This book introduces a planning support system called Strategic Spatial Plan Support System (SSP-SS) to visualize population growth and predict energy demand, land use, and waste discharge resulting from urbanization. By analyzing policy interactions between household agents, the book uses SSP-SS to visualize policy effects on urban areas during stages of growth and decline. Simulations are created based on these policy outcome assessments, taking into account the influences of energy and resource consumption on sustainable development in urban environments. The book is geared towards researchers, universities, and urban policy makers.
The book begins by presenting a framework of urban growth simulation, and introducing SSP-SS. Then, household lifecycle and relocation models are employed for simulating policy impacts on urbanization, and investigating the impacts of spatial strategic planning. Several projects are assessed using agent-based modeling including shopping centre construction, day-care service for aging populations, and shelter accommodation capacities for earthquakes and other disasters. The final chapters discuss water and energy management, the environmental impacts of demand and consumption, and future recommendations for sustainable development and policy implementation.
Introduces Strategic Spatial Plan Support System (SSP-SS) to visualize population growth and predict energy demand, land use, and waste discharge resulting from urbanization.
Analyzes policy effects on urban areas during stages of growth and decline.
Discusses the influences of water and gas consumption on environmental issues in urban areas for sustainable development.
Author(s): Yan Ma, Zhenjiang Shen
Series: Advances in Geographic Information Science
Publisher: Springer
Year: 2022
Language: English
Pages: 228
City: Cham
Preface
Acknowledgments
Contents
Chapter 1: Introduction to Agent-Based Modeling and Its Application to Policy Decision-Making
1.1 Introduction
1.2 Models for Analyzing Urban Spatial Strategic Plans
1.2.1 Models for Analyzing Urban Policies
1.2.2 MAS and CCA for Urban Modeling
1.2.3 Conclusion for Literature Review
1.3 What Is Agent-Based Models
1.3.1 The Definition of ABMs
1.3.2 How to Build a ABM
1.3.2.1 The Agents
1.3.2.2 The Environment
1.3.2.3 Agent’s Decision Rule
1.3.2.4 Agents’ Interaction
1.4 Agent-Based Models for Decision Support of Urban Planning Policies
1.4.1 ABMs for Individual-Based Behavior Research
1.4.2 ABMs for Policy Decision-Making
1.5 Discussion
References
Chapter 2: The Environment for Accommodating Agents and Representing Urban Planning Conditions
2.1 Introduction
2.2 Hypothetical Urban Space for Accommodating Urban Planning Conditions
2.2.1 An Environment for Representing Planning Urban Form
2.2.2 An Environment for Representing the Urban Planning Condition
2.3 Distributing Agents on the Environment and its Validation
2.3.1 Factors Affecting Spatial Distribution
2.3.2 Land Use Zoning Constraints
2.3.3 Housing Suitability of Each Cell
2.4 How HDF Works in an ABM
2.5 Validation of HDF for Urban Planning Conditions
2.5.1 The Variogram Model Using for Validation of HDF
2.5.2 Sub-area Radiuses Using as Validation Parameters
2.5.3 Using the Variogram for Validation
2.6 Discussions
References
Chapter 3: Simulation of Urban Growth and Household Aggregation for Planning Support of Local Spatial Strategic Plan
3.1 Introduction
3.2 CCA Model for Representing Multi-driving Forces in Urban Spatial Dynamic Simulation
3.2.1 Combined System for Decision-Making Support of Local Spatial Strategic Plan
3.2.2 The Framework of the Combined System
3.2.3 System Implementation and Results
3.3 CCA Model for Supporting Decision-Making of Territory Spatial Planning
3.3.1 The Establishing of Territory Spatial Planning and Basical Technical Needs
3.3.2 Coupling the “Double Evaluation” with CCA Simulation
3.3.3 Methods for the Coupled Simulation Model
3.3.4 Case Study Area
3.3.5 Recognizing the Spatial Constraints on Urban Spatial Dynamic
3.3.5.1 Building the Evaluation Index
3.3.5.2 Evaluation of TDS
3.3.6 Simulating Construction Land Resources Expansion During Urban Spatial Dynamic
3.4 Discussion
References
Chapter 4: Agent-Based Simulation of Household Residential Relocation and Decision-Making Support of Downtown Revitalization
