey. The gradient boost model learns what citizens characterize as the most livable urban environments through a series of street images tagged with up to 50 urban parameters with regards to habitability such as density and proportion of land use. The results of the algorithm are open to public access and an interactive map is generated to explore the most habitable streets in Madrid, Spain. The main goals of the project are to identify which streets are most livable, what the characteristics of these streets are as well as what makes the street habitable.

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FIGURE 15: Visualization for the most livable streets on the web page for Arturo

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Emotions are also able to be mapped with regards to urban environments as proven in the research conducted by Pánek, Pászto and Marek (2016). Locations that are perceived to be unsafe in the city of Olomouc, Czech Republic are mapped using a paper-based questionnaire as well as a web-based crowdsourcing tool. The data collected was gender specific and contained time stamps to analyse safety with regards to gender and the time of day. Through spatial density analysis, local correlations and hexagonal aggregations, hot spots within the city were able to be mapped while visualizing strong correlations between the level of safety and the time of day. The objective of the proposal was to allow local authorities to develop better safety strategies for the city.

In a study conducted by Nielek, Ciastek and Kopeć (2017), technology is used to bridge the gap between accessibility and the aged-group. The paper proposes an inclusive workflow to map the emotions of older adults using Robert Plutchik’s emotion wheel. Using images of streetscapes, users are required to tag the images with emotions by placing it on the wheel. The most important finding of the study was that although technology is readily accessible and is able to provide opportunities to those who don’t necessarily have access to life and development in the city, inclusive processes for the elderly needs to be carefully considered and integrated.

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Source: Nold (2007) Retrieved from http://www.sf.biomapping. net/index.htm

FIGURE 16: Heatmap visualization of places with high intensities of fear Source: Panek, Paszto & Marek (2016) Retrieved from https://www.arcgis.com/ apps/Cascade/index.html

On a more artistic note, the San Francisco Emotion Map involved 98 participants who explored a San Francisco neighborhood using a Bio Mapping divide which recorded the wearer’s physiological response to their surroundings. Under the Southern Exposure’s series of public art and programs related to investigating artists’ strategies for exploring and mapping public spaces, this project was part of Christian Nold’s five-week residency and participatory art project. It was themed around drifting through urban space and psychogeography which is the effects of geographic environments on behavior and emotions.

The result of this project was visualized using coloured dots and annotations from the participants generating a collective attempt at painting an emotional portrait of a neighborhood (Nold, 2007).

The benefits of adopting subjective user information into the design and planning of the urban environments can be seen through the access to personalized and collective experiences.

Crowdsourcing technologies alongside survey platforms enable decision-makers to reach out to a wider target group that don’t necessarily have the confidence to contribute to the betterment of the urban environment. While providing useful insights, user perception of the environment is rarely used in city council processes due to the intangible nature of the data. Much of these projects result in data visualization techniques for inclusive communication of information using maps enables geo-spatial data to be interpreted easily.

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FIGURE 17: The San Francisco Emotion Map of the Mission District neighborhood with annotations from the participants

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2.4 VOLUNTEERED GEOGRAPHICAL INFORMATION Although subjective user information has proven to be useful in assessing the quality of life in cities, access to this data is a controversial topic. However, there are platforms in which spatial metadata can be shared voluntarily. Coined by Michael Goodchild in 2007, volunteered geographic information (VGI) was described as an explosion of interest in using the web to create, assemble, and disseminate geographic information provided voluntarily by individuals. Through VGI, various forms of crowdsourcing and geotagged content can be created and accessed through various online platforms ( Zook and Breen, 2017). With Social Media Geographic Information (SGMI) that is under the larger umbrella of VGI, data sets that express community perceptions, interests, needs, and behaviours can be accessed. This allows communities to collectively communicate their preferences to better inform design and decision making processes. This system is made easier through the public presence and ease of accessibility of application programming interfaces (API) which enables the access of metadata provided by users through keywords, location as well as time. This allows for an in-depth analysis of spatio-temporal datasets (Campagna, 2016).

FIGURE 18: Various types of Social Media platforms that offer Volunteered Geographic Information

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FIGURE 19: Topographical map from the OpenStreetMap programme TopOSM Source: Ahlzen (2009) Retrieved from https://wiki.openstreetmap. org/wiki/Applications_of_OpenStreetMap

VGI is readily accessible online by all the stakeholders involved in the urban design process through existing databases and repositories as well as APIs. Most social media platforms provide the ability for users to express their opinions publicly, allowing the information to be extracted inexpensively without intrusive methods. The dynamic nature of VGI also enables divisionmakers to get real-time feedback of existing interventions and repurpose the data for future planning proposals.

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Various departments have deployed methodologies that integrate VGI. For example, the Center of Excellence for Geospatial Information Science (CEGIS) under the United States Geological Survey (USGS) has proven that participatory mapping projects can produce data that are as accurate as professional agencies. One of their biggest applications of participatory mapping and crowdsourcing is in the crisis domain. After a natural crisis, communities contribute to online mapping on various social media platforms such as Twitter to communicate geo-spatial information about the event (Center for Excellence for Geospatial Information Science, 2020). The Department of Geographic in Penn State highlights a variety of VGI initiatives that have emerged such as the OpenStreetMap (OSM), which relies on VGI contributions to develop a free base map for the world. Another interesting application of VGI comes from citizen science initiatives that integrate species sighting reports to identify locations of animals (Department of Geography, 2020).

DUNES | People, Place & Perception

2.5 SUMMARY This chapter has highlighted the importance of integrating citizens into the urban design and planning process so as to maintain social cohesion and the dynamics of the existing urban fabric. In doing so, citizens will be motivated to actively participate in city initiatives and in some cases, invest in money, time and skills. The public realm plays an important role in the quality of living of urban environments and therefore needs to be carefully curated to the needs of the citizens.

Besides that, adopting relevant urban indicators for the assessment of the urban environmental quality is vital in the city planning process by capturing trends, setting targets and assisting decision-makers in evaluating the performance of policies. In some cases, indicators can be used to evaluate the urban environmental quality. This data can also be extracted through citizen participation by using various forms of technologies such as camera vision and machine learning or even low-cost sensor data.

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Interpolation methods for low-cost technologies can be adapted into larger schemas to derive better localized information.

However, the data extraction process is limited to urban visualization strategies or introduced into data repositories. With regards to urban simulation platforms, much of the platforms primarily focus on the predictions of the effects of proposals on economic, transportation, environmental and population data with no opportunities to evaluate data on user perception.

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Studies have shown that qualitative data can be evaluated with indicators and would provide better insights on citizen lifestyle, in addition to traditional economic matrices. A variety of urban indicators could also be used to assess the user perception of the environment such as sound and happiness. Data can be extracted non-intrusively through low-cost methodologies that integrate volunteered geographic information that provide access to emotional metadata as well as spatio-temporal data.

chapter 3

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quantifying qualitative data

FIGURE 20: Identifying emotional cues emotional deep alignment network (DAN) Source: Tautkutè & Trzcinski (2018) / TechXplore

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with

a

chapter 3

Extracting quantitative information from emotion based data through artificial intelligence methodologies With the potential of integrating qualitative data such as user perception or emotions into the design and planning workflow, it is necessary to consider viable strategies that effectively extract this information. Before doing so, understanding what constitutes this subjective data is essential. The study of human emotions is multifaceted with perspectives that differ from professionals in the field of psychology to neuroscience.

In addition, the human experience in urban environments adds to this multi-dimensionality but provides opportunities for an objective understanding. Moreover, the emergence of artificial intelligence has proven to be beneficial by providing the technology to traverse the personalized experience of the human emotion through scientific scope of works.

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This chapter highlights various approaches that seek to decipher emotions as objectively as possible. Research into specific emotions experienced by citizens in urban environments is also investigated along with the identification of potential causal relationships between the physical space and emotions.

Then, a variety of existing platforms that integrate emotional analytics for specific use cases are explored. Non-intrusive methodologies for emotional analysis using social data are also analysed to understand user reactions. Finally, the potential for the integration of machine learning models to derive perceptive information about the city and its citizens are also investigated.

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3.1 EMOTIONS As a pioneer in the field of emotion awareness, Paul Ekman spent his academic career as an influential psychologist and co-discoverer of micro expressions, contributing to the methodologies of many camera vision projects. The Atlas of Emotions, supported by the Dalai Lama, is an interactive webpage that brings awareness to emotions by understanding its triggers and the physiological responses towards it. Based on a consensus among scientists through a survey, five core emotions are determined as well as their relationships to moods, personality and psychopathology (Ekman, 2016). While the atlas was developed through scientific research, the interactivity of the website allows for an easy understanding of the processes of the emotions.

On the other hand, The Geneva Emotion wheel was created with the understanding that emotions are multi-componential and include subjective feelings, appraisal and reactions in the service of action preparation and expressions, action tendencies, and regulation. Theoretically derived and empirically tested, the wheel is separated into four quadrants with two dimensions that represent valence on the X-axis from negative to positive and control on the Y-axis from low to high. 20 discrete emotions are systematically aligned in a circle with five levels of intensity that radiate from the center which contains options for “no emotion” and “other emotion”. Users are required to check the region of the relevant emotion on the wheel of their experience (Sacharin, Schlegel, and Scherer, 2012).

FIGURE 21: Geneva emotion wheel Source: Sacharin, Schlegel & Scherer (2012) Retrieved from https://commons.wikimedia. org/wiki/File:Geneva_Emotion_Wheel_-_ English.png

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Source: Lovheim (2012) Retrieved from https://commons.wikimedia. org/wiki/File:L%C3%B6vheim_cube_of_ emotion.svg

Similarly, Robert Plutchik’s wheel of emotions was derived from the psycho-evolutionary theory that categorizes emotions into primary emotions and its responses. According to Plutchik, there are eight basic emotions in which other emotions are a combination of. These primary emotions are paired with another and are polar opposites of that pair. Much like the Geneva Emotion Wheel, he states that emotions also vary in degrees of similarity with each other and have varying degrees of intensities on their own as well (Interaction Design Foundation, 2020).

On a more neuroscientific approach, Lövheim proposed a cube of emotions that is a three dimensional model using monoamine neurotransmitters (serotonin, dopamine and noradrenaline) representing the orthogonal axes and eight basic emotions representing the extreme corners of the cube.

Relying on neurobiological correlations, the model can be associated with neuro-chemical changes in the human body. It was developed to further the understanding of human emotions, psychiatric illnesses, and the effects of psychotropic drugs. The monoamines, produced in the human brain, greatly influence mood, emotion as well as behavior (Lövheim, 2012).

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FIGURE 22: Lövheim cube of emotion

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Emotions can be evaluated with its response to a variety of external factors as well. In the paper presented by Zeile et al.

(2015), an integrated approach with human emotional response with regards to the urban environment is investigated. Urban Emotions is labeled as a novel anthropocentric approach to understand the multidimensionality and interdisciplinary approach to spatio-temporal dynamics and the interactions of the human-space framework. Findings of the paper suggest that objective and subjective measurements of human feelings and perceptions with regards to urban circumstances are able to provide fine-grained resolution of data that could potentially create more sustainable urban planning. Most importantly, with technological advances, opportunities to create new levels of integration to validate the subjectivity of the human perception through sensor data or psychophysiological measurements are prominent.

By correlating urban sensor data with the human experience and utilizing urban indicators described in Chapter 2 of this research, three core urban emotions and their relationship with urban elements are examined. The first urban emotion identified is safety due to its priority in the sustainable development of cities as well as its socio-economic impacts. With regards to psychological safety, key factors that influence safety are privacy and the control of privacy. This is directly related to the level of protection experienced as well as the ability to avoid threats. The inability for individuals to orientate themselves within the environment also contribute to the feeling of safety.

Besides that, spatial layouts that have building density and open spaces as elements also affect the level of safety. Definite boundaries, simple road networks, appropriate placements of urban furniture and sufficient lighting also have a positive influence on safety. User behaviour with regards to safety is also easily influenced by elements such as materiality, finishing, and levels of walking paths. In terms of circulation, entrances and exits located within the line of sight would aid in providing a positive sense of behavioural safety. Clear division of private and public spaces with access to natural surveillance greatly influence the safety of an urban environment (Cai and Wang, 2009). Finally, the presence of vegetation also contributes to the feeling of safety as view-obstructing vegetation are associated with increased crime rates (Mazlaghani, 2014).

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The second urban emotion identified is comfort because of its importance in the productivity, social cohesion and civic identity of a city as well as quality of life. Studies have shown that comfort in public spaces is greatly influenced by the feeling of security, pavement conditions, lighting conditions, appealing surroundings, weather, and traffic conditions. The proper regulation of urban microclimates through carefully organized vegetation also provides a sense of comfort in addition to providing shade, reducing humidity levels, and offering protection from strong winds. Urban furniture also is an integral part of a comforting experience in public spaces. Benches and other forms of rest furniture provide points of relaxation as well as the opportunity to socialize. Finally, the materiality of pavements as well as shading elements also affect the perception of comfort in an urban setting (Vukmirovic, Gavrilovic and Stojanovic, 2019).

The final urban emotion identified is health because of its role

It can be seen that all three of these urban emotions have individual influence over the other. For example, health and safety is a contributing factor to the comfort of a public environment and vice versa. Across all the emotions, common urban factors overlap and are able to be categorized into four general urban topics. Firstly, urban factors related to accessibility which include entrance and exit points and access to transportation options. Secondly, visibility is an important factor as well which comprises lighting sources, density of buildings as well as density of vegetation. Thirdly, proximity to road networks and characteristics of walking paths contribute to the topic of circulation in an urban environment. Finally, infrastructures such as urban furnitures which include rest and waste furnitures also influence he perception of these emotions.

