A new framework for modeling urban land expansion in peri-urban area by combining multi-source datasets and data assimilation

Abstract:

Peri-urban area is a new type region under the impacts of both rural Industrialization and the radiation of metropolitan during rapid urbanization. Due to its complex natural and social characteristics and unique development patterns, many problems such as environmental pollution and land use waste emerged, which became an urgent issue to be addressed. Study area in this paper covers three typical peri-urban districts (Pudong, Fengxian and Jinshan), which around the Shanghai inner city. By coupling cellular automata and multi-agent system model as the basic tools, this research focus on modelling the urban land expansion and driving mechanism in peri-urban area. The big data is aslo combined with the Bayesian maximum entropy method (BME) for spatiotemporal prediction of multi-source data, which expand the dataset of urban expansion models. Data assimilation method is used to optimize the parameters of the coupling model and minimize the uncertainty of observations, improving the precision of future simulation in peri-urban area. By setting quantitative parameters, the coupling model can effectively improve the simulation of the process of urban land expansion under different policies and management schemes, in order to provide scientificimplications for new urbanization strategy. In this research, we precise the urban land expansion simulation and prediction for peri-urban area, expand the scopes and selections of data acquisition measurements and methods, develop the new applications of the data assimilation method in geographical science, provide a new idea for understanding the inherent rules of urban land expansion, and give theoretical and practical support for the peri-urban area in urban planning and decision making.