Portfolio Luyang Zhang
THE GOOD(S) SWARM
The good(s) swarm is a local sharing platform that uses a distributed storage system, a network of buildings and a fleet of drones to operate at Sant MartĂ scale.
David Casanovas TatxĂŠ Wei Wei Luyang Zhang
THE GOOD(S) SWARM CURRENT CHALLENGES
PHYSICAL INFRASTRUCTURE
TRAFFIC
SPACE OCCUPATION
DRONE SYSTEM
DIGITAL INFRASTRUCTURE
DISTRIBUTED STORAGE
BUILDING NETWORK
CENTRALIZED INTERFACE / APP
LOGISTICS
TYPE OF TRIP 7,689,654 in total
TYPE OF TRANSPORTATION
VAN VAN 25.9% 25.9%
MOTOR MOTOR MOTORBIKE BIKE BIKE 5.4% 5.4% 5.4%
PRIVATE 74.42km² 73%
PEDESTRIAN 16.16km² 58%
ROAD 9.64km² 73%
TYPE OF PUBLIC SPACE
TYPE OF VEHICLE SPACE IN BARCELONA 102km2 in total
+
+
PARKING 1.76km² 15%
MOTOR MOTOR BIKE BIKE 34.3% 34.3%
CAR 68.7% VEHICLE 11.40km² 42%
PUBLIC PUBLIC TRANSPORTATION TRANSPORTATION
33.5%
CAR 51.5%
PUBLIC 27.58km² 27%
++
VAN 14.3%
35.9% 35.9%
FOOT+BIKE 7.6%
CONNECTION CONNECTION
VEHICLE 17.5% VEHICLE 41.5%
INTERNAL 64.1%
PUBLIC FOOT+BIKE PUBLIC TRANSPORTATION TRANSPORTATION 48.9% 50.8%
TYPE OF VEHICLE SPACE
In Barcelona in 2014, over seven million trips were carried out, both internal, and in connection to other municipalities. We are lucky enough to have a quite walkable city, where 49% of the internal trips were done by foot or bicycle. However, the numbers don’t look so good on the connection trips. Private motorised vehicles constitute 41% of the trips, of which vans account for a 26%. Space occupation is a tricky thing to quantify, but a good approximation starts with these numbers: only a 27% of the total area of Barcelona is public space, and a 42% of that space is given to cars. Leaving pedestrians with approximately a 14% of the space, and accounting for 9.9m2 of public space per inhabitant.
PERFORMANCE INFRASTRUCTURE
WHERE ROOF
DRONE PHYSICAL BALCONY STORAGE
CONCEPT
Local
Sharing
Platform
CITIZENS STORES MAKERS
EXCHANGING SELLING BORROWING DONATIONG
DIGITAL MARKETPLACE + NETWORK OF BUILDINGS
A local sharing platform is a digital space for people to share their goods (including borrowing, lending, donating, but also renting or selling). It will involve all citizens living in Sant Martí, but also local shop owners, and local makers.
SYSTEM DIAGRAM
e-commerce global production
logistics centre
local production
reuse store
home
waste
reuse shared use
distributed storage
NETWORK
WAREHOUSE
DIGITAL INTERFACE
DATABASE
APP A distributed storage Storage spaces are the biggest invaders of the city space. In Sant Martí, a large number of spaces are used for storage and logistics. They are that large room in the back of the stores where everything is stored, that space that is actually built into the courtyard, occupying what should be green public spaces. They are the underused warehouses we see in every corner of the district, with no activity to the street or for the neighbours, just storage with tons of vans and SUV coming in and out to distribute goods.
recycling
material
UNDERGROUND
landfill
A fleet of drones Drones are the messengers that will deliver the goods from user to user. According to our calculations, and due to the high efficiency of drones and a distributed storage, with a fleet of only 31 units, we can cover the whole current online purchases of the whole neighborhood, providing products faster and rewarding proximity, to encourage second hand sharing between the users. Protocol A centralised app will provide the input into the building’s database, whether that is a new item to be shared, or a request for an item for a specific time and place. Once a request is placed, the buildings will communicate to find all available products fitting that description. The system will find closest drones and calculate the most efficient route to the destination. The app will let the user know all the options, both free second hand options and new, or rental ones, organised by energy saving.
THE GOOD(S) SWARM INFRASTRUCTURE ANALYSIS FLAT ROOFS
A distributed storage will use spaces that are now underused or that will become obsolete. Underused are the flat roofs of Sant Martí, as underused will be the underground spaces currently used to park cars that in the future we will be sharing. Underused are also our balconies, and our windows.