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in coping with the negative effects of urbanization and climate change on the urban living conditions. Several factors contribute to the feeling of health such as traffic-related exposures. This includes distance to the nearest road network as well as the intensity and frequency of traffic. Besides that, accessibility to transport networks as well as urban facilities through cycling networks and public transportation also encourage citizens to practice healthy lifestyles. Also, waste and the access to waste management systems such as recycling bins as well as public schemas for green waste collection are also contributing factors. Finally, in ensuring a positive experience of health in cities, the safety and maintenance of strategic recreational and green spaces with large surface areas and availability must be considered (World Health Organization, 2010).

DUNES | Quantifying Qualitative Data

3.2 EMOTIONAL ANALYTICS Although emotional analytics is a very recent field of study, the growth of technological innovation has benefited the processes of understanding human emotions and perceptions. Currently in the market, there are an increasing number of platforms that offer emotional data to organizations to improve their products and services. For example, Realeyes (2019) is a platform that extracts subconscious emotional responses of users as they view video contents. The attention span, emotions, and sentiments of the audience are measured using webcam feedback and can be complemented by survey questions. External physiological reactions such as eye movements, blinking, yawning, and distracted head movements are classified into behavioral cues. Using the Matthews Correlation Coefficient to assess the performance of their classifiers, they have achieved an 86% precision in detecting attentiveness. Results are then curated onto an online dashboard where users can view the demographics as well as the user experience throughout the duration of the video content. Organizations are able to use this data to improve their customer experiences and optimize their media buying decisions.

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FIGURE 23: Emotional tracking and facial recognition through computer vision technology by Realeyes Source: Levine (2016) Retrieved from https://martechtoday. com/marketers-welcome-to-the-world-ofemotional-analytics-159152

FIGURE 24: Driver monitoring systems for cars using Affectiva’s Automotive AI Source: Affectiva (2018) Retrieved from https://www.affectiva.com/ product/affectiva-automotive-ai-for-drivermonitoring-solutions/

Last but not the least, the Emotion Research Lab (2018) which uses facial recognition and eye tracking technology to provide customer response metrics with regards to emotion detection, emotional metrics, moods, secondary emotions, face detection, face tracking and head orientation, facial features, gender, age, ethnicity, eye tracking, sentiments, traffic as well as attention span.The Facial Action Coding System (FACS), initiated by Paul Ekman, real-time detection and decoding of facial muscles are able to be executed and categorized into specific emotions. Use cases for the platform include feedback from advertising and marketing strategies, user experience, product testing and even artificial empathy which is able to embed everyday objects with a video camera and capture user responses towards it.

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Using facial recognition technology as well, Affectiva is able to extract facial and focal expressions to get a more comprehensive and accurate understanding of emotional states. Video recordings of participants during activities such as watching videos or driving a car are gathered to analyse market research patterns. They are required to give permissions to record their faces. Participants have their data anonymously and spontaneously collected in their natural environments (Zijderveld, 2017). The complexities of the data extraction calls for technologies such as computer vision, speech science, and deep learning architectures. Convolutional neural networks and recurrent neural networks allow the system to function with higher accuracies. Using a human-centric approach to artificial intelligence, the potential to improve the way humans work, live, and interact become much more focused (Zijderveld, 2019).

DUNES | Quantifying Qualitative Data

3.3 NATURAL LANGUAGE PROCESSING While facial recognition and speech recognition prove to be innovative solutions in understanding emotions, they are considered intrusive although there are options to have the data anonymously extracted. There are non-intrusive methods of artificial intelligence that have the same limitations but prove to be as effective. Natural language processing techniques (NLP) such as text sentiment analysis are one of the most developed methods of emotional artificial intelligence with a high demand in various business and public sectors. With accessible databases to complement the process, new databases can be formed through data scraping of social media platforms. Some of the limitations that are prominent are the inability to extract double meanings, jokes and innuendos but most importantly, regional variations of language and non-native speech structures (Vartanova, 2019).

The flexibility of NLP models allow it to be adapted and applied in various use cases. In an urban context, a study conducted by Sayah and Shanbel (2018) proved that social media data can be used to provide real-time citizen feedback to inform smart city strategies. Extensive literature reviews were conducted to design an e-participation platform that is able to conduct text analysis and NLP techniques to identify opinions and emotions.

This platform not only complies to data protection regulations but it also maintains communication between citizen and city planners. Twitter API was used to extract the data while visualization tools were used to stream, pre-process and visualize the data for the target audience. Finally, the research also takes into account socio-cultural diversity and is used as an informing tool with offline methods of participation.

FIGURE 25: Map of parking availability plotted with the negatively rated areas of the city Source: Chen et al. (2017) Retrieved fromhttp://papers.cumincad.org/ data/works/att/cf2017_101.pdf

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Source: Chen et al. (2017) Retrieved fromhttp://papers.cumincad.org/ data/works/att/cf2017_101.pdf

Another form of social media that NLP can be applied to is review-based platforms. Using Trip-Advisor, Chen et al. (2017) investigated the validation of NLP techniques on social media platforms as a tool complementary to traditional survey methodologies. Results proved that distinct geographic regions in the case study city of Andorra showcased amenities that were reviewed as uniformly positive or negative. Correlations between the text analysis and existing geographical typologies were significant such as the negative reviews of parking availability with land use data showed that the parking shortages were related to traffic congestion issues. This methodology is cost effective at highlighting urban issues at a microscopic scale. The research paper proved that by extracting spontaneous reviews, patterns of sentiments with regards to urban issues can provide data-driven insights to urban designers and planners.

Where existing databases for NLP aren’t enough, there are opportunities to create specifically curated ones for the context of a research. In the research paper conducted by Mohammad and Turney (2012), emotion analysis was investigated beyond text polarity through crowdsourcing. In turn, generating a large, high quality, word-emotion and word-polarity association lexicon quickly and inexpensively. During the data extraction phase, the question prompts presented to the users had to be clear and brief to avoid misinterpretation of the classification. The lexicon managed to draw entries for more than 10,000 word-sense pairs via the Amazon Mechanical Turk crowdsourcing platform. The data set collected was then validated with existing gold standard data and proved to be of high quality.

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FIGURE 26: Sentiment analysis for the keyword street in Spanish

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3.4 MACHINE LEARNING MODELS In dealing with large quantities of data, machine learning provides the ability for data scientists to analyse big data through mathematical models. With reference to Smart Cities, data mining and machine learning techniques enable the support of its ideology. Predictive analysis is one of the most frequently deployed machine learning models due to its flexibility in application of various scenarios while providing reliable and easily interpreted results. Some of the main sectors that integrated this technology are smart mobility and smart environments with little developments on smart people, smart governance, and smart economies which are also important factors to consider in city development (Souza et al., 2019).

While largely used in statistical applications, the evolution of machine learning has allowed its application into design and planning fields, and more recently, analysis of subjective datasets. Such an example would be in a paper studied by Liu et al. (2017) that presented a computer vision methodology that contains three machine learning models for the large-scale and automatic evaluation of urban environmental quality. Using street images, the visual quality of street facades as well as the continuity of street walls were the focus of analysis. Deep convolutional networks were utilized to achieve a mean squared error of 0.614 for the visual quality on a rating scale from one to four and an accuracy of 75% for visual continuity. The results were then validated through a survey which showcased moderate to high correlations between the machine learning model and the public rating. As with most machine learning models, the biggest limitation of the research was the size of the data set that did not allow the algorithm to perform in its maximum potential.

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While the previous research used survey data as a validation tool, an investigation by Dubey et al. (2016) combines a visual preference survey into the methodology. The integration of computer vision and convolutional neural networks allows the quantification of the perception of the urban environments with regards to six perceptual attributes:- safe, lively, boring, wealthy, depressing, and beautiful. 81,630 volunteers contributed a crowdsourced dataset of over 1,000,000 pairwise comparisons of over 100,000 street images from 56 cities. The machine learning model utilizes a joint classification and ranking loss model to predict human judgement of the pairwise image comparisons. This was conducted through a gaming interface that integrated a visual preference survey with text prompts.

Innovation has allowed for the seamless integration of machine learning methodologies into digital workspaces. This contribution offers the opportunities for designers and planners to operate across a multitude of platforms while visualizing the result on their data in a digital environment. For example, Owl which is a small-scale machine learning oriented library for data processing with a plugin available for Grasshopper 3D. Currently, the plugin includes a regression model with neural networks, backpropogation, as well as 6D k-means clustering.

The availability of Owl allows for a new data type used in machine learning networks, Tensors, to be integrated into digital design processes. Not only are the network functions able to be visualized but the results can be imported or exported to external deep-learning methods through IDX files (Zwierzycki, 2017).

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With regards to spatio-temporal attributes, linear models perform better in the identification process as concluded by Matijosaitiene, McDowald and Juneja (2019). The research analyzed spatial and time patterns of theft in Manhattan, New York and was able to reveal factors that contributed to thefts of motor vehicles. The data set employed included information with regards to longitude and latitude, crime type, date and time of crime committed as well as the date and time when the crime was registered by authorities. The machine learning model, with an accuracy of 77% was able to identify elements that contributed to higher theft rates such as high numbers of subway entrances, graffiti and restaurants. The results were visualized on a map of Manhattan documenting the theft rates as well as hotpots of crimes with regards to the model confidence level.

DUNES | Quantifying Qualitative Data

3.5 SUMMARY This chapter has highlighted the potential of utilizing qualitative data through quantitative methods of extraction and technologies. Focusing specifically on emotions, researchers have proven that although emotions are based on subjective qualities. Perspectives through the scope of psychology, neuroscience and urban analysis enable emotions to be assessed as objectively as possible. The core methodology that enables this is the correlation of emotions to various factors such as psychology theories, neurochemical processes or urban circumstances.

Taking into consideration the human experience and correlating it with sensor data and urban indicators, three core urban emotions:- Safety, Comfort and Health are established as well as the factors that influence the user perception of these emotions.

Analysis has proven that all three emotions are interrelated and influence each other. Factors that contribute to the perception of these emotions can also be categorized into four urban topics:Accessibility, Visibility, Circulation and Infrastructure.

Besides that, emotional analytic platforms that integrated various forms of technology prove to be efficient and reliable in communicating emotions. Although the user participation is well-received, these methodologies integrate intrusive processes such as facial and speech recognition. The use cases of these platforms are primarily focused on product services, market patterns and user experience.

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While there are emotional artificial intelligence platforms that require users to give permission to access their physiological responses, natural language processing offers the same efficiency and limitations through an inexpensive analysis of text data. For a more focused extraction of information, social media platforms can be used because it contains userexperience based data. With regards to review-based platforms, text reviews are readily geolocalized and contain location ratings based on grades. Existing databases to analyse text allow a robust workflow and if the content of these databases prove to be insufficient, data mining techniques to extract social data and crowdsourced data can be utilized. This enables the creation of personalized databases with relevance to the analysis.

Finally, machine learning models allow for the processing of large datasets which are able to include subjective information.

Although primarily used in statistical analysis, one of the most common forms of machine learning, predictive modeling, can be applied to urban analysis as well. Through the use of street image tagging and public web visual preference surveys,

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quantitative analysis of subjective urban data can be analysed.

The developments of new technologies also allow the integration of machine learning methodologies into digital workspaces, further easing the design and planning process.

chapter 4

DUNES

DUNE S

FIGURE 27: Comparison heatmap of ratings and review sentiments in Parc de la Ciutadella in Barcelona, Spain Source: Surayyn Selvan (2020)

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chapter 4

Defining an integrated system of processes for the evaluation and simulation of urban emotions through a design support tool Based on Chapter 2, it can be concluded that the utilization of subjective urban indicators can prove to be useful in evaluating the urban environmental quality and there is potential in extracting accurate information from citizens through lowcost technologies with social media geographical information.

Emotions can also be extracted through crowdsourcing and visual preference surveys with regards to the city scape. The case studies also showcase the potential of correlating user perceptions with urban elements and deriving insights from the results. However, there are no platforms that allow decisionmakers to predict the emotional outcome of their proposals.

Based on Chapter 3, emotions can be evaluated through user response of external factors which supports the findings in Chapter 2. The analysis of urban perceptions resulted in three urban emotions:- safety, comfort and health. These urban indicators that influence these emotions are summarized and categorized into four topics:- accessibility, visibility, circulation and infrastructure. Non-intrusive and inexpensive methods such as natural language processing techniques enable qualitative information to be extracted from various social media platforms.

However, this research will focus on the viability of reviewbased social media platforms. Finally, the presence of machine learning models integrated into a digital modelling workspace allows the data to be visualized and manipulated.

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DUNES

This chapter describes the methodology established by DUNES, with reference to the previous chapters, that explores the hypothesis that if emotional analytics (qualitative data) is correlated with urban indicators (quantitative data), sentiments in dynamic urban environments are able to be digitally evaluated and simulated. The workflow is divided into four phases beginning with the Data Input of the text reviews from the review-based social media platforms followed by a preliminary analysis and visualization of the text using sentiment analysis and topic modelling in the Data Synthesis phase. The third phase, Data Evaluation demonstrates the correlation of the chosen emotions and the urban indicators while the final phase, Data Simulation, integrates the information into a digital workspace to be manipulated.

