Modelling and Simulating Urban Residential Land Development in Jiading New City, Shanghai
Authored by Rongxu Qiu, Wei Xu, John Zhang, Karl Staenz
Date Published: 2018
DOI: 10.1007/s12061-017-9244-4
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Abstract
This study develops an agent-based and spatial genetic algorithm
framework called Population-driven Urban Land Development (PDULD) to
simulate urban land development and population dynamics. In the model,
household-life cycles promote their location and relocation desires and,
thus, form local housing market demand. Land developers and local
governments make optimal use of current land reserves to meet housing
demands. Land development in an area is treated as a multi-goal
optimization activity. Community cohesion theory is introduced into the
model to illustrate the influence of the population on the spatial
structure of urban land use. The study uses the Spatial Genetic
Algorithm to help find the best land development choices to achieve
social, economic, and environmental goals. The results show that the
model simulates population distribution quite well and interprets the
real land use at a neighborhood level with a reasonable accuracy. A
historic data comparison indicates that government policies and
increasing land prices have dominated the process of land development in
Shanghai based on a case study of Jiading New City.
Tags
Agent-based modelling
Population dynamics
GIS
transportation
growth
Cellular-automaton model
San-francisco
Region
Form
Conversion
Cellular
automata
Spatiotemporal dynamics
Shanghai
Urban land development
Urban modelling
Rural
china