WAREHOUSES
A distributed storage means our goods don’t have to be stored in one place and shipped throughout, but can be intelligently stored using geolocalisation.
FLEET CALCULATION Average distance between 2 random points in rectangle
Average distance of = drone trip
X Lh = 2.200m
Time per trip
Average distance of drone trip 812m
=
Total air time / day TOTAL = 2,160,000m2 ROOF PERCENTAGE = 20.5% (AVAILABLE ROOF AREA/SNAT MARTI AREA)
=
Total drone time per day UNDERGROUND SPACE
/
=
PURCHASES PER DAY Total fleet
=
PROTOCOL
Number of habitants x purchases x = over 16 day x user 87 % Number of purchases x = pur./inh·day building 0.05
TOTAL = 1,350,000m2 UNDERGROUND PERCENTAGE = 12.8% (AVAILABLE ROOF AREA/SNAT MARTI AREA)
Total exchanges = Sant Martí
proportion of internet
proportion
users in Spain
x shoppers in x
of online Spain 71 %
88 % x
/ day 3 p/month / 30 days
No. inhabitants No. Households per household x per building 2.5
VAR
No. Purchases per Population of x inhabitant Sant Martí 0.05
purchases
0.05
= pur. / inh·day
=
0.125 purch. X VAR
11,775 = purch. / day
235.513
TOTAL = 11,775p
X
*70km/h
Total air time / day
Reduction (take off and landing) 50%
812m
=
1,4min
Time per trip X
=
274h
1.4min
+
Total charging time / day
274h
274h
Total air time / day
Total operating time
548h
proportion of in-
Average speed of drone
Total exchanges Sant Martí
=
0.8
Lw = 3.800m
11.775
TOTAL = 600,000m2 WAREHOUSE PERCENTAGE = 5.7% (AVAILABLE ROOF AREA/SNAT MARTI AREA)
Optimisation (closest object chosen)
/
18h
=
548h
=
31 units
THE GOOD(S) SWARM IMPLEMENTATION OF THE GOODS SWARM 0%
A change in the way we get things that both encourages us to share our goods and to get things locally will have an impact on traffic and therefore pollution, energy spent and the increase of local competitiveness, that will go from being at a disadvantage, to being at a clear advantage in a system that rewards proximity.
0% ------ 20%
Last but not least, in a city with the high density of Barcelona, it will have a meaningful impact on space occupation of logistics in the city.
FUTURE 0% ------ 20% ------ 40%
0% ------ 20% ------ 40% ------ 60%
0% ------ 20% ------ 40% ------ 60% ------ 80%
0% ------ 20% ------ 40% ------ 60% ------ 80% ------ 100%
We believe with the full implementation of the system in a district level, we can render obsolete most of the warehouses that now occupy the courtyards of Sant MartĂ, and return this space to the citizens. With a decreased logistics traffic, we can also remove a percentage of the lanes of the neighbourhood, paving the ground for a greener and more pedestrian oriented city, which is undoubtedly where modern cities need to head to.
IMPACT INFRASTRUCTURE DRONE
IMPACT TRAFFIC
PHYSICAL STORAGE
AIR POLLUTION
We tend to be reluctant to change, and a city populated by a swarm of drones can at first seem intrusive. But we certainly accepted living among cars that took over our public space and polluted our streets, so it shouldn’t be hard to imagine a city where the streets are returned to the citizens and a silent fleet of drones tirelessly carries our local goods above the buildings, making our cities more autonomous, resilient, and connected. And in due time, we might be the ones carried through the air, really releasing the ground space to the social use of the citizens. But that is the space for another project.
ENERGY
NETWORK
SPACE
INTERFACE
LOCAL ECONOMY
DIGITAL
CAPITALISM IN A SHARING CITY
Cities are constantly evolving through the interaction of people and the built environment, and data analytics becomes necessary to develop and enhance them. With the rise of Airbnb and the shift towards digital platforms, tomorrow’s economy will be reshaped. Airbnb has inverted a new economic model transforming the traditional one. Analyzing the data from InsideAirbnb will help understand the different patterns behind this sharing platform in 4 different cities, and try to improve the system and economic inequalities and policies in Barcelona.
Natali Barada Maria Uporova Mahsa Nikoufar Luyang Zhang
CAPITALISM IN A SHARING CITY INTRODUCTION
BENCHMARK
CROWD-BASED CAPITALISM
Going back to how we started, we analyzed the Airbnb datasets for 4 cities from InsideAirbnb and were able to grasp a pattern out of it.
