Modeling Urban Growth and Socio-Spatial Dynamics of Hangzhou: 1964-2010

TL;DR

This study analyzes Hangzhou's urban growth from 1964 to 2010 using mathematical models fitted to population density data, revealing how urban density and spatial distribution evolved with policy and suburbanization.

Contribution

It introduces the application of negative exponential and power-exponential models to describe urban density changes and links model parameters to spatial entropy, providing new insights into urban dynamics.

Findings

Models fit well to observed data

Density gradients flattened during 1990s-2000s

Urban growth linked to policy and suburbanization

Abstract

Urban population density provides a good perspective for understanding urban growth and socio-spatial dynamics. Based on sub-district data of the five times of national population censuses in 1964, 1982, 1990, 2000, and 2010, this paper is devoted to making analyses of urban growth and the spatial restructuring of population in the city of Hangzhou, China. Research methods are based on mathematical modeling and field investigation. The modeling result shows that the negative exponential function and the power-exponential function can be well fitted to Hangzhou's observational data of urban density. The negative exponential model reflect the expected state, while the power-exponential model reflects the real state of urban density distribution. The parameters of these models are linearly correlated to the spatial information entropy of population distribution. The density gradient in the negative exponential function flattened in the 1990s and 2000s is closely related to the development of suburbanization. In terms of investigation materials and the changing trend of model parameters, we can reveal the spatio-temporal features of Hangzhou's urban growth. The main conclusions can be reached as follows. The policy of reformation and opening-up and the establishment of a market economy improved the development mode of Hangzhou. As long as a city has a good social and economic environment, it will automatically tend to the optimal state through self-organization.