Urban Informatics and Planning Portfolio Bayi Li
Contents Researches Geographic Models Urban Land Use Layout Agent-based Model 3 - - - - - - - - - - - - - - - - - - - - - - - - - - - Based on Baidu POI Data 8 - - - - - - - - - - - - - - - - - - - Industrial Land Use Decision Making
Designs Urban & Architecture 12 - - - - - - - - - - - - - - - - - - - - - - - - - - - Automated City System 15 - - - - - - - - - - - - - - - - - - - - - - - - - The Humanity Purification
Other Works 21 - - - - - - - - - - - - - - - - Research Samples & Data Visualizations
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Urban Land Use Layout Agent-based Model Based on Baidu POI Data Case Study: Qingdao Liuting International Airport, Shandong, China Group Member: Bayi LI, Wenhan FENG and Lingda KONG Won a Second Prize in the 3rd Planning Decision Support Model Design Competition and Selected in Conference Proceedings and Oral Presentations for 16th International Conference on Computational Urban Planning and Urban Management (CUPUM) Abstract. The demand for dynamic land use adjustment in urban built-up areas is getting increasing but the prediction of it still have low prediction accuracy. We aimed to provide support for land use layout adjustment by constructing city in-formation modeling (CIM). Based on the analysis of Baidu POI data in urban built-up areas, which was identified by satellite imagery, the relative distance and quantity of various activities (residence, work, recreation) within the city were cal-culated. Taking it as the agent’s behavioural factors, through the agent-based model (ABM), the land use layout was generated inside the target parcel. The whole study took the urban renewal of the Liuting Airport area in Qingdao as a case. We combined big data and ABM as well as multiple techniques and analytical methods. The results can reflect the requirements of regional land use relatively objectively, making the internal function allocation of the city more reasonable.
Satellite imagery
Baidu POI Database Calculate the Relative Distance Frequency Distribution
K-means Indentification with Unsupervised Learning
Land Boundary Extraction Unit
of Different Land Use Determine the Scope of Analysis
Decision Rule Extraction Unit Spatial Data Analysis
Effective Range of Calculation
Metaspace Area
Land Use Evolution Rules
Buffer Analysis of Site
Behavior Rules
Map
Netlogo Model Deduced Multiple Times
Statistics Unit Support
Land Use Layout Decision
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Study Area: Liuting Airport It is 14.96 km2 in area, which locates in the northern part of Qingdao’s central city, east to Qingyin Expressway, west to Shuangyuan Road, south to Baisha River and north to Wenyang Road. In 2019, Liuting Airport area will be planned into a new financial city business center, replaced by a new airport. Because of the need for detail planning, and the urban texture of the surrounding area is relatively complete. It is suitable to utilize the model for land layout simulation.
Fig.1 The Scope of Study Area
Part I. Data Process A Clustering Method Based on K-Means Algorithm Extracting built-up area from image classification, we can ensure a subsequent analysis that the behavioral rules are consistent with the developmental characteristics of the urban built-up area where the target parcel is located. POI data processing Then we collected the POI data in the urban built-up area and categorize the data into three primary urban activities (residence, work, recreation).
Fig.2 the Extraction of Built-up Area Data Source: Google satellite Image
Residence
Recreation
Since people starts their day from residence in general, the relative distance is counted by reference to residence. Then we calculated and visualize the relative distance of residence and residence, residence and work, residence and recreation. The calculation method is as shown in the figure (Fig. 4).
Work
Fig.3 POI Data of the Study Area Number of residence: 5196; work : 38326, and recreation: 883 Data Source: Baidu Map Database
Fig.4 the Calculation Method of POI Data
A Clustering Method Based on K-Means Algorithm Calculate the relative distance between these categories and calculate the relative distance frequency distribution (class interval is 100m). so the data range under 5100 will be considered valid (before peak for first time). Fig.5 the Distribution of POI data The graph in the back is full data: 0-47199 (m), the graph in the front is scope of first peak: 0-5199 (m).