Going back in time, the economic system of the 18th century was a market-based economy, shifting to managerial capitalism in the 20th century, where products and goods were distributed by a firm, a traditional hierarchical organization. Whereas today, in the 21st century, digital technologies are transforming the economy, with the evolution of capitalism, called crowd-based capitalism where the sharing economy evolved out of the digital revolution and became an alternative model to the previous one, with the main focus being the crowd. 18TH CENTURY
We noticed that in all the different cities, there are a few hosts that have multiple listings on the platform, going up to 200 and 300 listings.
20 CENTURY
CURRENT MOMENT
MARKET BASED ECONOMY
CROWD-BASED CAPITALISM
MANAGERIAL CAPITALISM
TWO-SIDED MARKET The crowd-based capitalism is transforming business economics, leading to higher economic growth and more variety in products and services, simultaneously, it has disrupted the way the world’s economy operates.
Airbnb as a sharing platform in the era of digitalization has an impact on the cities we live in. The taxonomy developed for the analysis behind this platform: – Individual host: one apartment a local person part of the sharing economy. – Multi-list host: more than one apartment agencies doing business out of this platform.
The business model from an economy-sharing perspective represents a two-sided market, a business model facilitating direct interaction between two different groups through an intermediary being the platform. Such platforms are operating in different sectors, such as uber in the transportation sector, and Airbnb in the housing sector. As a sharing economy, Airbnb was a new hybrid business model, with the initial purpose being the activation of underutilized assets by a peer-to-peer interchange. This platform was supposed to respond to the demand of travelers who wanted to “live like a local; live in a local’s home; eat like a local”. With time it was also providing alternative accommodation in cities.
This taxonomy will allow differentiation between locals trying to be part of the sharing economy, versus agencies that are operating in the same platform. Based on the taxonomy, the focus on the data analysis will be to understand the corporations hijacking the sharing economy and the economic inequalities for benchmark cities, by the different pattern of the data showing different policies put in place. To later be able to propose a new policy for this disruptive technology.
CAPITALISM IN A SHARING CITY BARCELONA.
SINGAPORE.
Being one of the most touristic cities in the world, with a ratio of 1/19 of locals over tourists. The platform of Airbnb is taking over the city, with hosts listing entire apartments, and private rooms. By analyzing Airbnb Data from InsideAirbnb, we noticed an interesting point, most hosts have several listings (the example of one host having 173 listings), this shows how corporations are hijacking the sharing economy platform.
In the case of Singapore, 74% of the listings are for multi-list host.
top1 top2 top3
top5 top5
28%
28%
Airbnb hosts are offering more than one unit (2498 hosts)
Airbnb hosts are offering more than one unit (794 hosts)
64%
74%
Airbnb units are offering by those hosts (11557 units)
Airbnb units are offering by those hosts (5938 units)
Top1 - 173 units Top2 - 133 units Top3 - 116 units
top1 top2 top3
top5 top5
Top1 - 229 units Top2 - 177 units Top3 - 129 units
With the analysis of the data from 2015 to 2019, the business market is taking over this platform with 70% of the listings by multi-list host.
With Singapore being one of the most dense and populated areas, how is this digital platform delt with? Multi-list hosts are the main active users in Airbnb.
Explaining this number, the policies implemented by the government are not tackling this point. The city has mainly tried to limit the Airbnb effect with licensing schemes on rentals in the old town but had never tried to deal with the corporations hijacking sharing economy, concentrated in the hands of large corporations.
By understanding the policy by the government, we can explain this number. In fact, residents cannot rent out their properties for less than 3 months, in this case, Airbnb becomes a platform not focused on tourists but on visitors that stay for a longer period, which is mainly beneficial for the corporations who want to make a business out of this platform. Such strict rules make the city-state one of the tougher markets in which Airbnb operates, the city is trying to safeguard the interests of its citizens, with Singapore being one of the most dense and populated areas.
CAPITALISM IN A SHARING CITY PARIS.
COPENHAGEN.
In the case of Paris, we notice a shift from the active users of the Airbnb platform with only 21% of the listings by multi-list host.