MOST FREQUENT WORDS

SENTIMENT ANALYSIS

AVERAGE TEXT POLARITY

TEXT COMMENTS DATA MINING

REVIEW-BASED SOCIAL MEDIA PLATFORM(S)

DUNES

TOPIC MODELING

DUNES INTERACTIVE SURVEY

RATINGS

TEXT COMMENTS

URBAN INDICATOR VALUES FOR POSITIVE SENTIMENTS URBAN INDICATOR VALUES FOR NEGATIVE SENTIMENTS

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AVERAGE POLARITY HEATMAP

AVERAGE RATING HEATMAP

IDENTIFY AREA OF INTEREST

CONDUCT COMPARISON ANALYSIS

AVERAGE EMOTION(S) HEATMAP

MANIPULATION OF URBAN INDICATORS

GEOLOCALIZED WORDCLOUDS

PREDICTED AVERAGE POLARITY PREDICTED AVERAGE SAFETY SENTIMENTS PREDICTED AVERAGE COMFORT SENTIMENTS PREDICTED AVERAGE HEALTH SENTIMENTS PREDICTED AVERAGE OVERALL SENTIMENTS

WEIGHT OF SAFETY EMOTION CLASSIFICATION

WEIGHT OF COMFORT

TRAINED ALGORITHM

WEIGHT OF HEALTH

FIGURE 28: Simplified diagram of the DUNES workflow

POSITIVE VALUES NEGATIVE VALUES

TRAINED ALGORITHM

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REGRESSION ANALYSIS

DUNES

4.1 DATA INPUT Review-based social media platforms are utilized in the Data Input phase to investigate its potential in deriving metadata with regards to emotions in addition to geographical data.

Through these platforms, the user experience data is based on the geo-localized points with some deviations. In addition, these platforms offer public rating tools to grade the geo-location from low to high. Finally, the review interface integrates autodetection for spam data which cleans the data set leaving the most relevant reviews.

Information pertaining to the text review, rating and date are extracted from the geo-localized points of review and imported into an Excel spreadsheet that is divided according to the names of the geo-localized points of review. Personal information such as the username, location of the user, and the number of reviews contributed are not vital to this methodology and are not included in the analysis. Once the data has been organized, the text data is pre-processed to ensure all the words are normalized by transforming all the sentences into lowercase format as well as removing all punctuation, numbers, emojis, stop words as well as non-english words. Then, the text data is tokenized and turned into a document-term matrix to be used for the Data Synthesis phase.

The products of this phase will result in pre-processed text data that includes the rating and date of review. Based on the number of review-based platforms, the data could be aggregated or isolated depending on the analysis format as well as the similarities of the chosen platforms.

The urban emotions selected to be analysed for this methodology are safety, comfort and health because of its relevance to the city scape. The urban indicators synthesized from the literature reviews resulted in the following categories:- accessibility, visibility, circulation and infrastructure. The correlation between these urban emotions and their respective urban indicators will be the basis of this experimental methodology.

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4.2 DATA SYNTHESIS In the Data Synthesis phase, natural language processing (NLP) techniques are applied on the text data to efficiently extract citizen perceptions of the urban environment. Using the TextBlob library, which is a Python driven library for the simple processing of textual data, sentiment analysis and topic modelling is conducted on the tokenized text data. Through sentiment analysis property for TextBlob, a namedtuple is returned in the form of Sentiment(polarity,subjectivity) whereby the polarity score and the subjectivity score is a float number within the range of -1.0 to 1.0 and 0.0 to 1.0, respectively. The polarity is defined as a negative or positive “impression� of the text data whereas the subjectivity is defined on how much of the text is an opinion or based on factual data. The sentiment values for each review are calculated and the information is appended onto the Excel spreadsheet.

Then, through the gensim library, Latent Dirichlet Allocation (LDA) is conducted on the text data to execute the topic modelling algorithm. Based on the number of topics specified in the algorithm, the document-term matrix is passed through and the results are interpreted to find common topics for the geo-localized points of review. Another form of analysis that is conducted is the Most Frequent Word using the Counter property from the collections library. By categorizing the text data into negative and positive sentiments, the most frequent words are identified for each geo-localized point of review.

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DUNES

The average polarity and average subjectivity of the text data is calculated and integrated into the Grasshopper 3D workspace together with the average rating from the original text data. The result of this process generates heatmaps of the average rating and average sentiments of the geo-localized points of review. In addition, the topic modelling and most frequent words is also visualized on the heatmap for preliminary analysis.

DUNES

4.3 DATA EVALUATION In the Data Evaluation phase, the urban emotions and the urban indicators are the core of the analysis. The emotions that are going to be analysed are SAFETY, COMFORT and HEALTH based on the urban indicator categories of accessibility, visibility, circulation and infrastructure. These categories are further broken down into specific urban elements which are as follows:ACCESSIBILITY Entrance and Exit Points Transit Points VISIBILITY Vegetation Lighting CIRCULATION Walking Path Vehicle Path Frequency of Movement / People INFRASTRUCTURE Rest Furniture Waste Furniture This phase is divided into two sections which consist of an interactive public web survey proposed by the research as well as an emotional evaluation of the extracted text data based on the results of the survey.

FIGURE 29: Concept landing page for the DUNES Interactive Survey

49

DUNES INTERACTIVE WEB SURVEY The core of the research lies in the methodology for the public web survey that is able to identify the user perception of the urban indicators with relation to negative and positive sentiments of the three urban emotions. The polarity of the emotions were standardized in accordance with the TextBlob analysis to ensure normalization of the data. Two question typologies were created and themed according to the urban emotions. The question orders were randomized and the prompts were paraphrased to represent the three main emotions and their respective sentiments in order to retrieve as much of an unbiased response as possible. Based on the attention span of a human with an average of 75 seconds spent on one question, there were a total number of 21 questions in the survey with an average time spent of around 30 minutes. 12 questions were visual preference selection and the remaining 9 were scale grading.

With the visual preference questions, respondents are required to assess visual information with predetermined positive and negative sentiments whereas the scale grading questions give flexibility to the respondents to determine the sentiments based on the average grading of the results. The interactive survey is complemented by urban environment images embedded with urban indicator values extracted from various methodologies.

DUNES D Y N A M I C U R B A N N O D E S E M O T I O N S I M U L AT O R D Y N A M I C U R B A N N O D E S E M O T I O N S I M U L AT O R

INTE RAC TIVE S URVE Y

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DUNES

#SENDDUNES

DUNES

As with most visual preference surveys, the visual preference question typology required respondents to select an image out of two that is best suited to the question prompt. To ensure equal weightage of the sentiment polarity, half of the questions were positive prompts and the other half were negative. For the positive prompts, in addition to selecting the image, respondents were required to describe their image choice using keywords.

On the other hand, for the negative prompts, respondents were required to provide suggestions to enhance their image choice using keywords. The results of this question typology is a chosen image embedded with urban indicator values related to positive and negative sentiments as well as keywords with relation to the urban emotions. The prompts for the visual preference questions are as follows:SAFETY Positive prompt: “Which one of these areas would you feel better alone?”

Keywords prompt: “Why would you feel better alone in the area you chose?”

Negative prompt: “Which one of these areas would you AVOID walking alone?”

Keywords prompt: “Why would encourage you to walk alone in the area you chose?”

COMFORT Positive prompt: “Which one of these areas would you PREFER to rest in?”

Keywords prompt: “Why would you rest in the area you chose?”

Negative prompt: “Which one of these areas would you NOT rest in?”

Keywords prompt: “Why would encourage you to rest in the area you chose?”

HEALTH Positive prompt: “Which one of these areas would you PREFER to exercise in?”

Keywords prompt: “Why would you exercise in the area you chose?”

Negative prompt: “Which one of these areas would you AVOID exercising in?”

Keywords prompt: “Why would encourage you to exercise in the area you chose?”

Which one of these areas would you PREFER to exercise in?

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NE NT

0.25 u 0.25 u

NE NT

1u 1u

VG LL

34.0068 % 117.79 lux

VG LL

8.18 % 145 lux

WP 2 m FP 1 u VP 0.15 u

WP 3 m FP 15 u VP 1 u

RF 4 u WF 1 u

RF 0 u WF 0 u

26

5

With the scale grading typology, respondents were required to rate an image from a scale of 1 (least likely) to 5 (most likely) based on how likely the respondent would execute the prompt.

The sentiment of each of these questions are determined by the average likeliness of the responses whereby 1 is negative and 5 is positive. The results of this question typology include the overall majority grade of likeliness for the image that is embedded with the urban indicator values. The prompts for the scale grading questions are as follows:-

SAFETY Prompt: “How likely would you walk alone in this area?”

COMFORT Prompt: “How likely would you walk alone in this area?”

HEALTH Prompt: “How likely would you walk alone in this area?”

How likely would you walk alone in this area?

NE NT

0.25 u 0.25 u

VG LL

34.0068 % 117.79 lux

WP 2 m FP 1 u VP 0.15 u RF 4 u WF 1 u

26 1

5

(left) FIGURE 30: Example visualization of the visual preference question with a positive health prompt and the urban indicator values embedded in the image

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DUNES

(top) FIGURE 31: Example visualization of the scale grading question with a positive health prompt and the urban indicator values embedded in the image

DUNES

As the interactive web survey was deployed for the urban park case study, various images of different urban park settings and environments were selected from Google street views as well as royalty-free images based on visual variations of the urban indicators (refer to Figure 32). The number of images selected were determined by the number of images required by each question typology whereby the visual preference question typology required 2 images per question and the scale grading question typology required 1 image per question. These images were then labeled with number from 1 to 33 and each image was tagged with numerical information about the urban indicators using the following methodologies:ACCESSIBILITY (NE) Entrance and Exit Points

Numerical Value: Distance to the nearest point (unit)

Methodology: Visual comparison of images (NT) Transit Points

Numerical Value: Distance to the nearest point (unit)

Methodology: Visual comparison of images VISIBILITY (VG) Vegetation

Numerical Value: Percentage of green and brown pixels (%)

Methodology: OpenCV scripting to detect colour (LL) Lighting

Numerical Value: Perceived brightness (unit)

Methodology: OpenCV scripting to calculate perceived brightness CIRCULATION (WP) Walking Path

Numerical Value: Size of path (m)

Methodology: Visual comparison of scale (FM) Frequency of Movement / People

Numerical Value: Number of people (unit)

Methodology: Manual count of people (VP) Vehicle Path

Numerical Value: Distance to the nearest road (unit)

Methodology: Visual comparison of images INFRASTRUCTURE (RF) Rest Furniture

Numerical Value: Number of rest/leisure furniture (unit)

Methodology: Manual count of rest/leisure furniture (WF) Waste Furniture

Numerical Value: Number of waste furniture (unit)

Methodology: Manual count of waste furniture

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After extracting the values of the urban indicators, the images were streamed and clustered to the questions according to common and slightly varied urban indicator criteria with regards to the urban emotions.

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The evaluation of the visual preference question typology is conducted by extracting the image and its respective urban indicator values that have the highest number of responses.

These values are then directly correlated with the sentiment and urban emotion assigned to the question. In addition, the key words extracted are grouped according to the urban emotions assigned to the question. With regards to the scale grading question typology, the evaluation is conducted by identifying the highest number of responses for the likeliness within a range.

From 1 to 3, the image is automatically labelled as a negative sentiment and from 3 to 5, the image is automatically labelled as a positive sentiment.

DUNES

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FIGURE 32: List of the images of various urban park environments with their name tags

DUNES

EMOTIONAL EVALUATION OF EXTRACTED TEXT DATA Once the survey has been deployed and the responses are substantial, the results of the emotion classification machine learning model can be applied to the extracted text data. The weighted values for safety, comfort and health are appended into the Excel spreadsheet and the main urban emotion and its corresponding value are identified for each review. Then, the following calculation is performed to determine the urban emotion sentiment for each review and the results are also appended to the Excel spreadsheet:urban emotion weighted value x polarity value of text = urban emotion sentiment The results of this calculation provides a higher resolution of emotion analysis of the text data in addition to the sentiment analysis. The average results of the sentiment of safety, comfort, and health are integrated into the Grasshopper 3D workspace to generate an emotion heatmap.

With the creation of heatmaps for the average rating, average polarity, average urban emotion sentiment value, and the average overall urban sentiment value from the geo-localized points of review, a layered analysis is conducted to determine areas of interest that require design and planning intervention.

With the chosen area of interest, the urban indicators are collected from various sources using different methods and are integrated into the Grashopper 3D workspace.

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4.4 DATA SIMULATION With the urban indicators integrated into the digital workspace and the area of interest chosen, the Data Simulation phase is ready to be executed. Firstly, values of the urban indicators need to be extracted within the area of interest and normalized into 0 to 1. The same needs to be applied to the urban indicator values for the results of the survey. After setting up the digital environment, the simulation Grasshopper script is activated by drawing an analysis boundary around the area of interest. Then, the trained regression analysis model is launched and values related to the average sentiment of the area, the percentage of positive and negative weightage of the selected emotion as well as the average sentiment of the nearest points of review with regards to safety, comfort, health, and the overall emotions is computed. A grasshopper slider allows the analysis to interchange between the different urban emotions in order to view their respective values.

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The design and planning urban intervention of the area of interest can be informed by the topic modelling results of the geo-localized reviews, image based analysis, or the highest negative or lowest positive percentage of the urban emotion.

The urban indicators can then be manipulated to achieve the desired emotion value.

chapter 5

DUNES | Case Studies

case studies

FIGURE 33: Artistic rendering of the average ratings in Parc de la Ciutadella in Barcelona, Spain Source: Surayyn Selvan (2020)

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chapter 5

Applying and validating DUNES on dynamic urban infrastructures of varied typologies and scales This chapter describes the process in implementing the methodology established in the previous chapter on two separate case studies and documenting its results. Both case studies are located in Barcelona, Spain because of the accessibility

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to information as well as their physical locations. These case studies were also selected due to the dynamic nature of their infrastructures that house a continuous flow of people. The first case study is on a city-wide scale focusing specifically on transit infrastructures. The objective was to test the credibility of using review-based platforms as a data source from the Data Input phase to the Data Synthesis phase. In the second case study, a smaller scale of an urban park was selected in order to validate the complete methodology.