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The functional meta-space scale of the POI is determined by the data from the first 100m distance. And we observed that the increase in growth rate has been stabilized after 40 meters (Fig.7). From Fig. 8, the intersection coordinate of the “work” curve and “residence” curve is (1421, 0.0057). The intersection coordinate of “recreation” curve and “residence” curve is (157, 0.0014). The intersection coordinate of “recreation” curve and “residence” curve is (3463.89, 0.0064). Conclusion:
Fig.6 the Distribution of POI data (0-100m) (a) the Amount of Activities; (b) the Growth Rate; (c) the Accumulated Amount of each Value; (d) the Growth Rate of Accumulated Amount
From Fig.8, we divided POI data into four intervals by distance to residence: “recreation” distribution frequency dominants from 0 to 157; “residence” distribution frequency dominants from 157 to 1421 “work” distribution frequency is the dominants from 1421 to 3464 “recreation” distribution frequency dominants again after 3464.
Fig.7 the Cumulative Growth Rate (0-100m)
Fig.8 the Relative Distance Distribution of POI the graph in the back is recreation to residence, the graph in the front is work to residence.
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Part II. Simulation in NetLogo Behaviors of “agent”
Import Map
- “judgment” The “agent” will check the number of patches for each activity around its location to determine whether it meets the frequency distribution rules based on POI data.
Adjustment Effort Agent Properties Set up Quantity
Behavior Rules
Agent Detect Whether the Land Layout Conform the Rules
Yes
Random Motion of Agents
Quantity
No
Deduced Multiple Times
Statistic Unit
Target Plot Complete Evolution
Support
Land Use Layout Decision
Evolutionary Result
- “move” When a “judgment” is over, the agent moves to another location randomly. The distance of the motion is the input parameter “agents_step.” These two behaviors will continue to execute until the entire target area is stable.
Land Layout Integration Unit
Behavior for “residence”, “work”, and “recreation”
Deductive
Land Use Layout
The map includes the three kinds of patch agents: “residence”, “work”, and “recreation”. Their behaviors include “change,” “adjust”, and “integrate.” And, it has the input parameter “adjust_strength.” These patch agents did not involve in any behavioral actions during the evolution. The behaviors are as follows:
Fig.9 the Rules of “adjust.”
- “Change” is the conversion of the patch agent following the command of the “agent” agent after the “agent” agent judges. - “Adjust” is when a patch has been converted, the surrounding patches are also converted. The strength of the conversion can be adjusted as needed. This feature can increase the integration of regional activities and avoid over-fragmentation of results. The rules of “adjust” are as shown in the Fig.9. - After the evolution is over, users can use the “integrate” to sharpen the boundaries between activities. Thereby it makes the results more identifiable. The rules are shown in Fig.10. Each patch checks the number of patches of different colors around itself, and when the amount is different, it adjusts to the color of more patches.
Fig.10 the Rules of “integrate”
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Simulation We inputted all the parameters and the land use data (Qingdao city Master Plan: 2011 - 2020) into NetLogo. One evolution process is shown in Fig.11. “agents_num”=100, “agents_step”=100, “adjust_ strength”=1 Finally, a batch simulation is to observe the behavior of the entire system. - Integrating the outcomes of 30 simulations, there is a certain concentration of various activities, but the boundary of each one is vague. - Integrating the outcomes of 500 simulations, the aggregation of points can be obtained relatively clearly. (Fig.12): The color of each activity is extracted according to the color channel of the image, and the following results are obtained (Fig.13):
Fig.11 the Start and End of one Simulation in NetLogo
Features in the layout: - There are nine sections for “work”: six in the east and three in the western. There are five sections “recreation”: one in the east and four in the western. - “Residence” is sparse, and the west is more than the east. After 500 times, the percentage of them is 36.797% (work), 31.378% (residence), and 31.825% (recreation).
Fig.12 the Aggregated Simulation Outcomes the map in the left is the aggregated 30-simulation outcome, the map in the right is the aggregated 500-simulation outcome.
Conclusion
To summarize, we can conclude that the “work” activities of the Liuting Airport area are the main functions, and observed the suitable location for “work” and “recreation” activities.