For the case of Airbnb in Copenhagen, the active users of the Airbnb platform are the individual hots, representing the local community.
top1 top2 top3
top5 top5
6%
6%
Airbnb hosts are offering more than one unit (3178 hosts)
Airbnb hosts are offering more than one unit (1308 hosts)
21%
14%
Airbnb units are offering by those hosts (12386 units)
Airbnb units are offering by those hosts (3352 units)
Top1 - 301 units Top2 - 293 units Top3 - 177 units
top1 top2 top3
top5 top5
Top1 - 237 units Top2 - 45 units Top3 - 24 units
Throughout the years, the percentage of the listings by multi-list hosts is diminishing, with the locals being the active hosts of this platform.
Moreover, analyzing more the increase of apartments and rooms, the listings by actual hosts is the dominant; with an average of 13% business listings only throughout the years.
In the case of Paris, the government is fighting to keep Parisians in the center and not let tourists eat up their space, so they implemented policies with regulations. Since 2017, all apartments listings should be registered in the city hall, A tourist tax was implemented as well as a 120-Day rental limit in most neighborhoods in Paris. This explains why only 20% of the listings are for multi-list host.
We can understand this difference by the regulations put in place by the city in 2016: ‘tax on airbnb earnings’ and ‘the number of nights a host can rent out his apartments should be a maximum of 100days/year’ which is not beneficial for the corporations who want to make a business out of this platform.
CAPITALISM IN A SHARING CITY INTERVENTION
SYSTEM
POLICIES
When it comes to comparing governance & policies to markets, there are three main points: 1. The policies and governance operate on a different scale. While markets (specifical ones like Airbnb have one global scale) 2. Markets adapt quickly to change while policies take more time. 3. There are more steps included in following the regulation process, while the registration process in digital platforms requires much fewer steps.
1
2
Everybody has the right to share.
Dynamic Licencing for sharing:
The licence is a right you have, and it can not become a commodity.
A licence for each time the activity is carried out
1-Setting up a policy for a free city where everybody has the right to share. The current tourist accommodation license ( that in most cases are owned by agencies and hotels) is removed. The license is a right you have, and it can not become a commodity. This brings everyone the opportunity to be part of the sharing economy.
2-This means that each time the host wants to share their apartment/room, they have to check if they meet the conditions to be able to have access to the market and operate with their license.
The city needs a system that enables it to have the policies operate in a digital format (dynamic licensing), and also have real-time integrated information of the users and activities to be able to increase the adaptability of the policies by consisting of spatial data infrastructure. The operating system helps to bridge the gap between private businesses, the public sector, and individuals. City’s System
New operation model EU City OS Spain
GOVERNANCE SCALE Catalonia
Registration by the Cadastral System
Barcelona
Dynamic Licence %1.5 Service Fee
4
3 Margin for operation days & units:
The city as a hotel with dynamic rooms
Individuals (Tourists/Locals)
Capacities defined for each neighborhood
3-There is a margin for operation days and units. the host can rent out the apartment for a maximum of 120 days, with a limit of 2 apartment registered. There are no limits for rooms. This type of regulation has been adopted in several cities including Paris & Copenhagen.
4-Platforms like Airbnb enable considering the city as a hotel, with dynamic rooms. As hotels have specific capacity to host, the city should also determine a capacity to be considered “tolerable” to be able to provide a good service for the tourist while conserving the rights of locals.
301-400 400-584
5
15 23
50
150 276
12-6 % Service Fee
The City OS becomes the 4th element in the new operation model of Airbnb ( a two-sided market platform). As the City OS provides service for the platform, 1.5 % of the platform’s revenue goes to the city for the operation management of the system.The City OS becomes the 4th element in the new operation model of Airbnb ( a two-sided market platform).As the City OS provides service for the platform, 1.5 % of the platform’s revenue goes to the city for the operation management of the system.
Dynamic Licence Portal
INDICATORS
DENSITY OF TOURIST ACCOMODATION Hotels Density Airbnb Density
Renting Fee
Airbnb
Spatial Data Infrastructure
Multi-purpose cadastre
The capacities are defined for each neighborhood, and can be set based on 2 indicators: A definite number is not introduced, but some indicators are suggested to be taken into account for determining the capacity density of population, density of tourist accommodation (comparing the overnight of locals to tourists
Request
Offer Renting Fee 3- % Service Fee
Multi-Purpose Cadastre
1-100 101-200 201-300
Access to Market
Access to Market Policy in digital format
120 days for max 2 apartments per user (No limits for rooms)
Guest
BCN OS
Public Sector
DENSITY OF POPULATION BY NEIGHBORHOOD
Sharing of Asset
Host
Private Businesses (Airbnb)
SDI
Unique Identifier
General Information
EL RAVAL Density 43 300 / km2
SAGRADA FAMILIA Density 50 000 / km2
POBLENOU Density 21 900 / km2
844 rooms 586 appartments
369 rooms 626 appartments
196 rooms 226 appartments
Airbnb Profile
The cadastre is a tool that has existed for so many years, however, it has the capacity of being used in other segments as well. This enables integrated information in one platform, enabling new administration services. “Civilized living in market economies is not simply due to greater prosperity but to the order that formalized property rights bring” (De Soto, 1993). New information technology forms the new role of the cadastral systems: the multipurpose cadastre. The cadastre system helps to have a unique identifier for each apartment. This number acts as the key to the profile which consists of general information such as the owner, land use, etc. People should use this number for signing up for Airbnb to be a host. This new process of registration avoids multi-host to register more than the limited amount.
