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E-raamat: Geospatial Analysis to Support Urban Planning in Beijing

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  • Formaat: PDF+DRM
  • Sari: GeoJournal Library 116
  • Ilmumisaeg: 14-Oct-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319193427
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Geospatial Analysis to Support Urban Planning in BeijingSuurem pilt
  • Formaat: PDF+DRM
  • Sari: GeoJournal Library 116
  • Ilmumisaeg: 14-Oct-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319193427
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This book describes a comprehensive framework of novel simulation approaches, conventional urban models, and related data mining techniques that will help develop planning support systems in Beijing as well as other mega-metropolitan areas. It investigates the relationships between human behaviors and spatial patterns in order to simulate activities in an urban space, visualize planning alternatives, and support decision making.

The book first explains urban space using geometric patterns, such as points, networks, and polygons, that help identify patterns of household and individual human behavior. Next, it details how novel simulation methodologies, such as cellular automaton and multi-agent systems, and conventional urban modeling, such as spatial interaction models, can be used to identify an optimal or a simulated solution for a better urban form.

The book develops a comprehensive land use and transportation integrated model used to explore the spatial patterns ofmutual interaction between human mobility and urban space. This model can help forecast the distribution of different types of households, rent prices, and land prices, as well as the distribution of routes and traffic volume based on an appraisal of labor demand and supply.

This book shows how geospatial analysis can be a useful tool for planners and decision makers to help in ascertaining patterns of activities and support urban planning. Offering both novel and conventional approaches to urban modeling, it will appeal to researchers, students, and policy makers looking for the optimal way to plan the d