Work
Residence
Recreation
Fig.13 the Simulation Outcomes in Categories
ABM based on POI data can generate the land use layout to support land use planning decisions quickly and relatively objectively, and the model can be integrated and adjusted according to needs. However, urban land use evolution was an extremely complicated process. This research made simplifications to the real city. The accuracy is still uncertain due to the problem of land use layout scale. Therefore, it is necessary to adjust accordingly according to the characteristics of sites for practical application purposes. 7
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Urban Land Use Layout Agent-based Model Industrial Land Use Decision Making Case Study: Qingdao Liuting International Airport, Shandong, China Winter 2019 | Individual Work Abstract. One of the bases to ensure a sustainable city is the exploration of the driving mechanism for urban expansion, as well as how to manage the industrial and oth-er functions is driven by industrial development and human settlements. Howev-er, there has been little focus on how the industrial land is planned from the per-spective of complex systems. This research conducted analysis and build under-standing of rules of collaboration behavior. More specifically, this research was to combine factors of industrial land planning such as land use control policy, de-veloper’s cost-based decision, and homebuyers’ living demands. To achieve this aim, a multi-method approach was utilized, including the evaluation of traditional land use suitability on GIS, and agent-based modelling, exploring the position and role of the government, developer, and the homebuyers. Ultimately, by gen-erating a land use layout for the Tianfo Health Industrial Park in Linyi, Shandong, this research develops a universal simulation framework and a multi-agent model (LULO-IP) based on the NetLogo, to support Planning decisions.
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Fig.1 The Research System Flowchart
Study Area: Tianfo Health Industrial Park, Shandong Province Currently, the site is mainly for argriculture, surrounding by mountains on three sides and reservoirs inside, the natural conditions are superior. Acoording to the comprehensive plan, it will be developed with health industries, and provide residence for the aged.
Fig.2 The Scope of Study Area
Health agedness is an aim of aging social development, agedness health industry is precondition to realize health aging. The quality of human settlement and the development of industry is of same importance. Hence, ABM can help decision-maker to meet the need of both sides.
The Environmental Factors Layer The cost of land development is correlated to the result of the land-use suitability evaluation. Specifically, we collected data of physical entities, accessibility, flow accumulation, gradient, slope, elevation, ecological buffer, and landscape, then performed the spatial analysis to evaluate each factors respectively on ArcGIS 10.2. (see Fig. 3) The weight of each factor was set up on the development of industrial land, and to obtain the weighted score of the suitability. Residential Location Selection Decision
Fig.3 Industrial Land use Suitability Evaluation with Weighted Impact Factors
According to Tab.1, the 15-minute neighborhood, emphasizes accessibility to various basic service facilities within a walking distance of 15 minutes, which is about 800 meters. Residents make independent decisions in accordance to the 15-minute residential area residential land control index, Tab.1 the Index of 15-minute Pedestrian-scale Neighborhood (Standard for Urban Residential Planning and Design GB50180-2018) the housing area constitutes 48% ~ 61% of the residential land area, and green space accounts for 7% ~ 16%, and public service facilities account for 12% ~ 23%. Residents would check the accessibility to enough public green spaces and public service facilities and make housebuying decision. 9
Government planning regulation The government needs to regulate or restart the gaming process from the perspective of the comprehensive benefits of land use when: • The intensity of industrial land development exceeds the limit of land development; • The need land-use function within the 15-minute neighborhood exceeds a acceptable number to residents; • Over-concentration of housing land, which cannot promote the optimization of the interests of other entities in the area. The Driving Force System The change of land use layout is driven by a force system, based on the correlation of the increase amount g and the initial patch amount:
g t (n)= n + ax
(1)
t is iteration times; n is the initial value of land-use patches (initial value is 0); a is a constant, and correlated to the value of x; x is the changing number of patch in one iteration.
In the model, the patch can only change adjacent agents, so the value of x ranges from 1 to 4. (see Fig. 4) When x = 1 or x = 4: a = x; when x = 2 or x = 3: a = x in the first iteration, a = x-1 from the second iteration. Because of the stochastic direction of patch expanding, there will be a more complex graphical change. For the function is characteristic to the formula of a fractal system, the outcomes of it are fractal. The total amount of certain patches in the evolution is: Fig.4 The General Rules of the Driving Force System
(2)
In the model, the control of the number of various patches influences the evolution result directly. The three types of agents sense the number of patches in real-time and adjust the number of patches accordingly. This process is based on a judgment func-tionOnly when the ratio of land use is lower than the expectation, the dynamic formula starts to work.: (3)
(4)
N is the sum of number of patches. R is the ratio of expected land to total land, that is, the capacity limit of various functions in land development.
The Model Design The user interface includes three parts, parameter input section, demonstration section, and observation section: • Area 3 is the observation section, which plots the sum of the incremental number of land uses (“Total”), the incremental number of different kinds of land (“yellow”, “green”, “red”, “blue”), number of land and expected value (“yellow2”, “green2”, “red2”, “blue2”) and the difference value between several land types (“rb”, “rg”, “gb”, “yg”, “yr”, “yb”, “r-ygb”).