Another part of the OS is the portal that manages the dynamic licensing operation, in which every Airbnb is registered. The portal receives the requests and applies the condition rules ( the days, units, and neighborhood capacity limits) and allows access to market-based on them. Since a license is issued for each time the host wants to rent their home, the city has the data of all the sharing units in its system.
HEALING CITY
Healing City is a project that explores the positive applications of surveillance technology for improving the urban environment and living following emotion's data of citizens. The project explores the use of facial recognition technology in order to collect emotional information of people interacting with different typologies of urban environment. Intense green spaces, parks or low density areas for instance are connected with emotions of calmness or satisfaction while high dense areas, lack of urban furniture or bad shading conditions in public spaces are connected with highly stressful emotions and discontent. The project focuses on collecting emotional - urban environment crowdsourced data by building a system operating in facial recognition. This way a catalogue of different typologies of urban space in relation to different emotions is created. Furthermore, through image analytics of street views in a case study in Barcelona, different typologies of urban space are defined and then translated into emotions according to their type. The outcome of overlapping of these actions creates a heatmap of emotions in the selected city area. The heat-map of emotions allows to identify risk areas and urban problems, and the final step of the project is the proposal of improvements for the urban environment enhancing people's experience in cities. Luyang Zhang
HEALING CITY CURRENT SITUATION
OUTDOOR PUBLIC SPACE PROPERTY
SURVELLANCE CAMERA IN GENERAL 5G IoT installed endpoints for outdoor surveillance cameras will reach 11.2 million units in 2022.
CONNECTION ATTRACTIVE
ATTRACTIVE
OUTDOOR PUBLIC SPACE
MENTAL HEALTH PROBLEM By 2050, 75% of the world’s population will live in urban area.
by up to
39%
.
Increase in anxiety disorders
75%
MAKE PEOPLE FEEL POSITIVE
Urban living is associated with increases in the following mental health problems
Increase in mood disorders
URBAN OUTDOOR PUBLIC SPACE ENVIRONMENT
CONNECTION
RESIDENT MAKE PEOPLE FEEL POSITIVE
Healing city is mainly to improve people’s mental health by improving the urban environment. Because for example many studies have pointed out even by just visual access (viewing natural scenes), green space exposure could reduce stress and restore the ability to pay attention and concentrate. So it’s very import for me to find the connection between the urban environment and residents.
21% Double by up to
.
the risk of schizophrenia.
RESEARCH MAP
EMOTION
DECREASE IN THE USE OF OUTDOOR PUBLIC SPACE Most people spend as much as 87% of their time indoors and 6% in a car or other automobile.
The benefits of being outdoors URBAN SCENE TYPOLOGY
7% outdoor Increase emotional stability.
6% car or other automobile
Reduces anxiety and depression.
87% indoor
10 DISTRICTS RISK OF POOR MENTAL HEALTH (%)
HAD WALKING TRIP YESTERDAY (%)
40
100
30
75
Risk of poor mental health 31%
Good mental health 69%
50
Sarrià-Sant Gervasi
Sant Martí
Sants-Montjuïc
Les Corts
Nou Barris
Sant Andreu
0
analyze the
URBAN ENVIRONMENT from a
25
Gràcia
Sant Martí
Sants-Montjuïc
Sarrià-Sant Gervasi
Nou Barris
Sant Andreu
Gràcia
Les Corts
Horta-Guinardó
Eixample
Ciutat Vella
Ciutat Vella MENTAL HEALTH (%)
in average
Eixample
in average
10
85%
Ciutat Vella
18%
20
0
URBAN SCENE TYPOLOGY ANALYSIS
HAD WALKING TRIP YESTERDAY (%)
Horta-Guinardó
RISK OF POOR MENTAL HEALT (%)
HUMAN PERSPECTIVE
STREET VIEW IMAGE
URBAN SCENE TYPOLOGY from a
HUMAN PERSPECTIVE
Ciutat Vella WALKING TRIP (%)
Analysis street view images can study the city’s environment from a human perspective.