DUNES | Case Studies

N

Barcelona Metro and Tramway Network Map Barcelona, Spain not to any scale

FIGURE 34: Barcelona Metro and Tramway Network Map

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case study 1 transit infrastructures

DUNES | Case Studies

5.1 TRANSIT INFRASTRUCTURES With a focus on the transit network and its infrastructures in Barcelona, the methodology was applied to test the credibility of the review-based social media platform, Google Reviews. This platform was selected because it is not a common source of analysis and the data needed to be validated in order to move forward with the research. In the context of this case study, the natural language processing techniques were applied to investigate the insights that were derived from the results using the survey results from a report by the Transports Metropolitans de Barcelona (2018) on the metro services as a basis. In the report, it was stated that the lowest rated parameters were comfort and security (refer to Table 1).

Survey Parameters

2017

2018

Offers

7.75

7.89

Reliability

7.37

7.45

Comfort

6.75

6.83

Information

7.38

7.55

Security

6.86

6.85

Accesibility

7.51

7.68

Attention

6.71

7.59

TOTAL AVERAGE

7.59

7.73

TABLE 1: Results from the public survey conducted by Transport Metropolitans de Barcelona for the years 2017 & 2018

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DATA INPUT The Barcelona Metro network is a 121.4km long line with 158 stations. The number of reviews as well as the public rating on Google Reviews was logged. The data was then reviewed and preliminary analysis on the data collection was conducted. The initial findings were that responses were higher in stations that had interchanges, significant landmarks as well as in touristic neighborhoods (refer to Figure 35). On average, the public rating was 3.9 stars (refer to Figure 36).

Then a calculation for the rating per input was performed to determine the stations that were to be analysed. From the results of the calculations, the top three metro stations with the lowest and highest rating per input for each line was determined. From this list, the metro station with the highest and lowest rating per input was selected as case studies bringing a total of 24 metro stations to analyse (refer to Figure 37). This elimination process was essential because the number of locations were too large for analysis.

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From the filtered metro stations, reviews were extracted from Google Reviews for each of these stations amounting to a total of 2,515 responses, 1,355 of which had text comments (refer to Figure 38). The first issue experienced during the data extraction was the limitation imposed by the Google Maps API request that only allows for the top five most relevant reviews per location. The data mining method had to be reevaluated and the Data Miner widget for Google Chrome was tested. Although the data mining process took much longer, as all the individual stations needed to be pulled up, all the information was able to be extracted (refer to Appendix 3).

DUNES | Case Studies

N

Number of Responses Visualization Map Metro Stations, Barcelona, Spain not to any scale

FIGURE 35: Number of responses visualization map

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N

Average Rating Visualization Map Metro Stations, Barcelona, Spain not to any scale

FIGURE 36: Average rating visualization map

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1 STAR

3 STARS

5 STARS

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GOOGLE RATINGS

DUNES | Case Studies

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Filtered Stations Map Metro Stations, Barcelona, Spain not to any scale

FIGURE 37: Filtered stations map

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11 14

1 Hospital de Bellvitge

11 Pep Ventura

21 Onze de Setembre

2 Torrassa

12 Roquetes

22 La Salut

3 Plaza Espanya

13 Barceloneta

23 Fira

4 Plaza Catalunya

14 Selva de Mar

24 Aeroport Terminal 1

5 La Sagrera

15 Collblanc

6 Torras i Bages

16 Vilapicina

LINE 1

LINE 6

7 Fondo

17 Muntaner

LINE 2

LINE 7

8 Tetuan

18 El Putxet

LINE 3

LINE 8

9 Sagrada Familia

19 Europa | Fira

LINE 4

LINE 9

10 Bac de Roda

20 Moli Nou

LINE 5

LINE 10

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DUNES | Case Studies

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Number of Ratings Visualization for Filtered Stations Metro Stations, Barcelona, Spain not to any scale

FIGURE 38: Number of ratings visualization for filtered stations

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DATA SYNTHESIS Then using the data synthesis methodology of sentiment analysis, the average polarities of the individual stations were calculated which resulted in a total average polarity of 0.15 (refer to Figure 40). The average polarities of the analysed stations were overlaid onto the Google rating which showcased a visible discrepancy between the publicly rating and the text reviews generated by users (refer to Figure 41). When the ratings were plotted against the subjectivity of the text, it was concluded that the higher rated comments were more subjective and the lower rated comments had more objectivity (refer to Figure 39). When the average subjectivity of the reviews were overlaid onto the Barcelona land use zoning map, it can be seen that the residential zones on the outskirts of Barcelona have more objective comments when compared to touristic locations (refer to Figure 42). An inference that could be derived from this insight is that locals were contributing to the comments and have a more objective understanding of the location.

FIGURE 39: Subjectivity-polarity scatter plot diagram of selected stations

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Plaza Espanya

Hospital del Bellvitge

Sagrada Familia

Barceloneta

Europa | Fira

Aeroport T1

Collblanc

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Plaza Catalunya

DUNES | Case Studies

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1

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Average Polarity Map of Filtered Stations Metro Stations, Barcelona, Spain not to any scale

FIGURE 40: Average polarity map of filtered stations

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Average Rating and Average Polarity Comparison Map Metro Stations, Barcelona, Spain not to any scale

FIGURE 41: Average rating and average polarity comparison map

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-1.0

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Average Subjectivity Visualization with Land-use Map Metro Stations, Barcelona, Spain not to any scale

FIGURE 42: Average subjectivity visualization with land-use map

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(1)MOST

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Plaza Catalunya

Torras i Bages

Bac de Roda

Pep Ventura

Tetuan

Roquetes

Selva de Mar

Vilapicina

Muntaner

El Putxet

Europa | Fira

Aeroport T1

Fira

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La Sagrera

La Salut

FIGURE 43: Wordcloud for selected metro stations

When the most frequent words of each station were analysed, some of the more prominent words include people, service, security, greenery, clean and access whereas the most frequent word commented was people (refer to Figure 43). For the Latent Dirichlet Allocation model, four slots for topics were allocated with 80 passes to generate topics that were related to accessibility, service, comfort, and security, which correspond to the topics scored the lowest in the survey by Transports Metropolitans de Barcelona (refer to Figure 44).

BEAUTIFUL ACCESS GREAT PLACE AREA NEIGHBORHOOD TIME EXCELLENT STOP TICKET

PLACE CITY CENTER BARCELONA NICE TRAIN GOOD SQUARE CLEAN TRANSPORT

GOOD BARCELONA BEAUTIFUL CLEAN AIRPORT SERVICE WORK LOT GREAT CHURCH

STATION LINE PEOPLE AIRPORT SUBWAY STAFF HOUR SECURITY TRAIN BAD

FIGURE 44: Topic modelling results for the LDA model

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SUMMARY Firstly, this validation experiment proved to be successful in verifying the credibility of using Google Reviews. Although the Google Maps API request was limiting, the utilization of the Data Miner widget for Google Chrome enabled the data extraction.

With full access to the API request, the access to the information would be much easier.

The results of the sentiment analysis also proved to be insightful as the discrepancy between the public rating of the location and the text reviews was significant. Accordingly, natural language processing techniques are able to provide a higher resolution of information with regards to public opinions on the urban environment. Geographical inferences can also be made by overlaying data onto the analysis such as the subjectivity-land use study.

Using the topic modelling algorithm and the most frequently used words, categories of keywords can be identified and used as a basis for decision making. Comparatively, government bodies are able to use this methodology to validate their policies instead of conducting public surveys.

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However, the scale of this analysis was too large to conduct the Data Evaluation phase and the limitations of the metro environment to derive urban parameters would impede the research analysis. A case study of a smaller scale in an urban setting with a higher resolution of geo-localized points would be able to corroborate the full research methodology.

DUNES | Case Studies FIGURE 45: The fountain in Parc de la Ciutadella in Barcelona, Spain Source: Bernard Gagnon (2009) / Wikipedia

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case study 2 urban park

DUNES | Case Studies

1 KM

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5.2 URBAN PARKS Taking into consideration the results and findings of the validation experiment of the research methodology on the metro stations in Barcelona, Spain. A case study of a smaller scale in an urban setting was explored which resulted in selecting Parc de la Ciutadella, Barcelona as the second case study. Sharing the site with the city zoo, this urban park is a dynamic urban node that hosts all kinds of leisure and cultural activities. Within its 70acre grounds, there are numerous buildings, public art as well as monuments such as the waterfall located in its center (Ajuntament de Barcelona, 2020). Its diverse typology and strategic location allows for a higher resolution of analysis for the DUNES methodology. With the positive results of utilizing review-based social media platforms, TripAdvisor was also integrated in

FIGURE 46: Overall site plan of Parc de la Ciutadella

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addition to Google Reviews.

DUNES | Case Studies

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DATA INPUT Before initiating the workflow, an analysis grid of 50 x 50 with 20m spacing was generated over the site where spheres visualize data in the form of colours at the intersection points (refer to Figure 47). This resolution proved to be the most efficient in processing power without compromising the visualization of the data. The interpolation method used for the heatmap was based on inverse distance weighting. Within the boundaries of the site, with exception of two locations, 34 geolocalized points of review were identified on Google Reviews and 13 in TripAdvisor (refer to Figure 48 & 49).

In the case of this methodology, the geo-localized points were aggregated because of the similarities in location as well as the format of the reviews which consisted of text data, dates of review, and ratings from a scale

FIGURE 47: Analysis grid

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from 1 to 5. The aggregated number of locations form a total of 36 geo-localized points of review (refer to Figure 50).

DUNES | Case Studies

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FIGURE 48: Google Reviews locations

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TYPE OF LOCATION

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Transport Infrastructure

Public Space

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FIGURE 49: TripAdvisor locations

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TYPE OF LOCATION

DUNES | Case Studies

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Public Space

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Kiosks

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Private Building

FIGURE 50: Combined platforms - locations

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TYPE OF LOCATION

DUNES | Case Studies 27 26 5

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Limitations with the API requests for both Google Maps and TripAdvisor, similarly experienced in the validation experiment, resulted in the reuse of the web scraping widget for Google Chrome, Data Miner.

While this allowed for the relevant information to be mined, in this application, there were limitations with the amount of data that was allowed to be extracted.

For Google Reviews, only a maximum of 2500 reviews per location was allowed and with TripAdvisor, a total limit of 5000 reviews was allowed. This restriction resulted in a limited amount of reviews that were able to be extracted from Google Reviews and only english reviews were mined from TripAdvisor. Even so, the total number of reviews extracted was 13,818 where 9343, more than half, of the reviews were text comments (refer to Figure 52).

25

24

9343

4475

Comments No Comments

NUMBER OF REVIEWS LESS

FIGURE 51: Combined platforms - number of mined reviews

MORE

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FIGURE 52: Pie chart of number of reviews with and without comments

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Through the visualization of the number of reviews, hotspots are able to be identified where the locations receive the most traffic such as the Barcelona Zoo, the entrance of Parc de la Ciutadella as well as the waterfall monument (refer to Figure 55). When the average ratings of the geo-localized points were generated onto the analysis grid, a more positive visualization is displayed (refer to Figure 53). The majority of the ratings were rated in the higher end of the positive scale (refer to Figure 54).

7707

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7000

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Trip Advisor

AVERAGE RATING 1

FIGURE 53: Combined platforms - average ratings

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FIGURE 54: Stacked graph of number of reviews for each rating

DUNES | Case Studies FIGURE 55: Isometric map of the case study, average rating heatmap and number of reviews heatmap

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Combined Platforms - Average Text Polarity Parc de la Ciutadella, Barcelona, Spain 0

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DATA SYNTHESIS Once again validated, the sentiment analysis that was conducted to generate the polarity heatmap of the text data proved to be of a higher resolution of analysis when compared to the average rating heatmap (refer to Figure 56). A visualization of neutral to positive colours were displayed with a majority of the polarity being 0, neutral and more than 0, positive (refer to Figure 57).

10000

8273 25

8000

6000

24

4527 4000

2000

1018 <0

0

0>

Google Reviews

Trip Advisor

AVERAGE TEXT POLARITY -1.0

FIGURE 56: Combined platforms - average text polarity

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1.0

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FIGURE 57: Stacked bar chart of number of reviews rated negative, neutral and positive

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A discrepancy calculation was carried out using the following formula to identify the level of deviation:average polarity heatmap value / average rating heatmap value = discrepancy value

The values for the heatmap were retrieved by calculating the average RGB values from each intersection point using Grasshopper which resulted in a value larger than one, proving the substantial amount of discrepancy:3726.11 / 2659.83 = 1.401

FIGURE 58: Isometric map for the most frequent words used in reviews with positive sentiments

FREQUENCY OF WORD LEAST

MOST

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In addition to the average polarity, the most frequent words for positive as well as negative sentiments were visualized over the site (refer to Figure 58 & 59). The texts with positive sentiments were related to people, infrastructure, various recreational activities, vegetation as well as ambient qualities of the environment. On the other hand, the texts with negative sentiments were mostly related to cleanliness, service, vegetation, and infrastructures located throughout the park.

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FIGURE 59: Isometric map for the most frequent words used in reviews with negative sentiments

FREQUENCY OF WORD LEAST

MOST

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This experiment further demonstrates the high level of accuracy generated by using natural language processing techniques on text data from review-based social media platforms. The subjectivity analysis was not conducted on this case study because of the lack of distinctive zones to derive substantial inferences.

The most frequent word analysis was used instead of the topic modelling because the results of the data proved to be more insightful by showcasing keywords generated by users.

DUNES | Case Studies

ENTRANCE/EXIT POINTS

TRANSIT POINTS

VEGETATION

SAFETY

LIGHTING

WALKING PATH

VEHICLE PATH

COMFORT

FREQUENCY OF PEOPLE

REST FURNITURE

HEALTH

WASTE FURNITURE

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DATA EVALUATION

(far left) FIGURE 60: Urban Emotions (left) FIGURE 60: Urban indicator categories and the elements utilized in this research

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As Parc de la Ciutadella has proven to be a functional case study, the Data Evaluation phase was implemented. This allows the research to move forward and have Parc de la Ciutadella be evaluated with regards to the three urban emotions:- safety, comfort and health using the specific urban elements under the urban indicator categories:- accessibility, visibility, circulation and infrastructure.