Fig.5 The User Interface of the LUL-IV Model (overview) 1-input parameter section, 2-demonstration section, 3-observation section Digital Scale: “Zoom” refers to the side length of a patch in meter; Agent Parameters: “num” refers to the number of agents; “step” refers to the moving distance in meter; “ds”, “gs”, and “rs” refer to agent: developers, governments, and residents; “rate” refers to the expected ratio of different types of land; “r” refers to industrial land in red; “y” refers to housing in yellow; “g” refers to green spaces in green; “b” refers to public facilities in blue.
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Fig.6 Example results Top – First outcome & Bottom - Second outcome
Conclusion A land-use layout was generated to meets the requirements of the simulation dynamics framework. This layout can be used to support the planning of the Tianfo Industrial Park. Firstly, the number of function is basically matched the expectation. Second, in the land layout generated by aggregating 500 simulations, we can see that (Fig.7):
Fig.7 Model Outcomes (Left - Aggregated Outcome of 500-time Simulations & Right - Clustered Outcome)
• The boundary between the different land uses assume organic form, and there are interspersed between each other; • Industrial land (orange) is agglomerated, with two cores distributed in the north and south; • The land for housing mainly assume planar distribution; • The green space and public facilities are dispersed in points; • The distribution of land for housing, green, and public facilities assumes even distribution. Discussion The interaction between the various agents is complex and uncertain. Still, there are some reasons to explain it according to the driving force mechanism of the dynamic system. The distribution of industrial land matches the land suitability results mostly but not strictly match. This is mainly caused by gaming between residents and developers under government control. Therefore, land for housing also occupies a certain percentage of high-quality areas. The distribution and the shape also has a great relationship, especially between housing area and green land and public facilities. This lay-out can meet the basic requirements of a 15-minute life circle. If it can be simulated at different bases, it can evolve through different bases. The comparison of the results should be able to discover more basic laws in the model evolution, which will help to improve the model in future studies. 11
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Automated City System “Cities are becoming computable and automated at every level of their operation.” Xuhui, Shanghai, China Summer 2019 | Team Work Group Member: Bayi LI, Wenhan FENG, Xiaoran GUO Role in Team: Concept Development, Data Analysis, Spatial Design Won a Third Prize in Shanghai Urban Design Challenge Competition
Interactive QR Code
The automated city system is to pursue a dynamic balance of urban control and autonomy. Referencing the concept of automation, it considers the city as a pipeline system of citizens and the networks. The proposal aims to build an automated community and proposes targeted strategies through analysis of the data. At the same time, this flexible and changeable dynamic control will not affect the citizens’ daily life, when reaching the maximum utilization of machinery and technology to bring convenience to the citizens. In the specific spatial design, we used multiple layers of data to quantify physical spaces, human behaviors, and the interaction between these two.
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Automation Automated City System Quantification Quantitative Analysis Design Urban Furniture Design
Shared Bike Statistics
Controllability
Citizens
Autonomy
Urban Furnitures
Urban Built Environment System
Analysis of current land-use layout Land Use Expectation Street Space Quantification Autonomous Interactable Isolated
Corner Parks
Lightening
Water Recycle
Taffic Control
Transportation System
Green Building
Convenience
Public Space System
Green Street
Entertainment
Public Service System
Non-motorized
Trash Recycle
Environment Sensing System
Passive Interactive Proactive
The Average Daily Metro Traffic Data Source: Department of Transportation, collected from 05/01/2019 to 06/01/2019.
Data Source: Mobike Co.
Weekday Weekend Holiay
Yishan Rd.
XujiaHui
Shanghai Stadium
Regulatory Detailed Planning
Underground Entrances and Exits
Green Spaces
Building Height
Light Environment Simulation
Thermal Environment Simulation
Where People in this area come from?
Where People in this area head to?
Thermal Environment Assessment
Light Environment Assessment
Wind Environment Assessment
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Quantification of Socioeconomic Factors Categorizing POI data into three main sections, leisure, service and work. And then evaluating POI by the density, accessibility, and hotspots. Finally, the influence degree of three categories on the site is generated.