NO 3%
31%
97% YES 97%
Because it is not like studying the urban environment through satellite images or bird’s-eye views, street view images can really reflect the urban scene that people see.
HEALING CITY STREET VIEW IMAGE PANORAMIC SEGMENTATION detect the
Panoramic segmentation includes two aspects, one is instance segmentation and the other is semantic segmentation. Instance segmentation can analyze the number of different types of objects in each street view image, such as person amount and vehicle amount. Semantic segmentation can analyze the area ratio of different things, such as the proportion of the sky in the entire image.
amount of
each different type object.
PERSON
BICYCLE
VEHICLE
URBAN FURNITURE
But I cannot use all the features for the next analysis. Because some can’t reflect the scene of the city well. So I chose nine features, including four quantitative features and five proportional features.
INSTANCE SEGMENTATION
STREET VIEW IMAGE
BARCELONA STREET VIEW IMAGES - FEATURE SPATIAL DISTRIBUTION PANORAMIC SEGMENTATION
SEMANTIC SEGMENTATION
detect the
ratio
ROAD
SIDEWALK
BUILDING
VEGETATION
PERSON
BICYCLE
VEHICLE
URBAN FURNITURE
ROAD
SIDEWALK
BUILDING
VEGETATION
of
different types of physical environment area.
SKY
SKY
STREET VIEW IMAGE PANORAMIC SEGMENTATION - FEATURES
instance segmentation
person
bicycle
bicycle
car motorcycle bus truck
vehicle
road
semantic segmentation [stuffs]
pavement dirt floor platform building wall tree grass sky
road sidewalk building vegetation sky
BARCELONA STREET VIEW IMAGES - FEATURE SPATIAL DISTRIBUTION - CITY CENTRE PERSON
features
street view
urban furniture
stabled physical environment
bench umbrella dinning_table chair
DOG
moving objects
[things]
person
BIRD
Passeig de Gràcia La Sagrada Família
La Rambla
BICYCLE
VEHICLE
HEALING CITY URBAN FURNITURE
Use these nine features for cluster analysis to get the urban scene typology. ROAD
SIDEWALK
However, there are many methods for clustering. kmeans can handle a large amount of data well, and hierarchical clustering can well show the hierarchical relationship between various categories. So I use these two clustering methods together.
KMEANS CLUSTERING KMEANS CLUSTERING (K=40) PCA (dimensionality reduction) visualization
USE KMEANS FOR PRELIMINARY
BUILDING
VEGETATION
CLUSTERING
SKY
AND OBTAIN 40 CLUSTERING CENTERS FOR THR NEXT HIERARCHICAL CLUSTERING
First divided all the data into 40 groups using kmeans clustering.
URBAN SCENE TYPOLOGY ANALYSIS
PANORAMIC SEGMENTATION
PERSON BICYCLE VEHICLE URBAN FURNITURE ROAD SIDEWALK BUILDING VEGETATION SKY
CLUSTER ANALYSIS
KMEANS + HIERARCHICAL
URBAN SCENE TYPOLOGY
HIERARCHICAL CLUSTERING HIERARCHICAL CLUSTERING
6 MAIN CLUSTER
19 SUB - CLUSTER
40 KMEANS CLUSTERING CENTERS
Afterwards, I used hierarchical clustering, using the 40 cluster centers as input, to obtain 6 main clusters (elbow chart shows that clustering these data into 6 categories will have a better result) and 19 sub-clusters, and also to obtain the relationship between these main clusters and sub-clusters.