DUNES | Case Studies FIGURE 61: First page of the DUNES Interactive Survey on Google Forms

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The DUNES Interactive Survey was deployed through the Google Forms platform because of its easy accessibility and interface.

The survey was conducted from the 3rd of June 2020 to the 11th of June 2020 and received 256 responses from 20 different countries. The user profile extracted from the survey was limited to two criteria which were gender identity and age group. Slightly more than half of the respondents were within the age of 18-29 years old and were mostly female (refer to Figure 63 & 64).

FIGURE 63: Pie chart of the age groups of the respondents

FIGURE 62: Locations of countries in which the respondents were from

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FIGURE 63: Pie chart of the gender identity of the respondents

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Through the survey, images as well as its respective urban indicator values with regards to the urban emotions are retrieved (refer to Figures 64 -79). The results portray a myriad of oddities, which were unexpected but interesting insights were able to be derived which are as follows:-

SAFETY Positive Sentiments NE

NE

1.0

WF

Entrance / Exit Points: Very long distance WF NT Transit Points: Very long distance 1.0

NT

0.8

0.8

0.6

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0.4

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WP

0.2

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0.2

WP

Walking Path: Very narrow Frequency of movement/people: Very low LL FM Vehicle Path: Very long distance

FM

VG

Vegetation: Low density RF Lighting: Low levels

Rest Furniture: Very low number of units VG VP Waste Furniture: Very low number of units

VP

FIGURE 64: Radial chart of the positive perceptions of safety with regards to the urban indicators

Negative Sentiments NE 1.0

WF

Entrance / Exit Points: Very long distance Transit Points: Very long distance

NT

0.8

0.6

0.4

RF

WP

0.2

LL

FM

VG

VP

FIGURE 65: Radial chart of the negative perceptions of safety with regards to the urban indicators

111

Vegetation: Low density Lighting: Low levels Walking Path: Very narrow Frequency of movement/people: Very low Vehicle Path: Very long distance Rest Furniture: Very low number of units Waste Furniture: Very low number of units

Positive Sentiments NE

NE

1.0

WF

Entrance / Exit Points: Very long distance WF NT Transit Points: Very long distance 1.0

NT

0.8

0.8

0.6

0.6

0.4

RF

0.4

WP

0.2

LL

0.2

WP

Walking Path: Narrow Frequency of movement/people: Low LL FM Vehicle Path: Very long distance

FM

VG

Vegetation: Low density RF Lighting: Moderate levels

VP

FIGURE 66: Radial chart of the positive perceptions of comfort with regards to the urban indicators

Rest Furniture: Low number of units VG VP Waste Furniture: Very low number of units

Negative Sentiments NE 1.0

WF

Entrance / Exit Points: Long distance Transit Points: Very long distance

NT

0.8

0.6

0.4

RF

WP

0.2

LL

FM

VG

VP

Vegetation: High density Lighting: Very low levels Walking Path: Very narrow Frequency of movement/people: Very low Vehicle Path: Long distance Rest Furniture: Low number of units Waste Furniture: Very low number of units

FIGURE 67: Radial chart of the negative perceptions of comfort with regards to the urban indicators

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WP

COMFORT

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HEALTH Positive Sentiments NE

NE

1.0

WF

Entrance / Exit Points: Short distance WF NT Transit Points: Long distance 1.0

NT

0.8

0.8

0.6

0.6

0.4

RF

0.4

WP

0.2

LL

0.2

WP

Walking Path: Narrow Frequency of movement/people: Low LL FM Vehicle Path: Moderate distance

FM

VG

Vegetation: Low density RF Lighting: High levels

Rest Furniture: Very low number of units VG VP Waste Furniture: Very low number of units

VP

FIGURE 68: Radial chart of the positive perceptions of health with regards to the urban indicators

Negative Sentiments NE 1.0

WF

Entrance / Exit Points: Very long distance Transit Points: Very long distance

NT

0.8

0.6

0.4

RF

WP

0.2

LL

FM

VG

VP

FIGURE 69: Radial chart of the negative perceptions of health with regards to the urban indicators

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Vegetation: Low density Lighting: High levels Walking Path: Narrow Frequency of movement/people: Very low Vehicle Path: Very long distance Rest Furniture: Very low number of units Waste Furniture: Low number of units

Positive Sentiments NE

NE

1.0

WF

Entrance / Exit Points: Long distance WF NT Transit Points: Very long distance 1.0

NT

0.8

0.8

0.6

0.6

0.4

RF

0.4

WP

0.2

LL

0.2

WP

Walking Path: Narrow Frequency of movement/people: Low LL FM Vehicle Path: Long distance

FM

VG

Vegetation: Low density RF Lighting: Moderate levels

VP

FIGURE 70: Radial chart of the positive perceptions of overall emotions with regards to the urban indicators

Rest Furniture: Low number of units VG VP Waste Furniture: Low number of units

Negative Sentiments NE 1.0

WF

Entrance / Exit Points: Very long distance Transit Points: Very long distance

NT

0.8

0.6

0.4

RF

WP

0.2

LL

FM

VG

VP

Vegetation: Low density Lighting: Moderate levels Walking Path: Very narrow Frequency of movement/people: Very low Vehicle Path: Very long distance Rest Furniture: Very low number of units Waste Furniture: Low number of units

FIGURE 71: Radial chart of the negative perceptions of overall emotions with regards to the urban indicators

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WP

OVERALL EMOTIONS

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SAFETY Positive Sentiments

FIGURE 72: Images chosen by respondents related to positive sentiments of safety

SAFETY Negative Sentiments

FIGURE 73: Images chosen by respondents related to negative sentiments of safety

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COMFORT Positive Sentiments

FIGURE 74: Images chosen by respondents related to positive sentiments of comfort

COMFORT

FIGURE 75: Images chosen by respondents related to negative sentiments of comfort

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Negative Sentiments

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HEALTH Positive Sentiments

FIGURE 76: Images chosen by respondents related to positive sentiments of health

HEALTH Negative Sentiments

FIGURE 77: Images chosen by respondents related to negative sentiments of health

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OVERALL EMOTIONS Positive Sentiments

FIGURE 78: Images chosen by respondents related to positive sentiments of overall emotions

OVERALL EMOTIONS

FIGURE 79: Images chosen by respondents related to negative sentiments of overall emotions

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Negative Sentiments

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An overview of the survey data shows that the urban indicator values are inconsistent with the urban emotions. This is mostly due to the lack of diversity in tagging the images with the urban indicator data. This can be seen especially with the urban indicators related to accessibility that are consistently in the higher values and the infrastructure indicators which are constantly in the lower values.

Although the values are inconsistent with the general understanding of the effects of the urban indicators with the urban emotions, the difference in the values can assess what indicators greatly influence the perception of the emotions. The values are as follows:-

SAFETY Entrance / Exit Points: 0 Transit Points: 0

Entrance / Exit Points: 0.158 Transit Points: 0.104

Vegetation: 0.394 Lighting: 0.166

Vegetation: 0.480 Lighting: 0.521

Walking Path: 0.009 Frequency of movement/people: 0.111 Vehicle Path: 0

Walking Path: 0.166 Frequency of movement/people: 0.191 Vehicle Path: 0.700

Rest Furniture: 0.034 Waste Furniture: 0.019

Rest Furniture: 0.095 Waste Furniture: 0

HEALTH

119

COMFORT

OVERALL EMOTIONS

Entrance / Exit Points: 0.5 Transit Points: 0.15

Entrance / Exit Points: 0.125 Transit Points: 0.084

Vegetation: 0.152 Lighting: 0.035

Vegetation: 0.061 Lighting: 0.04

Walking Path: 0.05 Frequency of movement/people: 0.255

Walking Path: 0.063 Frequency of movement/people: 0.118

Vehicle Path: 0.42

Vehicle Path: 0.067

Rest Furniture: 0.17 Waste Furniture: 0.011

Rest Furniture: 0.116 Waste Furniture: 0.1

By calculating the deviation of the values, these results prove to be consistent with the literature reviews conducted in this research. Urban indicators with regards to visibility are important in determining the perception of safety in the urban environment. Frequency of movement could also be inferred by associating the data to the need for natural surveillance. The indicators that affect the perception of comfort are related to key urban indicators used in city surveys such as accessibility.

Besides that, circulation and visibility also contribute to the perception of comfort. With regards to health, transit points and vegetation are key indicators that determine the perception of health which is consistent with the aspects of health. With the overall emotions, an interesting insight is that the frequency of people and movement is very influential. The survey was conducted during the COVID-19 pandemic, where large crowds and social environments are deemed unsafe. This indicator proves interesting insights into trends experienced by citizens and further validates the use of this methodology to assess the urban quality.

(middle) FIGURE 81: Wordcloud of keywords from respondents related to comfort (bottom) FIGURE 82: Wordcloud of keywords from respondents related to health

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(top) FIGURE 80: Wordcloud of keywords from respondents related to safety

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In order to determine the numerical relationship between the sentiment of the urban emotions and the urban indicators, a regression analysis machine learning model is set up to compute the results of the survey. The Grasshopper 3D plugin, OWL for Machine learning was used to facilitate this process as the results would be readily integrated into the digital environment for the Data Simulation phase. After conducting trial and error exercises, it was found that using the backpropagation technique with a learning rate of 0.03 and 5 epochs proved effective in minimizing the errors (refer to Table 2).

Urban Emotion

Level of Error

SAFETY

0.000237

COMFORT

0.000257

HEALTH

0.000182

OVERALL EMOTIONS

0.000043

TABLE 2: Level of errors produced by the backpropogation model for the urban emotions

The evaluation of the sentiments of the existing reviews with regards to the urban emotions, a classification machine learning model is set up using the Tensorflow KERAS library through Jupyter Notebook to evaluate the keywords extracted from the visual preference question typology. After experimenting with various techniques using a standard batch size of 32 and 5 epochs, the accuracy of the machine learning model that was the highest determined the technique used for the emotion classification model (refer to Table 3)

Classification Technique

Level of Accuracy

Linear Support Vector Classifier

0.651693

Logistic Regression

0.651371

Multinomial IB

0.647454

Random Forest Classification

0.603504

Sequential

0.688

TABLE 3: Accuracy produced by the various classification technique for emotion classification

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The emotion classification algorithm allows the reviews for the case study to be evaluated on the urban emotions.

The algorithm returns the confidence level of the emotion classification through a percentage value known as weightage. Each review can be assessed on how much of the sentence is related to safety, comfort, and health. Through simple mathematical calculations using the results of the polarity analysis, the urban emotion sentiments are able to be produced (refer to Figure 83). Using these values, the average emotion sentiments for each urban emotion can be calculated to produce emotion heatmaps (refer to Figure 84, 86 & 87).

review polarity

0.4333 " Nice park, quiet, not that touristique as Park Guell.

Good place to chill out and read a book for example. " - 3 STAR GOOGLE REVIEW FROM PLACA DE JOAN FIVELLER

safety weight

comfort weight

health weight

0.351

0.639

0.001

0.351 comfort weight

0.639 health weight

0.001

FIGURE 83: Calculation for the sentiment of urban emotions

x x x

review polarity

0.433 review polarity

0.433 review polarity

0.433

= = =

sentiment of safety

0.152 sentiment of comfort

0.277 sentiment of health

0

safety sentiment

comfort sentiment

health sentiment

0.152

0.277

0 122

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safety weight

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On average, according to the emotion classification model, all the reviews in the case study mostly discussed feelings of comfort through the reviews followed by safety and health (refer to Figure 85).

17.2% 36.5%

Safety Comfort

46.3%

Health

25

FIGURE 85: Percentage of emotions detected in the aggregated reviews

AVERAGE SAFETY SENTIMENTS -1.0

FIGURE 84: Combined platforms - average sentiment of safety

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AVERAGE COMFORT SENTIMENTS -1.0

FIGURE 86: Combined platforms - average sentiment of comfort

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AVERAGE HEALTH SENTIMENTS -1.0

FIGURE 87: Combined platforms - average sentiment of health

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DUNES | Case Studies FIGURE 88: Isometric map of the average polarity map, safety sentiment map, comfort sentiment map, and health sentiment map

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Combined Platforms - Areas of Interest Parc de la Ciutadella, Barcelona, Spain 0

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By overlaying all these heatmaps, areas of interest can be identified in which there are potentials for urban interventions (refer to Figure 89). For this research methodology, areas of interest for each emotion are identified and the results are overlaid to identify a general area in which there are overlaps.