evelopment of a mega-metropolitan area.
1 Geospatial Analysis and Application: A Comprehensive View of Planning Support Issues in the Beijing Metropolitan Area
1(18)
1.1 How Geospatial Analysis Help Planners
2(3)
1.1.1 Geospatial Analysis: Spatial Patterns and Urban Development
2(1)
1.1.2 Better Urban Form: Human Behaviour and Their Spatial Patterns
3(1)
1.1.3 Planning Support: Developing Tools for Planning and Design
4(1)
1.2 Urban Form: Spatial Patterns and Land Use Development
5(2)
1.2.1 Planning Targets and Raster Dataset for Simulating Urban Form
5(1)
1.2.2 Vector Database for Measuring and Simulating the Urban Form
6(1)
1.3 Urban Form: Human Behaviour and Their Spatial Patterns
7(4)
1.3.1 Open Data and Survey for Investigating Mechanism in Urban Space
8(1)
1.3.2 Big Data and Findings of the Human Mobility in Urban Space
9(2)
1.4 Planning Support and Its Future in Beijing
11(8)
References
12(7)
Part I Urban Form: Spatial Patterns and Land Use Development
2 Target or Dream? Examining the Possibility of Implementing Planned Urban Forms Using a Constrained Cellular Automata Model
19(20)
2.1 Introduction
19(2)
2.2 Method
21(5)
2.2.1 Form Scenario Analysis
21(2)
2.2.2 Identification of Urban Policy Parameters
23(1)
2.2.3 Constrained Cellular Automata (CA)
23(3)
2.3 Study Area and Data
26(6)
2.3.1 Study Area
26(2)
2.3.2 Constraints in Cellular Automata (CA)
28(2)
2.3.3 Planning Alternatives
30(2)
2.4 Results
32(3)
2.4.1 Identification of Policy Parameters
32(1)
2.4.2 Validation of Planning Alternatives
33(2)
2.5 Discussion
35(1)
2.6 Conclusions and Future Perspectives
36(3)
References
37(2)
3 Urban Expansion Simulation and Analysis in Beijing-Tianjin-Hebei Area Based on BUDEM-BTH
39(30)
3.1 Introduction
39(2)
3.2 Study Area and Data
41(1)
3.3 The Build of BUDEM-BTH Model
42(9)
3.3.1 The BUDEM-BTH Model
42(4)
3.3.2 The Status Transition Rule for BUDEM-BTH
46(5)
3.4 BUDEM-BTH Model Parameter Identification
51(3)
3.4.1 The Urban Expansion Analysis for BTH
51(1)
3.4.2 History Parameters Identification
51(2)
3.4.3 Model Validation
53(1)
3.5 Model Application
54(12)
3.5.1 BTH2020: The Urban Expansion Study for the Year 2020
54(3)
3.5.2 BTH2049: The Scenario Analysis for 2049
57(9)
3.6 Conclusion and Discussion
66(3)
References
67(2)
4 Parcel Direction: A New Indicator for Spatiotemporally Measuring Urban Form
69(22)
4.1 Introduction
69(3)
4.2 Approach
72(4)
4.2.1 Definition
72(1)
4.2.2 Computational Approach Based on GIS
73(1)
4.2.3 Measuring Urban Form in Three Spatial Scales
73(2)
4.2.4 Measuring Urban Form in the Temporal Dimension
75(1)
4.3 The Case Study of Beijing
76(5)
4.3.1 Study Area and Data
76(1)
4.3.2 Calculation Results
76(1)
4.3.3 Correlation Analysis of the Parcel Direction and Other Indicators
77(4)
4.4 Measuring Urban Form in Three Spatial Scales Using the Parcel Direction
81(4)
4.4.1 The Parcel Scale: Four Types of Urban Forms in Terms of the Parcel Direction
81(2)
4.4.2 The Zone Scale: Aggregated Indicators for Zones
83(1)
4.4.3 The Region Scale: Cluster Analysis of All Zones in the Whole Study Area
83(2)
4.5 Evaluating the Temporal Dynamics of Urban Form Using the Parcel Direction
85(3)
4.5.1 PD Calculation Results for the Historical Urban Form
85(2)
4.5.2 Comparing the Parcel Direction of the Planned and Historical Urban Forms
87(1)
4.6 Conclusions
88(3)
References
89(2)
5 V-BUDEM: A Vector-Based Beijing Urban Development Model for Simulating Urban Growth
91(24)
5.1 Introduction
91(4)
5.2 The V-BUDEM Model
95(7)
5.2.1 Constrained Cellular Automata
95(2)
5.2.2 Constraint Variables
97(1)
5.2.3 The Parcel Subdivision Framework
97(3)
5.2.4 The Simulation Procedure
100(2)
5.3 Model Application
102(6)
5.3.1 Study Area
102(1)
5.3.2 Data
102(4)
5.3.3 Yanqing 2020 Simulation
106(2)
5.4 Conclusion and Discussion
108(7)
References
109(6)
Part II Urban Form: Human Behaviour and Their Spatial Patterns
6 Population Spatialization and Synthesis with Open Data
115(18)
6.1 Introduction
115(2)
6.2 Study Area and Data
117(4)
6.2.1 Study Area
117(1)
6.2.2 The OSM Road Networks of Beijing
117(1)
6.2.3 POIs
118(1)
6.2.4 The 2010 Population Census of Beijing
119(2)
6.3 Approach
121(4)
6.3.1 The Proposed Process
121(1)
6.3.2 Generating Parcels
121(1)
6.3.3 Selecting Urban Parcels
122(2)
6.3.4 Identifying Residential Parcels
124(1)
6.3.5 Allocating Urban Population
124(1)
6.3.6 Synthesizing Population Attributes Using Agenter
124(1)
6.3.7 Model Validation
125(1)
6.4 Results
125(3)
6.4.1 Population Spatialization
125(1)
6.4.2 Population Synthesis
126(2)
6.5 Validation
128(1)
6.5.1 Validating Residential Parcels with Ground Truth from BICP
128(1)
6.5.2 Validating Population Density with Buildings
128(1)
6.5.3 Validating Population Attributes
129(1)
6.6 Conclusions
129(4)
References