HEALING CITY BARCELONA URBAN SCENE TYPOLOGY
NARROW ALLEY PEDESTRIAN AREA ON THE ROAD 5%
VEHIC
N BA RE UR ITU N R FU
LE CL CY BI
MIX STREET
7%
A LK EW SID
HIGHWAY or MAIN ROAD
PEDESTRIAN AREA TRAFFIC - DRIVEN ROAD or or ROAD ROAD WITH PARKING WITH HIGH SPACE GREENING RATE
PERSON
SQUARE or WIDE PEDESTRAIN STREET
ROA D
E
NARROW ALLEY or PEDESTRIAN AREA ON THE ROAD
17%
1%
SK Y
G
IN
ILD
BU
20%
14%
26%
N TATIO
8%
VEGE
17%
4%
15% PERSON
BICYCLE
VEHICLE
URBAN_FURNITURE
ROAD
SIDEWALK
BUILDING
View the main features and the street view images of each cluster, name each main cluster, and get the urban scene typology. NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
HIGHWAY MAIN ROAD
HIGH GREENING RATE PEDESTRIAN AREA or ROAD
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
MIX STREET
VEGETATION
SKY
BUILDING
BUILDING
BUILDING
BUILDING
HEALING CITY SQUARE WIDE PEDESTRAIN STREET
HIGHWAY MAIN ROAD 2%
VEHIC
LE
ROA D
ROA D
E
E CL CY
CL CY BI 1%
A LK EW
A LK EW SID
PERSON
PERSON
8%
SK Y
Y G
IN
VEGE ROAD
SIDEWALK
BUILDING
VEGETATION
SKY
PERSON
LD
N TATIO
URBAN_FURNITURE
BU I
VEHICLE
N TATIO
4%
VEGE
G
IN
LD
BU I
BICYCLE
20%
SID
1%
SK
PERSON
9%
VEHIC
N BA RE UR ITU N R FU
LE
BI
N BA RE UR ITU N R FU
BICYCLE
11%
VEHICLE
URBAN_FURNITURE
ROAD
SIDEWALK
BUILDING
VEGETATION
SKY
BUILDING
BUILDING
BUILDING
BUILDING
HEALING CITY TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
7%
VEHIC
LE
VEHIC
LE
ROA D
ROA D
E A LK EW
2%
26%
6%
SID
A LK EW SID
PERSON
PERSON
14%
SK
Y
Y
SK G
IN
VEGE
N TATIO
URBAN_FURNITURE
ILD
BU
VEHICLE
ROAD
SIDEWALK
BUILDING
VEGETATION
SKY
PERSON
N TATIO
G
IN
LD
BU I
BICYCLE
5%
VEGE
PERSON
15%
E CL CY
CL CY BI
N BA RE UR ITU N R FU
BI
N BA RE UR ITU N R FU
BICYCLE
5%
VEHICLE
URBAN_FURNITURE
ROAD
SIDEWALK
P
BUILDING
BUILDING
BUILDING
BUILDING
BUILDING
BUILDING
P
BUILDING
VEGETATION
SKY
HEALING CITY BARCELONA URBAN SCENE TYPOLOGY - ZOOM
MIX STREET
1%
VEHIC
N BA RE UR ITU N R FU
LE CL CY BI
ROA D
E
A LK EW SID
PERSON
15%
6%
SK Y
VEGE
N TATIO
VEHICLE
URBAN_FURNITURE
ROAD
SIDEWALK
BUILDING
VEGETATION
SKY
Sant MartÃ
Ciutat Vella
G
IN
LD
BU I
BICYCLE
8%
And because I study street view images, I can do more detailed research. For example, when I zoom into a certain area, I can use a smaller grid for research and obtain more detailed results, which can be used as a basis for urban environment improvement.
URBAN SCENE TYPOLOGY ANALYSIS SUMMARY PANORAMIC SEGMENTATION
CLUSTER ANALYSIS
ANALYZE & EXPLAIN THE MEANING OF EACH CLUSTER
NARROW ALLEY or PEDESTRIAN AREA ON THE ROAD
17%
KMEANS + HIERARCHICAL
BUILDING
BUILDING
P
PERSON
BICYCLE
VEHICLE
URBAN FURNITURE
ROAD
SIDEWALK
BUILDING
VEGETATION
SKY
SQUARE or WIDE PEDESTRAIN STREET
8%
HIGHWAY or MAIN ROAD
20%
PEDESTRIAN AREA TRAFFIC - DRIVEN ROAD or or ROAD ROAD WITH PARKING WITH HIGH SPACE GREENING RATE
14%
26%
MIX STREET
15%
HEALING CITY ANALYSIS OF URBAN SCENE TYPLOGY - EMOTION
STREET VIEW IMAGE - EMOTION ANAYLSIS
SELECTION OF VISUALLY STIMULATING IMAGES
UNCONSCIOUS & CONSCIOUS EMOTION
type - 1
selected images
DISGUST
unconscious emotion disgust
SCARED
scared
type - 2
happy sad
SAD street view image
HAPPY
street view image SCARED
scared
HAPPY
person bicycle vehicle urban furniture road sidewalk building vegetation sky
happy sad
type - 3
type - 4
select images according to different types of urban scene as input for visual stimulation experiments.