25

FIGURE 89: Combined platforms - areas of interest

Negative Sentiment Risk Area

Negative Comfort Risk Area

Negative Safety Risk Area

Negative Healthy Risk Area

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Combined Platforms - Chosen Area of Interest Parc de la Ciutadella, Barcelona, Spain 0

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FIGURE 90: Combined platforms - chosen area of interest

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In preparation for the Data Simulation phase, the urban indicator data for the case study was extracted using a multitude of information and sources and integrated into the Rhinoceros and Grasshopper 3D workspace:ACCESSIBILITY (NE) Entrance and Exit Points

Data: Point locations of entrance and exits

Source: Analysis from satellite imagery and open-data maps (NT) Transit Points

Data: Point locations of transit option locations

Source: Analysis from satellite imagery and open-data maps VISIBILITY (VG) Vegetation

Data: Surface area of vegetation

Source: Analysis from satellite imagery and open-data maps (LL) Lighting

Data: Average light levels during operation hours

Source: Smart Citizen Kit sensor data in Parc de la Ciutadella CIRCULATION (WP) Walking Path

Data: Walking path areas

Source: Analysis from satellite imagery and open-data maps (FM) Frequency of Movement / People

Data: Coloured heatmap of the frequency of movement

Source: Strava heatmap of activity hotspots (VP) Vehicle Path

Data: Road network areas

Source: Analysis from satellite imagery and open-data maps INFRASTRUCTURE (RF) Rest Furniture

Data: Point location of rest/leisure furniture

Source: Analysis from satellite imagery (WF) Waste Furniture

Data: Point location of waste furniture

Source: Analysis from satellite imagery

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VEGETATION

LIGHTING

FREQUENCY OF PEOPLE

FIGURE 91: Visualizations of the urban indicators in the Rhinoceros environment

TRANSIT POINTS

REST FURNITURE

WALKING PATH

VEHICLE PATH

WASTE FURNITURE

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ENTRANCE/EXIT POINTS

DUNES | Case Studies

DATA SIMULATION Once the urban indicator data is integrated into the Grasshopper workspace, an analysis boundary of the area of interest is drawn in the Rhinoceros workspace and the Grasshopper script is immediately launched to extract the urban indicator values using the following methodologies:ACCESSIBILITY (NE) Entrance and Exit Points

Numerical Value: Distance to the nearest point (m)

Methodology: Shortest walk from the center of the analysis boundary (NT) Transit Points

Numerical Value: Distance to the nearest point (m)

Methodology: Shortest walk from the center of the analysis boundary VISIBILITY (VG) Vegetation

Numerical Value: Surface area of vegetation (m 2 )

Methodology: Total surface area within the analysis boundary (LL) Lighting

Numerical Value: Average lux levels (lux)

Methodology: Information from sensor data CIRCULATION (WP) Walking Path

Numerical Value: Surface area of walking path (m 2 )

Methodology: Total surface area within the analysis boundary (FM) Frequency of Movement / People

Numerical Value: Frequency of movement (unit)

Methodology: Data extraction from Strava heatmap (VP) Vehicle Path

Numerical Value: Distance to the nearest road (m)

Methodology: Closest distance to the road center line INFRASTRUCTURE (RF) Rest Furniture

Numerical Value: Number of rest/leisure furniture (unit)

Methodology: Point location of rest furniture (WF) Waste Furniture

Numerical Value: Number of waste furniture (unit)

Methodology: Point location of rest furniture

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ENTRANCE/EXIT POINTS

TRANSIT POINTS

X X

VEGETATION

X X

FREQUENCY OF PEOPLE

X

REST FURNITURE

FIGURE 92: Visualizations of the urban indicator extraction within the area of interest chosen

LIGHTING

WALKING PATH

X

X

VEHICLE PATH

X

WASTE FURNITURE

X X X

X X X

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The extracted values are then fed into the trained regression analysis model to predict the emotion landscape of the area of analysis. The following values are derived from the script:-

Average Sentiment (avs) Quantification method: The average value of the three closest points divided by the distance to the center of the analysis area (units) Positive Sentiment Weightage (pw) Quantification method: Value extracted from the results of the regression analysis model trained by the responses from the interactive survey (%) Negative Sentiment Weightage (nw) Quantification method: Value extracted from the results of the regression analysis model trained by the responses from the interactive survey (%) Risk Area Sentiment Quantification equation: [ ( avs / pw ) + avs ] + [ (avs / nw) - avs ] (units)

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AVERAGE SENTIMENT

POSITIVE WEIGHTAGE

NEGATIVE WEIGHTAGE

X%

Y%

S2 d2

S1

d1

S/d d3 S3

RISK AREA SENTIMENT

Z

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FIGURE 93: Visualizations of the extraction of the sentiment criteria within the area of interest chosen

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All the urban indicator layers that are intended to be manipulated are unlocked. A closed polyline curve is drawn of the area of interest in the Rhinoceros workspace.

The Grasshopper 3D script integrated with the regression model is activated and performs all the relevant calculations automatically.

A slider from Grasshopper 3D enables the visualization of the four urban emotions as well as their relevant values within the boundary.

FIGURE 94: Step-by-step screenshots of conducting the Data Simulation phase in the Rhinoceros environment

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The values for the emotions are displayed over a sphere that represents the gradient of emotion sentiment through colour. The less saturated it is, it is negative.

The urban indicators are manipulated in the Rhinoceros workspace and the resulting values are displayed in real-time for efficiency.

FIGURE 94: Step-by-step screenshots of conducting the Data Simulation phase in the Rhinoceros environment

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The urban indicator values are also displayed above the sphere to enable a quantitative visualization of the manipulation.

DUNES | Case Studies

N

Initial State - Average Sentiment of Comfort Parc de la Ciutadella, Barcelona, Spain 0

143

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The design and planning intervention for the area of interest could be informed by analysing the most frequent words for negative sentiments adjacent to the risk area, image based analysis, or referring to the highest negative or lowest positive percentage of emotion. For the current methodology, because the urban emotion comfort had the lowest positive percentage of the three emotions, the urban indicators within the boundary were manipulated to increase its percentage value (refer to Figure 94 & Table 4).

Criteria

Value

Unit

Boundary Area

11675.38

m²

Av. Nearest Sentiment

0.013

u

Positive Weightage

62.67

%

Negative Weightage

37.16

%

Risk Area Sentiment

0.0556

u

Urban Indicator

Value

Unit

NE

101.49

m

NT

85.46

m

VG

2975.39

m2

LL

218.91

lux

WP

9027.33

m²

FM

341

u

VP

71.04

m

RF

23

u

WF

7

u

AVERAGE COMFORT SENTIMENTS -1.0

FIGURE 95: Initial state average sentiment of comfort

0

1.0

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TABLE 4: Values of the initial state of the comfort emotion within the area of interest

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N

Manipulated State - Average Sentiment of Comfort Parc de la Ciutadella, Barcelona, Spain 0

145

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The comparison study of the existing and intervened emotion landscape for comfort shows that by manipulating urban indicators, in this case increasing the size of the walking path and vegetation area as well as adding more infrastructures, the positive weightage increases and the negative weightage decreases (refer to Table 5).

Criteria

Value

Unit

Boundary Area

11675.38

m²

Av. Nearest Sentiment

0.013

u

Positive Weightage

64.32

%

Negative Weightage

36.06

%

Risk Area Sentiment

0.0561

u

Urban Indicator

Value

Unit

NE

101.49

m

NT

85.46

m

VG

3247.22

m2

LL

218.91

lux

WP

11245.16

m²

FM

341

u

VP

71.04

m

RF

58

u

WF

9

u

AVERAGE COMFORT SENTIMENTS -1.0

FIGURE 96: Manipulated state average sentiment of comfort

0

1.0

146

DUNES | Case Studies

TABLE 5: Values of the manipulated state of the comfort emotion within the area of interest

The manipulation of the urban indicators to increase the positive percentage for comfort also affects the values of the other urban emotions. Most notably, the overall emotions increased by 2.53% and the health sentiment positive percentage increased by 1.77%. While the area of interest emotion sentiment value for comfort had a minute increase, the overall emotion sentiment and safety sentiment increased by 5.44% and 5.21%, respectively (refer to Table 6).

OVERALL

HEALTH

COMFORT

SAFETY

ORIGINAL VALUES

AFTER MANIPULATION

Criteria

Value

Unit

Criteria

Value

Unit

Positive Weightage

97.37

%

Positive Weightage

97.58

%

Negative Weightage

2.62

%

Negative Weightage

2.48

%

Risk Area Sentiment

0.508

u

Risk Area Sentiment

0.5359

u

Criteria

Value

Unit

Criteria

Value

Unit

Positive Weightage

62.67

%

Positive Weightage

64.32

%

Negative Weightage

36.16

%

Negative Weightage

36.06

%

Risk Area Sentiment

0.0556

u

Risk Area Sentiment

0.0561

u

Criteria

Value

Unit

Criteria

Value

Unit

Positive Weightage

81.11

%

Positive Weightage

82.57

%

Negative Weightage

24.46

%

Negative Weightage

23.86

%

Risk Area Sentiment

0.069

u

Risk Area Sentiment

0.07

u

Criteria

Value

Unit

Criteria

Value

Unit

Positive Weightage

78.34

%

Positive Weightage

80.37

%

Negative Weightage

31.61

%

Negative Weightage

20.02

%

Risk Area Sentiment

0.0765

u

Risk Area Sentiment

0.0809

u

TABLE 6: Values of the initial and manipulated state of the all the urban emotion within the area of interest

147

SUMMARY Implementing the DUNES methodology on a more focused scale of an urban park has allowed for a higher resolution of understanding the results. The integration of an analysis grid enables visualization techniques that communicate various levels of data through colours and sphere sizes. Exploratory data analysis of the initial heatmaps of number of reviews and average ratings identify hotspots within the case study.

Although there were limitations with the access to the data, the final aggregated data set extracted proved to be functional without any inconsistencies with the sentiment analysis as well as the most frequent word models. Similarly to the validation experiment, the sentiment analysis was able to showcase a finer grained understanding of public opinions with a visible discrepancy between the average public rating.

While the overall perception of Parc de la Ciutadella is mostly positive, the heatmaps generated were able to showcase certain areas in the case study in which negative sentiments of the urban emotions are prevalent. Regardless, to experiment with the methodology, the integration and utilization of the regression model into the digital workspace was seamless and operated as expected. The biggest insight about the experiment on improving the percentage value for comfort was that the other three urban emotions were significantly affected positively as compared to the manipulation of the urban indicators for comfort.

148

DUNES | Case Studies

In the Data Evaluation phase, while the interactive survey was able to provide a general understanding of the public perception of urban parks, there were inconsistencies with the values of the urban indicators. This stems from the fact that there was a limited number of images that were tagged with urban indicator values that were not diverse enough for analysis. Aside from that, the small number of responses limits the potential of the regression model. This portion of the research could definitely be improved by providing a larger image dataset tagged with a wide range of urban indicator values. Also, by receiving more responses, the regression analysis model would be further optimized.With regards to the emotion classification model, it showed evidence of an effective implementation as the text data was able to be evaluated and weighted based on the urban emotions. Much like the regression model, the classification model would further benefit with a larger data set to provide a better accuracy of analysis.

DUNES | Case Studies

5.3 SUMMARY The implementation of the DUNES methodology proved to be successful with exceptions in the extraction of reviews and the set-up for the interactive survey. The biggest restriction was the limitations of the data mining process. While the text data set extracted was able to ensure a functional workflow, the data can be improved by enabling access to all the reviews. The Google Maps API and the TripAdvisor API allows unrestricted access to businesses and consumer-facing websites.

In both cases, natural language processing was a useful technology in providing a higher level of understanding of user experience in public environments. Sentiment analysis, topic modeling, and most frequent words are essential tools that should be adopted by urban design and planning strategies as they provide useful insights to inform decisions.

Depending on the availability of data, the methodology can be replicated in both scales with some adaptations in the Data Evaluation phase. Although the third process was not implemented into the first case study, there is a potential for it to be implemented in future developments. While the results of the DUNES Interactive Survey was not anticipated, the cause of the inconsistencies was identified.

149

It is vital to integrate a large and diverse dataset so as to not have certain values that remain constant throughout the analysis of the data. The survey can definitely be improved further by retaining the number of questions but shuffling the images with a larger data set where the images are tagged with diverse values for urban indicators. Although not fully effective, the classification model proved to be functional and was able to be applied onto existing reviews with no visible inconsistencies.

In the Data Simulation phase, the methodology was uncomplicated and straightforward. The relationships between the urban indicators and the urban emotions are able to be visualized and manipulated as well. As the change in one value affects the other, a balance or priority needs to be established before intervening in the urban environment. While portions of the DUNES methodology required more analysis and experimentation, the core of it has proven the ability to derive

150

DUNES | Case Studies

quantitative values from qualitative data such as emotions.

chapter 6

DUNES | Discussion

discussion

FIGURE 97: The tourist street Las Ramblas in Barcelona, Spain occupied by a large crowd Source: Rasmus (2018) / The Guiri Guide

151

chapter 6

Addressing the challenges of extracting and translating qualitative data to inform design decisions The methodology established by DUNES, which comprises data science processes as well as urban design, tackles a variety of topics weighted mostly on the role of artificial intelligence (AI) in data analysis. More so, the research focuses on a subjective form of data that has no existing standard of analysis. Although theoretical, DUNES intends to contribute to the field of emotional analytics by proposing a practical approach to analysing emotions. This would inform design decisions through the systematic evaluation of subjective data in a digital workflow.

However, the integration of AI applications into human-centered design and planning have sparked concerns and debates especially with regard to bias, ethics and autonomy.

152

DUNES | Discussion

This chapter discusses the acknowledgement of bias within the methodology as well as strategies to respect the importance of data ethics and privacy with regards to accessing personal information from users. Finally, the concern of providing autonomy to artificial intelligence processes in the design and planning field is explored with full consideration on accountability.

DUNES | Discussion

6.1 BIAS It is almost impossible to avoid bias in data analysis and research but it can be addressed by acknowledging these biases and adopting strategies to limit its impact on the research.

Large datasets generated by artificial intelligence or machine learning platforms are known to be biased. An example would be response or activity bias which is created due to a small proportion of who contribute to human generated content and therefore their opinions and preferences are unlikely to reflect the opinions of the general population (Krishnamurthy, 2019). In the case of DUNES, response bias is prominent especially with regards to the extraction of data. As the Google API request is limited to the top five most relevant reviews per location, the Data Miner widget for Google Chrome was used in its replacement.

However, the widget in itself had limitations with a maximum scraping of 2500 reviews per location for Google Reviews and a total of 5000 reviews in Trip Advisor which meant that only English reviews were able to be extracted from Tripadvisor.