POI Kernel Analysis
Accessibility to POI
POI - Influence Degree of Leisures
Hotspots of Public Services
POI - Influence Degree of Public Services
Hotspots of Companies
Hotspots of Leisures
POI - Influence Degree of Companies
Quantification of Physical Factors Identify physical elements of the street, as to extract and quantify different sections of the street.With space syntax, drawing the connectivity and isovist graph to identify the street space structure.
Spatial Connectivity
The Influence Degree of Buildings
Space Isovist
Isovist Perimeter
The Influence Degree of Plants
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Isovist Drift Magnitute
Isovist Drift Angle
Influence Degree of Views
after evaluate the design outcomes, the accessibility of each function is improved, and the scores at all the levels are more balanced this way.
after evaluate the design outcomes, a number of green plants and landscape are applied to improve the space quaility of the entire street.
Slow down the traffic. Create a walking friendly environment. Value experience over objects, and the sensual pleasures of life and an appreciation. Slow Malleable Changable urban furnitures to create different functions and services. people takes design responsibilities into their own hands. Adaptable Because the development includes a wide variety of uncertainties, an adaptive approach to planning predefined outcomes are being explored. Recyclable How to planning cities effectively for waste? every piece of the system is connected to the next. it’s collected, stored, sorted, and put into new use. Tolerant The tolerant street adds value to its urban environment, where city squares and recreational areas facilitate the coexistence of different people.
Smart - Trash Can Intelligient
In the mid of 2019, Shanghai releases new garbage sorting guidelines, and this trash can could classify garbage through image recognition system, to improve sorting efficiency.
Recyclable - Water Recycle System Seepage To adapt the characters of the urban waterlogging area in Shanghai, we design a structure that can collect natural water, which can be infiltrated into the filtering devices, and then it will seepage into the storage tank, and is pumped to the fountains at every corner. For this reason, children can enjoy clean and recyclable water for fun and encourage a sustainable urban life.
Tolerant Space - Folding Space Meditation Set up foldable devices in the corner of the street, to make full use of the scattered spaces.
Malleable Space - Entertainment Cube Interaction By folding or unfolding the devices, people can create a private, semi-private or open spaces. Movable structure can connect multiple cubes.
Adaptable - Street Light Sustainable The foldable wings can collect solar power during the daytime, and utilize it to light the street at the night. Besides, when it collects enough power, it can be shrunk under the ground, and the screen can provide fun for pedestrians when waiting for the green light.
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The Humanity Purification “I want a life, but not being alive.” Bachelor of Engineering in Rural and Urban Planning Spring 2018 | Team Work Team Member: Bayi LI Hui ZHAO Role in Team: Design Concept Development Type Analysis Manhatten, New York, America
In Manhattan, a lot of high-rises are constructed according to the needs of the function. Based on the land use of Manhattan Mid-town, about 80% are commercial and office buildings. And according to the population density and flow distribution of Manhattan, every day the commuters ply from home and office. The great pressure and fast pace make the whole society be lack of communication. On the road or in the subway, all people look down on the cell phone. However, faith seems like the most sensual thing. Combined them together gives the officers a chance to self-examine. Therefore, choosing the site at Rockefeller Center which is a modernist architecture in the center of Manhattan Mid-town, designed related to the function. And Select arch, which is the most represented element in faith. Using the element, create a different sense for the visitors.
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Other Works This section contains samples of analysis in different perspectives,
R=5
such as space syntax, kernel analysis, or network analysis. R=2
These graphs come from these projects: - The evaluation of the built environ-
R=n
ment of Zhangjiang industrial parks (Honorable Mention in NSC-URPEC Urban and Rural Comprehensive Practice Report Competition) Depth Map
- The analysis report on Mogan Mountain’s life and public facilities. (Led by Prof. Liu Yong)
Distribution of Industrial Parks in Shanghai
- The research on CAZ area identifying in Pudong, Shanghai. the Space Syntax Analysis of Zhangjiang, Shanghai
(In Citory Ltd.)
Holiday 03:00
Holiday 12:00 Buffer Accessibility Evaluation of Two Schools in Mogan Area
Weekday 03:00 weekday 12:00 Comparison in Distribution of People at Holiday and Weekday (Data collected at 05.01.2019 and 05.10.2019)
Road Accessibility Evaluation of Schools A
Density Map of Road in Pudong, Shanghai
Residence-Commute Distribution for Zhangjiang
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Road Accessibility Evaluation of Schools B