4.1 Introduction
4.2 Existing Researches for Household Residential Location Choice
4.3 Approach of Agent-Based Simulation for Downtown Revitalization
4.3.1 Simulation Framework
4.3.2 Framework of Agent-Based Relocation Model
4.4 Model Design
4.4.1 The Agents
4.4.2 Agents’ Decision Rule
4.5 How to Make a Relocation?
4.6 Evaluation of Residential Utility and Satisfaction
4.6.1 Equations for Calculating Residential Satisfaction
4.7 Agents Evaluate on the Utilities of Candidate Cells in the Urban Space
4.7.1 Agents’ Interactions
4.8 Environment and Parameters for Simulation Configuration
4.8.1 Hypothetical Urban Space and Household Agents
4.8.2 Parameters for Satisfaction Evaluation and Utility
4.8.2.1 Questionnaire of Residential and Housing Environment in Kanazawa City
4.8.2.2 Parameters for Household satisfaction Evaluation and Utility Calculation
4.8.2.3 Initial Parameters for Simulation
4.9 Model Test
4.9.1 Sensitivity Test of Household Lifecycle
4.9.2 Satisfaction Threshold for Making Decision on Relocation Choices
4.9.3 Policy Impacts on Downtown Revitalization
4.10 Model Validation
4.11 Discussions
References
Chapter 5: Agent-Based Simulation for Decision-Making Support of Spatial Strategy for Large-Scale Shopping Center Development
5.1 Introduction
5.2 Agents in the Urban Space and Their Behavior Rules
5.2.1 The Framework of ShopSim Model
5.2.2 Planner Agent
5.2.3 Large-Scale Shop Developer Agent
5.2.4 Shop Agent
5.2.5 Household-Agents
5.2.6 The Interactions Between Agents
5.3 Modelling the Transportation Mode in the Shop-Selection Model
5.4 Sensitivity Test and Validation
5.4.1 Initial Settings
5.4.2 Sensitivity Test
5.5 Analysis on the Large-Scale Shopping Center Development Policy
5.5.1 Case Study Area
5.5.2 Simulating the Spatial Patterns and Market Shares
5.5.3 The Hypothetical Urban Space
5.5.4 Policy Scenarios
5.5.5 Model Test
5.5.6 Comparison of Different Scenarios
5.6 Discussions
References
Chapter 6: Agent-Based Simulation on Residents’ Travel Mode Choice for Local Transportation Development Strategy
6.1 Introduction
6.2 Methodology of TDM Decision-Making Support
6.2.1 Planning and Decision-Making Process
6.2.2 The Framework of RB Model
6.3 Model Formulation
6.3.1 The Environment of RB Model
6.3.2 Resident Agent
6.3.3 Bus Agent
6.3.4 Agents Interaction
6.4 Variables and Parameters for Model Application
6.4.1 Case Study Area
6.4.2 Variable Description
6.4.3 Initial Conditions for the Simulation
6.5 Model Test and Validation
6.5.1 Stability Test
6.5.2 Sensitivity Test
6.5.3 Validation by Real Data
6.6 Scenario Analysis
6.6.1 Scenario Design
6.6.2 Strategy 1: Bike-Sharing Area Control
6.6.3 Strategy 2: Improve Bus Service
6.6.4 Strategy 3: Improve the Bus Station Environment
6.7 Discussions
References
Chapter 7: Agent-Based Modeling for Decision-Making on the Strategy of Community Senior Activity Center Development
7.1 Introduction
7.2 Planning and Decision-Making Process for Community Senior Activity Centers Based on ABM
7.3 CFE Model Design
7.3.1 Agent Design
7.3.1.1 Family Agent (Senior Agent)
7.3.1.2 CFE Agent
7.3.1.3 Planner Agent
7.3.2 Agent Adaptive Behavior Selection Based on Individual Learning
7.3.2.1 Seniors’ Choice of Community Senior Activity Centers
7.3.2.2 Planning Management Decisions
7.4 CFE Model Application—A Case Study in Downtown Fuzhou City, China
7.4.1 Model Development Platform, Data Pre-processing and Parameter Attribute Determination
7.4.1.1 Model Data Pre-processing
7.4.1.2 Determination of Model Parameters
7.4.2 Model Testing
7.4.2.1 Stability Test
7.4.2.2 Sensitivity Test
7.4.2.3 Verification with Real Data
7.4.3 Model Simulation Results
7.4.3.1 Simulation and Prediction of Space Demand for Community Activity Centers in 2030
7.4.3.2 Simulation for Policy Scenarios to Explore the Planning Strategy of Community Senior Activity Centers
7.5 Conclusion and Discussion
References
Chapter 8: Decision-Making Support System for Low-Carbon Urban Master Plan of New Town
8.1 Introduction
8.2 Carbon-Emission Simulation and Low-Carbon Assessment for Master Planning
8.2.1 Simulation Framework
8.2.2 Model Simulation Design
8.2.2.1 Prediction and Identification of New Towns’ Spatial Structure Based on Land-Use Layout
8.2.2.2 Intracity Traffic Origin–Destination (OD) Prediction Based on Traffic Behavior
8.2.3 Building a Land Use–Carbon Emission Correlation Framework
8.2.4 Carbon-Emission Accounting Method
8.2.4.1 Calculating Carbon Emissions in New Towns
8.2.4.2 Accounting Methods for Carbon Emissions from Different Systems
8.2.5 Low-Carbon Assessment of Planning Schemes
8.3 System Development and Testing
8.3.1 System Framework
8.3.2 Development Results
8.3.3 System Testing
8.4 System Application
8.4.1 Overview of the Xixian New Town Master Plan of Xi’an City, China
8.4.2 Low-Carbon Assessment of the Xixian New Town Master Plan
8.4.2.1 Forecast of Total Carbon Emissions in Xixian New Town
8.4.2.2 Low-Carbon Measurement of Xixian New Town
8.4.3 Multiscenario Regulation of Xixian New Town Master Plan
8.4.3.1 Multipolicy Scenario Design
8.4.3.2 Policy Scenario 1: Optimize the Transportation Structure and Increase Public Transportation Use
8.4.3.3 Policy Scenario 2: Improve the Degree of Mixed Land Use in Monofunctional Clusters
8.4.3.4 Policy Scenario 3: Adjust the Land Structure and Increase the Proportion of Green Space in Built-Up Land
8.5 Conclusion
References
Index