As a result, the data generated does not fully represent the perspective of the entire population of Barcelona but it does provide interesting insights on how individuals, both locally and internationally, experience the respective case studies.

As most natural language processing systems are built on english-based resources, which mean that non-english words are unable to be analysed. In addition, Google Maps automatically translates non-english reviews into english and was fed into the analysis model. Hence, the true sentiments of these reviews could potentially be lost but the essence of their opinions are represented by words instead of sentences. As the field of natural language processing develops, analysis of multi-lingual sentences would be made possible. The databases available for sentiment analysis also differ depending on the library utilized but this offers the opportunity to conduct multiple analysis and form a higher resolution of results.

153

6.2 DATA ETHICS and PRIVACY A common topic addressed by research of this nature is the application of data ethics and respect for individual privacy.

With several conferences on artificial intelligence, robotics and autonomous systems over the recent years, attention is being shifted from technical aspects to concepts related to human values. To preserve the ontological nature of humankind, ethics exists. In the European Regulation 2016/679 (General Data Protection Regulation - GDPR) in processing personal data, the following principles should be applied:- lawfulness, fairness and transparency, purpose limitation, data minimisation, accuracy, storage limitation, integrity and confidentiality as well as accountability (Fabiano, 2019). During the data mining process of DUNES, measures were adopted to ensure these regulations were met. Respecting oneâ&#x20AC;&#x2122;s privacy is an important aspect of data analysis even if the methodology is transparent and open-access to the public. DUNES could be used as a model to encourage users to share information pertaining to the city while restricting access to vital personal information. This is because the data gathered is implemented into a methodology that would enhance their personal experience of the urban environment.

Integrating artificial intelligence (AI) into systems have caused concerns about the role of humans in workflows. The response towards its concerns on the social and ethical impacts can be addressed through three aspects. The first being compliance which entails taking action of basic steps to adhere to a set of industry best practices or legal obligations. Integrating the perspectives of user groups into the design of the AI workflow should also be considered. Finally, enabling thought experiments on responsibility and culpability should also be encouraged (Crawford and Calo, 2016). While AI is able to process copious amounts of data, it is unable to humanely synthesize the information. In certain methodologies that require an empathic such as design and planning, AI should be used as a support tool and not as a definitive guide. With that, various factors regarding socio-economics, environmental standards as well as governing policies have to be considered to ensure an inclusive and holistic end result. During the course of this research while AI methodologies were applied, some of the information needed to be manually assessed as the algorithms were unable to understand the data which highlights the importance of the human factor in working with automated processes.

154 2

DUNES | Discussion

6.3 AUTOMATING DESIGN PROCESSES

chapter 7

DUNES | Conclusions

conclusions

FIGURE 98: The lobby of the underground museum, in Paris underneath the glass pyramid Source: Surayyn Selvan (2018)

155

chapter 7

Highlighting the viability of integrating emotional analytics into a design support tool to create holistic cityscapes The Dynamic Urban Nodes Emotion Simulator (DUNES) research project highlights the importance of citizen engagement in designing holistic urban landscapes that take into consideration the individual and collective user experience of space. Although the resources to combat mental health issues in cities are severely underfunded, the awareness of its effects have begun to shift the perspective of decision-makers to better integrate more effective mental health strategies in cities. The research provides the opportunity to make better informed design and planning decisions through a design support tool weighted on emotional analytics that is inexpensive and readily accessible.

In-depth research was conducted on the most effective and non-intrusive method to extract emotions which resulted in the use of natural language processing techniques. The analysis employs text analysis on volunteered geographic information from review-based social media platforms. Geo-localized text reviews are extracted from Google Reviews and TripAdvisor and fed through a sentiment analysis model. The results of this model are fed into a digital workspace where the average ratings and average sentiment scores can be visualized through a heatmap.

Not only are discrepancies between the public rating and public opinions visible, areas of interest of negative sentiments can be identified as well.

156

DUNES | Conclusions

As the concept of emotions is subjective, extensive literature reviews were conducted to derive correlations between the urban environment and citizen emotion. The study concluded that there are three main urban emotions (Safety, Comfort and Health) in which the user perception is heavily influenced by urban indicators related to Accessibility, Visibility, Circulation and Infrastructure. This correlation allows for the quantitative evaluation of the urban emotions and is represented throughout the research methodology.

DUNES | Conclusions

Through the interactive public web survey that integrates visual preference and scale grading question prompts, user perceptions on safety, comfort and health are able to be extracted. The urban indicator results of the survey trains a regression model that is able to identify the numerical relationship between all the urban indicators with regards to negative or positive sentiments of safety, comfort and health. Although the results of the survey were inconsistent, by analysing the deviations of the values, the urban indicators that greatly influence the perception of the urban emotions can be identified. This proves that correlations are possible but the tagged image set needs to be diverse in its urban indicator values. The product of this machine learning model is used in the data simulation phase. The text data derived from the survey, trains a classification model that is able to identify the weight of safety, comfort and health in text data.

This classification model is applied onto existing geo-localized text reviews and correlated with the average sentiment values.

This process results in the generation of heat maps related to negative and positive sentiments of the urban emotions.

The emotion heatmaps are overlaid with the average sentiment heatmaps to further analyse and identify potential areas of interest that require an intervention. The relevant urban indicator data for the case study is extracted through various means and integrated into a digital workspace. Here, the regression model is executed and is able to compute the average sentiment of a chosen area, the percentage of positive and negative weightage of the emotion as well as the average sentiment of the nearest points of review all with regards to safety, comfort, health, and the overall emotions. In this digital environment, the urban indicators are able to be manipulated to produce varied results of the urban emotions.

In the dynamic nature of the cities and citizens, the methodology established by the research project addresses the constantly changing flow of information by adopting artificial intelligence technologies to aid in the explicit quantification of qualitative data. The research project provides the opportunity for decisionmakers to integrate citizens and their perception into design and planning processes. DUNES proves that holistic design strategies can be supported by integrating empathy and datadriven design to derive useful insights into the inner workings of the urban fabric.

157

7.1 REPLICABILITY OF DUNES As proven by the case study experiments, the DUNES methodology can be reproduced at a variety of different scales. Not only that, the process of correlating indicators with emotions is able to be adapted into a variety of different urban contexts such as touristic locations. The urban emotions could be correlated with different urban indicators such as price points, access to tourist information booths, or even distances to attractions.

Potentially, the methodology can be applied into a building scale by setting up various geo-localized points of review within a building boundary and implementing the methodology. At the architectural scale, subjective information such as aesthetics could replace emotions and various building indicators such as colour, materiality, spatial geometry, and spatial volume can be extracted.

7.2 FUTURE DEVELOPMENTS The priority for the research project would be to develop a realtime feedback system from a web-based platform and mobile application into the digital workspace as a continuous process.

Besides that, a real-time interactive visualization web platform where results from the public web survey can be displayed along with the relevant urban maps would be an interesting addition to the existing methodology.

158

DUNES | Conclusions

DUNES could also be deployed in different countries to analyse different trends and experiences with regards to the urban environment. This would create an extensively curated urban emotion database that could be integrated into a digital workspace to evaluate new proposals or existing spaces for that specific location. Ultimately, DUNES can be viewed as a service that decision-makers could integrate into their workflow.

DUNES | List of Figures

list of figures FIGURE 1: Conceptual visualization of the emotion heatmap of Parc de la Ciutadella in Barcelona, Spain. Source: Surayyn Selvan (2020) FIGURE 2: Comparison heatmap of ratings and review sentiments in Parc de la Ciutadella in Barcelona, Spain. Source: Surayyn Selvan (2020) FIGURE 3: High frequency crowd and vehicular movements in Times Square, New York City Source: Oto Godfrey (2015). Retrieved from https://internationalbusinessguide.org/25-largestconsumers-markets-outlook-2015/ FIGURE 4: The pedestrianized Sancho de Avila street in Barcelona, Spain. Source: Š Twitter_Col. SuperillaP9.

Retrieved

from

https://www.publicspace.org/works/-/project/k081-poblenou-s-

superblock FIGURE 5: The proposed master plan for The Great City in Chengdu, China. Source: Adrian Smith + Gordon Gill Architecture (2012). Retrieved from http://smithgill.com/news/great_city_press_ release/ FIGURE 6: Summary of mental health risks statistics and population growth in cities FIGURE 7: City chapters for the Centre for Conscious Design. Source: The Centre for Conscious Design. Retrieved from https://theccd.org/conscious-cities/ FIGURE 8: Locals relaxing in an open space in an urban park in Amsterdam, Netherlands. Source: Nu.nl (2020). Retrieved from https://www.nu.nl/wonen/5787408/amsterdam-plek-gestegen-oplijst-met-leefbaarste-steden-ter-wereld.html?redirect=1 FIGURE 9: Children taking part in local initiatives for The Glories Commitment. Source: Elena Guim (2015). Retrieved from https://www.toposmagazine.com/in-transition/#Photo-6%C2%A9ElenaGuim-631x440 FIGURE 10: 3D printed data cylinders produced from data collection walks. Source: David Hunter (2016). Retrieved from http://datawalking.com/phaseone.html FIGURE 12: The interface for Changing Places by MIT using Lego blocks and projections. Source: MIT (2004). Retrieved from http://web.mit.edu/jiw/www/city-simulation/ FIGURE 13: UrbanCanvas Modeller using the UrbanSim platform. Source: UrbanSim (2017).

Retrieved from https://urbansim.com/urbancanvas-info FIGURE 14: The map interface for Happy Maps in Berlin, Germany showcasing the happiest and fastest routes. Source: Querice, Schifanella & Aiello (2014). Retrieved fromhttps://ideas.ted.com/ the-shortest-paths-to-happiness-literally/ FIGURE 15: Visualization for the most livable streets on the web page for Arturo. Source: 300000kms (2018). Retrieved http://arturo.300000kms.net/#5 FIGURE 16: Heatmap visualization of places with high intensities of fear. Source: Panek, Paszto & Marek (2016). Retrieved from https://www.arcgis.com/apps/Cascade/index.html FIGURE 17: The San Francisco Emotion Map of the Mission District neighborhood with annotations from the participants. Source: Nold (2007). Retrieved from http://www.sf.biomapping.net/index. htm FIGURE 18: Various types of Social Media platforms that offer Volunteered Geographic Information FIGURE 19: Topographical map from the OpenStreetMap programme TopOSM. Source: Ahlzen (2009). Retrieved from https://wiki.openstreetmap.org/wiki/Applications_of_OpenStreetMap

159

FIGURE 20: Identifying emotional cues with a emotional deep alignment network (DAN). Source: Tautkutè & Trzcinski (2018) / TechXplore FIGURE 21: Geneva emotion wheel. Source: Sacharin, Schlegel & Scherer (2012). Retrieved from https://commons.wikimedia.org/wiki/File:Geneva_Emotion_Wheel_-_English.png FIGURE 22: Lövheim cube of emotion. Source: Lovheim (2012). Retrieved from https://commons. wikimedia.org/wiki/File:L%C3%B6vheim_cube_of_emotion.svg FIGURE 23: Emotional tracking and facial recognition through computer vision technology by Realeyes . Source: Levine (2016). Retrieved from https://martechtoday.com/marketers-welcometo-the-world-of-emotional-analytics-159152 FIGURE 24: Driver monitoring systems for cars using Affectiva’s Automotive AI . Source: Affectiva (2018).

Retrieved

from

https://www.affectiva.com/product/affectiva-automotive-ai-for-driver-

monitoring-solutions/ FIGURE 25: Map of parking availability plotted with the negatively rated areas of the city. Source: Chen et al. (2017). Retrieved fromhttp://papers.cumincad.org/data/works/att/cf2017_101.pdf FIGURE 26: Sentiment analysis for the keyword street in Spanish. Source: Chen et al. (2017).

Retrieved fromhttp://papers.cumincad.org/data/works/att/cf2017_101.pdf FIGURE 27: Comparison heatmap of ratings and review sentiments in Parc de la Ciutadella in Barcelona, Spain. Source: Surayyn Selvan (2020) FIGURE 28: Diagram of the DUNES workflow FIGURE 29: Concept landing page for the DUNES Interactive Survey FIGURE 30: Example visualization of the visual preference question with a positive health prompt and the urban indicator values embedded in the image FIGURE 31: Example visualization of the scale grading question with a positive health prompt and the urban indicator values embedded in the image FIGURE 32: List of the images of various urban park environments with their name tags FIGURE 33: Artistic rendering of the average ratings in Parc de la Ciutadella in Barcelona, Spain.