conscious emotion
regression coefficient
URBAN FEATURES
SAD disgust
MULTIPLE LINEAR REGRESSION
EMOTIONS
DISGUST
EMOTIONS
urban scene typology
FEATURE - EMOTIONREGRESSION ANALYSIS
BARCELONA URBAN SCENE TYPLOGY - EMOTION SELECTION OF VISUALLY STIMULATING IMAGES type - 5
VISUAL STIMULATION EXPERIMENT
type - 6
152 street view images (each sub-typology 8 images)
VISUAL STIMULATION EXPERIMENT
PSYCHOPY
STEP 1
street view image visual stimulation
show selected street view image
MULTIPLE LINEAR REGRESSION (URBAN FEATURE COEFFICIENT) DISGUST
STEP 2_1
STEP 2_2
unconscious emotion (facial emotion recognition)
conscious emotion
SCARED
PERSON
URBAN_FURNITURE
BICYCLE
VEHICLE
VEHICLE
PERSON
URBAN_FURNITURE
BICYCLE
BUILDING
SIDEWALK
SKY
SKY
SIDEWALK
BUILDING
ROAD
VEGETATION
VEGETATION
ROAD
SAD URBAN_FURNITURE
HAPPY URBAN_FURNITURE
PERSON
SKY
VEHICLE
ROAD
SIDEWALK
VEGETATION
ROAD
SIDEWALK
BUILDING
BUILDING
SKY
BICYCLE
VEGETATION
PERSON
BICYCLE
VEHICLE
urban emotion
HEALING CITY BARCELONA URBAN SCENE TYPOLOGY - EMOTION
DISGUST - URBAN SCENE TYPOLOGY
DISGUST HEAT-MAP
NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
DISGUST SPATIAL DISTRIBUTION NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
HIGHWAY MAIN ROAD
MIX STREET
DISGUST
SQUARE WIDE PEDESTRAIN STREET
HIGHWAY MAIN ROAD
+
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
MIX STREET
HEALING CITY SCARED HEAT-MAP
SCARED - URBAN SCENE TYPOLOGY
-
SCARED
NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
+
SCARED SPATIAL DISTRIBUTION NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
HIGHWAY MAIN ROAD
MIX STREET
HIGHWAY MAIN ROAD
HEALING CITY SAD HEAT-MAP
SAD - URBAN SCENE TYPOLOGY
-
SAD
NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
HIGHWAY MAIN ROAD
+
SAD SPATIAL DISTRIBUTION NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
HIGHWAY MAIN ROAD
MIX STREET
MIX STREET
HEALING CITY HAPPPY HEAT-MAP
HAPPY - URBAN SCENE TYPOLOGY
-
HAPPY
NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
HIGHWAY MAIN ROAD
+
HAPPY SPATIAL DISTRIBUTION NARROW ALLEY PEDESTRIAN AREA ON THE ROAD
SQUARE WIDE PEDESTRAIN STREET
PEDESTRIAN AREA WITH HIGH GREENING RATE ROAD WITH HIGH GREENING RATE
TRAFFIC - DRIVEN ROAD ROAD WITH PARKING SPACE
HIGHWAY MAIN ROAD
MIX STREET
MIX STREET
HEALING CITY URBAN SCENE TYPLOGY - EMOTION SUMMARY PANORAMIC SEGMENTATION
CLUSTER ANALYSIS
ANALYZE & EXPLAIN THE MEANING OF EACH CLUSTER
NARROW ALLEY or PEDESTRIAN AREA ON THE ROAD
17%
SQUARE or WIDE PEDESTRAIN STREET
8%
HIGHWAY or MAIN ROAD
20%
PEDESTRIAN AREA TRAFFIC - DRIVEN ROAD or or ROAD ROAD WITH PARKING WITH HIGH SPACE GREENING RATE
14%
26%
MIX STREET
15%
KMEANS + HIERARCHICAL PERSON
BICYCLE
VEHICLE
URBAN FURNITURE
ROAD
SIDEWALK
BUILDING
VEGETATION
SKY
MULTIPLE LINEAR REGRESSION
REGRESSION ANALYSIS
VISUAL STIMULATION EXPERIMENT
SELECT STREET VIEW IMAGES ACCORDING TO URBAN SCENE TYPOLOGY
FUTURE WORK
PREVIOUS ANALYSIS RISK AREAS
MORE DETAILED ENVIRONMENT - EMOTION CORRELATION
REFORM
CCTV DETECT (EMOTION , AGE, GRNDER)