130(3)
7 Spatially Heterogeneous Impact of Urban Form on Human Mobility: Evidence from Analysis of TAZ and Individual Scales in Beijing
133(22)
7.1 Introduction and Background
133(2)
7.2 Methodology
135(3)
7.2.1 Modeling Spatial Effects in Urban Mobility
135(2)
7.2.2 Mixed-GWR: Modeling Mobility on Multi-levels
137(1)
7.3 Data
138(7)
7.3.1 Study Area and Sample Data
138(2)
7.3.2 Computing Factors for Mobility Modelling
140(2)
7.3.3 Calibration of OLS, SAR and Mixed-GWR
142(3)
7.4 Empirical Results
145(6)
7.4.1 Primary Findings on the TAZs Level
145(5)
7.4.2 Primary Findings on the Individual Level
150(1)
7.5 Conclusions
151(4)
References
152(3)
8 Finding Public Transportation Community Structure Based on Large-Scale Smart Card Records in Beijing
155(14)
8.1 Introduction
155(1)
8.2 Data
156(1)
8.3 Methodology
156(2)
8.4 Results
158(8)
8.4.1 Identification of Communities on Weekdays and Weekends
158(3)
8.4.2 Comparison with Household Survey Data
161(1)
8.4.3 Hourly Patterns
162(2)
8.4.4 Identification of Community Structure on Commuting Trips
164(2)
8.5 Conclusions and Future Work
166(3)
References
167(2)
9 Profiling Underprivileged Residents with Mid-term Public Transit Smartcard Data of Beijing
169(24)
9.1 Introduction
169(2)
9.2 Related Research
171(3)
9.2.1 Urban Poverty of Chinese Cities
171(1)
9.2.2 Social-Economic Level Identification Using Trajectories
171(1)
9.2.3 Smartcard Data Mining
172(2)
9.3 Study Area, Data and Local Background
174(9)
9.3.1 Study Area
174(1)
9.3.2 Data
175(4)
9.3.3 Underprivileged Residents in Beijing and Their Mobility
179(3)
9.3.4 Most of Frequent Bus/Metro Riders in Beijing Are Underprivileged Residents
182(1)
9.4 Method
183(3)
9.4.1 Housing and Job Place Identification of All Cardholders
184(1)
9.4.2 Underprivileged Residents Identification and Classification
185(1)
9.5 Results
186(3)
9.5.1 Identified FRs and Their Dynamics During 2008--2010
186(3)
9.5.2 Evaluation on Underprivileged Degree
189(1)
9.6 Conclusions and Discussion
189(4)
References
191(2)
10 Discovering Functional Zones Using Bus Smart Card Data and Points of Interest in Beijing
193(28)
10.1 Introduction
193(3)
10.2 Overview of Study Area and Explanation of Data
196(2)
10.2.1 Overview of Study Area
196(1)
10.2.2 Data
196(2)
10.3 Method
198(7)
10.3.1 Blind Clustering of Bus Platforms
200(4)
10.3.2 Identification of Urban Functional Areas
204(1)
10.4 Results
205(6)
10.4.1 Clustering Results of Bus Platforms and Summary at TAZ Scale
205(1)
10.4.2 Function Identification
206(5)
10.4.3 Examination of the Results of Identification
211(1)
10.5 Conclusion and Discussion
211(10)
References
215(6)
Part III Planning Support and Its Future in Beijing
11 An Applied Planning Support Framework Including Models, Quantitative Methods, and Software in Beijing, China
221(14)
11.1 Introduction
221(2)
11.2 Methods for Establishing the Framework
223(4)
11.2.1 Requirement Analysis
223(1)
11.2.2 Selecting the Form of the Framework
224(1)
11.2.3 The Selection of Plan Elements
225(1)
11.2.4 The Selection of PSS Types
226(1)
11.2.5 Proposing PSSs for Plan Elements
227(1)
11.3 The New Framework
227(3)
11.3.1 The Framework and Detailed Descriptions
227(3)
11.3.2 The Online Query System
230(1)
11.4 Discussion
230(2)
11.4.1 Application and User Evaluation of the Frameworkin BICP
230(1)
11.4.2 Potential Contributions
231(1)
11.5 Conclusions and Next Steps
232(3)
References
233(2)
12 The Planner Agents Framework for Supporting the Establishment of Land Use Patterns
235(20)
12.1 Introduction
235(2)
12.2 Framework and Methods
237(5)
12.2.1 Basic Concepts
237(1)
12.2.2 The Framework of Planner Agents
238(1)
12.2.3 Obtaining Comprehensive Constraints
238(2)
12.2.4 Identifying Planning Rules
240(1)
12.2.5 Establishing the Land Use Pattern
240(1)
12.2.6 Evaluating the Land Use Pattern
241(1)
12.2.7 Coordinating Land Use Patterns
242(1)
12.3 Beijing Experiment
242(8)
12.3.1 Study Area
243(2)
12.3.2 Obtaining Comprehensive Constraints
245(1)
12.3.3 Identifying Planning Rules
245(1)
12.3.4 Establishing Land Use Patterns
245(4)
12.3.5 Evaluating Land Use Patterns
249(1)
12.4 Conclusion
250(5)
References
252(3)
13 Big Models: From Beijing to the Whole China
255
13.1 A Golden Era of Big Models
255(3)
13.2 Big Models: A Novel Research Diagram for Urban and Regional Studies
258(1)
13.3 Case Studies Using Big Models
259(11)
13.3.1 Mapping Urban Built-Up Area for All Chinese Cities at the Parcel/Block Level
259(3)
13.3.2 Simulating Urban Expansion at Parcel Level for All Chinese Cities
262(2)
13.3.3 Evaluating Urban Growth Boundaries for 300 Chinese Cities
264(3)
13.3.4 Estimating Population Exposure to PM2.5
267(3)
13.4 Conclusions and Future Directions
270
References
271
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