Source: Surayyn Selvan (2020) FIGURE 34: Barcelona Metro and Tramway Network Map FIGURE 35: Number of responses visualization map FIGURE 36: Average rating visualization map FIGURE 37: Filtered stations map FIGURE 38: Number of ratings visualization for filtered stations FIGURE 39: Subjectivity-polarity scatter plot diagram of selected stations FIGURE 40: Average polarity map of filtered stations FIGURE 41: Average rating and average polarity comparison map FIGURE 42: Average subjectivity visualization with land-use map FIGURE 43: Wordcloud for selected metro stations FIGURE 44: Topic modelling results for the LDA model

(2009) / Wikipedia FIGURE 46: Overall site plan of Parc de la Ciutadella FIGURE 47: Analysis grid

160

DUNES | List of Figures

FIGURE 45: The fountain in Parc de la Ciutadella in Barcelona, Spain. Source: Bernard Gagnon

DUNES | List of Figures

FIGURE 48: Google Reviews - locations FIGURE 49: TripAdvisor - locations FIGURE 50: Combined platforms - locations FIGURE 51: Combined platforms - number of mined reviews FIGURE 52: Pie chart of number of reviews with and without comments FIGURE 53: Combined platforms - average ratings FIGURE 54: Stacked graph of number of reviews for each rating FIGURE 55: Isometric map of the case study, average rating heatmap and number of reviews heatmap FIGURE 56: Combined platforms - average text polarity FIGURE 57: Stacked bar chart of number of reviews rated negative, neutral and positive FIGURE 58: Isometric map for the most frequent words used in reviews with positive sentiments FIGURE 59: Isometric map for the most frequent words used in reviews with negative sentiments FIGURE 60: Urban Emotions FIGURE 60: Urban indicator categories and the elements utilized in this research FIGURE 61: First page of the DUNES Interactive Survey on Google Forms FIGURE 62: Locations of countries in which the respondents were from FIGURE 63: Pie chart of the age groups of the respondents FIGURE 63: Pie chart of the gender identity of the respondents FIGURE 64: Radial chart of the positive perceptions of safety with regards to the urban indicators FIGURE 65: Radial chart of the negative perceptions of safety with regards to the urban indicators FIGURE 66: Radial chart of the positive perceptions of comfort with regards to the urban indicators FIGURE 67: Radial chart of the negative perceptions of comfort with regards to the urban indicators FIGURE 68: Radial chart of the positive perceptions of health with regards to the urban indicators FIGURE 69: Radial chart of the negative perceptions of health with regards to the urban indicators FIGURE 70: Radial chart of the positive perceptions of overall emotions with regards to the urban indicators FIGURE 71: Radial chart of the negative perceptions of overall emotions with regards to the urban indicators FIGURE 72: Images chosen by respondents related to positive sentiments of safety FIGURE 73: Images chosen by respondents related to negative sentiments of safety FIGURE 74: Images chosen by respondents related to positive sentiments of comfort FIGURE 75: Images chosen by respondents related to negative sentiments of comfort FIGURE 78: Images chosen by respondents related to positive sentiments of overall emotions FIGURE 79: Images chosen by respondents related to negative sentiments of overall emotions FIGURE 80: Wordcloud of keywords from respondents related to safety

161

FIGURE 81: Wordcloud of keywords from respondents related to comfort FIGURE 82: Wordcloud of keywords from respondents related to health FIGURE 83: Calculation for the sentiment of urban emotions FIGURE 84: Combined platforms - average sentiment of safety FIGURE 85: Percentage of emotions detected in the aggregated reviews FIGURE 86: Combined platforms - average sentiment of comfort FIGURE 87: Combined platforms - average sentiment of health FIGURE 88: Isometric map of the average polarity map, safety sentiment map, comfort sentiment map, and health sentiment map FIGURE 89: Combined platforms - areas of interest FIGURE 90: Combined platforms - chosen area of interest FIGURE 91: Visualizations of the urban indicators in the Rhinoceros environment FIGURE 92: Visualizations of the urban indicator extraction within the area of interest chosen FIGURE 93: Visualizations of the extraction of the sentiment criteria within the area of interest chosen FIGURE 94: Step-by-step screenshots of conducting the Data Simulation phase in the Rhinoceros environment FIGURE 95: Initial state - average sentiment of comfort FIGURE 96: Manipulated state - average sentiment of comfort FIGURE 97: The tourist street Las Ramblas in Barcelona, Spain occupied by a large crowd. Source: Rasmus (2018) / The Guiri Guide FIGURE 98: The lobby of the underground museum, in Paris underneath the glass pyramid. Source: Surayyn Selvan (2018)

TABLE 1: Results from the public survey conducted by Transport Metropolitans de Barcelona for the years 2018 & 2018 TABLE 2: Level of errors produced by the backpropogation model for the urban emotions TABLE 3: Accuracy produced by the various classification technique for emotion classification TABLE 4: Values of the initial state of the comfort emotion within the area of interest TABLE 5: Values of the manipulated state of the comfort emotion within the area of interest TABLE 6: Values of the initial and manipulated state of the all the urban emotion within the area

162

DUNES | List of Figures

of interest

DUNES | References & Bibliography

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appendices no.

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title

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appendix 1

survey image tagging

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appendix 2

survey form

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appendix 3

survey results

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appendix 4

survey analysis

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APPENDIX 1: SURVEY IMAGE TAGGING

Gradient

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Select & Explain

ALONE

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Gradient

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SURVEY QUESTION DETAILS

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Positive

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Polarity Prompt

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31 L10 R12 L3 R16 17 L9 R7 15 2

1 0.25 1 1 0 1 1 1 0.25 1 0 1

0.25 0.75 1 1 0.5 0.25 1 1 0.95 1 1 1

L22 R19 L33 R32 27 L26 R5 18 L24 R29 L4 R1

L11 R25 L21 R28 23 L6 R14 8 L13 R20 30

NE 0.75 0.15 1 1 1 1 0.75 1 1 0.75 0.25

Image Tag

IMAGE DETAILS No. of Images

Health

Safety

Comfort

Safety

Safety

Safety

Comfort

Themes

EMOTIONS

1 1 1 1 0.15 1 1 0.5 1 1 0.15 1

1 1 1 1 1 0.25 1 1 1 1 1 1

NT 1 0.75 1 1 1 1 0.75 0.25 1 1 1

5 1.5 2 1.5 15 5 3 3 5 15 1.5 40

35 20 20 10 30 2 3 2 1.5 3 5 3

WP 10 5 40 3 3 2 15 3 3 2 3

0 1 2 0 1 1 1 1 3 21 0 63

18 63 0 0 39 1 15 0 2 3 0 0

FM 24 0 0 0 0 0 3 11 20 0 0

1 0.5 1 1 0.1 1 1 1 0.25 1 0.1 1

1 1 1 1 0.85 0.15 1 1 0.95 1 0.5 1

VP 1 0.15 1 1 1 1 0.75 1 1 0.8 0.5

7.9112 13.136 5.6544 9.856 4.627 4.0671 3.8096 10.23 3.311 1.6986 1.0056 40.522

1.7676 13.366 14.657 11.527 15.94 34.007 8.1774 7.8012 22.869 21.62 22.163 4.454

VG 8.354 20.779 1.8186 5.0964 40.522 1.0056 2.6052 7.5294 1.0756 4.123 8.2719

URBAN INDICATORS

90.5 120.47 117.62 113.74 118.89 114.53 120.04 136.01 120.5 144.08 35.67 150.77

150.77 136.95 117.74 121.43 90.07 117.79 144.65 90.32 98.55 121.46 88.99 67.6

LL 107.9 137.67 35.67 44.99 124.31 42.61 56.7 105.49 67.99 104.79 98.93

2 1 1 1 0 0 2 15 10 12 0 30

0 6 1 6 30 4 0 0 1 2 1 2

RF 6 8 4 6 0 1 0 0 6 7 1

1 0 1 0 0 0 0 0 0 4 0 9

0 1 0 0 1 1 0 0 0 3 0 1

WF 0 9 0 0 0 0 0 1 1 2 1

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67

66

65

64

63

62

61

AA

AB

AC

AD

AE

AF

AG

AH

AI

AJ

AK

129

128

126 127

125

124

123

122

121

120

119

118

115 116 117

114

113

112

110 111

109

107 108

106

101 102 103 104 105

100

99

98

95 96 97

94

93

AA

AB

AC

AD

AE

AF

AG

AH

AI

AJ

AK

DUNES | Appendices

211

APPENDIX 3: SURVEY RESPONSES

APPENDIX 3: SURVEY RESPONSES

DUNES | Appendices

212

163

162

161

160

159

158

157

156

155

154

153

151 152

149 150

147 148

146

145

144

143

142

140 141

139

137 138

136

135

132 133 134

131

130

AA

AB

AC

AD

AE

AF

AG

AH

AI

AJ

AK

201

200

199

198

195 196 197

194

191 192 193

190

189

184 185 186 187 188

183

181 182

180

179

177 178

176

174 175

173

172

171

166 167 168 169 170

165

164

AA

AB

AC

AD

AE

AF

AG

AH

AI

AJ

AK

DUNES | Appendices

213

APPENDIX 3: SURVEY RESPONSES

APPENDIX 3: SURVEY RESPONSES

DUNES | Appendices

214

229

228

227

226

225

224

223

222

221

220

219

218

217

216

215

214

213

212

211

210

209

206 207 208

205

204

203

202

AA

AB

AC

AD

AE

AF

AG

AH

AI

AJ

AK

256

255

254

253

252

251

250

249

248

247

246

245

244

242 243

241

237 238 239 240

236

235

232 233 234

231

230

AA

AB

AC

AD

AE

AF

AG

AH

AI

AJ

AK

DUNES | Appendices

215

APPENDIX 3: SURVEY RESPONSES

Av. RAv.

1 1

1 1

3.5 0.056

8 27 L3 L9

IMAGE

Av. RAv.

0 0

1 1

14.496 81.85 0.42 0.507

RF 6 2 0 2

WF 0 1 0 1

2.5 0.044

0.5 0.074

COMFORT POSITIVE NE 1 0.5 1 1

NT 0.25 1 1 1

WP 3 30 1.5 3

0.875 0.8125 9.375 0.875 0.813 0.205

FM 11 39 0 1

VP 1 0.85 1 1

VG 7.5294 15.94 9.856 3.8096

LL 105.49 90.07 113.74 120.04

RF 0 30 1 2

12.75 0.9625 9.2838 107.34 8.25 0.202 0.963 0.21 0.621 0.275

WF 1 1 0 0 0.5 0.056

HEALTH POSITIVE

30 R19 L26 15 2

NE 0.25 0.75 0.25 0.25 1

NT 1 1 0.25 1 1

WP 3 20 5 5 15

FM 0 63 1 3 21

VP 0.5 1 0.15 0.25 1

VG 8.2719 13.366 34.007 3.311 1.6986

LL 98.93 136.95 117.79 120.5 144.08

RF 1 6 4 10 12

WF 1 1 1 0 4

Av. RAv.

0.5 0.5

0.85 0.85

9.6 0.21

17.6 0.279

0.58 0.58

12.131 123.65 0.282 0.763

6.6 0.22

1.4 0.156

NE 1 1 1 1 1 0.5 1 1 0.25 0.75 0.25 0.25 1

NT 1 1 1 1 0.25 1 1 1 1 1 0.25 1 1

WP 3 3 3 5 3 30 1.5 3 3 20 5 5 15

FM 0 0 0 0 11 39 0 1 0 63 1 3 21

VG 5.0964 4.454 40.522 7.9112 7.5294 15.94 9.856 3.8096 8.2719 13.366 34.007 3.311 1.6986

RF 6 2 0 2 0 30 1 2 1 6 4 10 12

WF 0 1 0 1 1 1 0 0 1 1 1 0 4

IMAGE R28 R1 23 31 8 27 L3 L9 30 R19 L26 15 2 Av. RAv.

POSITIVE VP 1 1 1 1 1 0.85 1 1 0.5 1 0.15 0.25 1

LL 44.99 67.6 124.31 90.5 105.49 90.07 113.74 120.04 98.93 136.95 117.79 120.5 144.08

0.7692 0.8846 7.6538 10.692 0.8269 11.983 105.77 5.8462 0.8462 0.769 0.885 0.16 0.17 0.827 0.278 0.608 0.195 0.094

APPENDIX 4: SURVEY ANALYSIS

L6 L13 17

NE 1 1 1

NT 1 1 1

Av. RAv.

1 1

1 1

R25 18 R12

Av. RAv.

WP 2 3 5

FM 0 20 1

3.3333 7 0.047 0.111

SAFETY NEGATIVE VP VG LL 1 1.0056 42.61 1 1.0756 67.99 1 4.0671 114.53

1 1

RF 1 6 0

WF 0 1 0

2.0494 75.043 2.3333 0.3333 0.026 0.341 0.078 0.037

COMFORT NEGATIVE NE 0.15 1 1

NT 0.75 1 1

WP 5 2 2

FM 0 0 2

VP 0.15 1 1

VG LL 20.779 137.67 7.8012 90.32 5.6544 117.62

RF 8 0 1

WF 9 0 1

0.7167 0.9167 3 0.6667 0.7167 11.411 115.2 0.717 0.917 0.039 0.011 0.717 0.263 0.69

3 0.1

3.3333 0.37

HEALTH NEGATIVE

L33 R29

NE 1 1

NT 1 1

WP 20 3

FM 0 3

VP 1 1

VG LL 14.657 117.74 21.62 121.46

RF 1 2

WF 0 3

Av. RAv.

1 1

1 1

11.5 0.26

1.5 0.024

1 1

18.139 119.6 0.434 0.728

1.5 0.05

1.5 0.167

NE 1 1 1 0.15 1 1 1 1

NT 1 1 1 0.75 1 1 1 1

WP 2 3 5 5 2 2 20 3

FM 0 20 1 0 0 2 0 3

RF 1 6 0 8 0 1 1 2

WF 0 1 0 9 0 1 0 3

IMAGE L6 L13 17 R25 18 R12 L33 R29

Av. RAv.

NEGATIVE

0.8938 0.9688 5.25 0.894 0.969 0.097

VP 1 1 1 0.15 1 1 1 1

VG 1.0056 1.0756 4.0671 20.779 7.8012 5.6544 14.657 21.62

LL 42.61 67.99 114.53 137.67 90.32 117.62 117.74 121.46

3.25 0.8938 9.5825 101.24 2.375 0.052 0.894 0.217 0.568 0.079

1.75 0.194

216

DUNES | Appendices

WP 3 3 3 5

IMAGE

NT 1 1 1 1

IMAGE

NE 1 1 1 1

IMAGE

R28 R1 23 31

SAFETY POSITIVE FM VP VG LL 0 1 5.0964 44.99 0 1 4.454 67.6 0 1 40.522 124.31 0 1 7.9112 90.5

IMAGE

IMAGE

11/5/2020 ( 256 )

DU N E S DYNAMIC URBAN NODES EMOTION SIMULATOR

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