Spatial Heterogeneity in the Determinants of Urban Form: An Analysis of Chinese Cities with a GWR Approach

Shijie Li,Chunshan Zhou,Zhitao Liu,Shaojian Wang,Shuang Gao

doi:10.3390/su11020479

Abstract

It is of great significance to investigate the determinants of urban form for shaping sustainable urban form. Previous studies generally assumed the determinants of urban form did not vary across spatial units, without taking spatial heterogeneity into account. In order to advance the theoretical understanding of the determinants of urban form, this study attempted to examine the spatial heterogeneity in the determinants of urban form for 289 Chinese prefecture-level cities using a geographically weighted regression (GWR) method. The results revealed the spatially varying relationship between urban form and its underlying factors. Population growth was found to promote urban expansion in most Chinese cities, and decrease urban compactness in part of the Chinese cities. Cities with larger administrative areas were more likely to have dispersed urban form. Industrialization was demonstrated to have no impact on urban expansion in cities located in the eastern coastal region of China, which constitutes the country’s most developed regions. Local financial revenue was found to accelerate urban expansion and increase urban shape irregularity in many Chines cities. It was found that fixed investment exerted a bidirectional impact on urban expansion. In addition, urban road networks and public transit were also identified as the determinants of urban form for some cities, which supported the complex urban systems (CUS) theory. The policy implications emerging from this study lies in shaping sustainable urban form for China’s decision makers and urban planners.

Highlights

Urban form is the spatial structure of urban area, which is believed to significantly affect the spatial configuration of urban land use, transportation, and infrastructures [1]
The Determinants of Urban Form A variance inflation factor (VIF) test was first run in order to avoid possible multicollinearity amongAthveanriianneceexipnlfalnataitoonryfavcatorira(bVleIsF.) AtesstshwoawsnfiirnstTraubnlein3,othrdeeVr ItFosaovfotihdepnoinsseibvlaermiabulletiscowlleirneeaarllity fouamndontogbtheelensisntehaenxp1l0a,naantodrythveatroilaebrlaensc. eAsswsehroewanll ifnouTnadblteo3b,ethgereVatIeFrs tohfatnhe0.1n,ininedvicaaritainbgletshwatetrheeall ninfoeuinnddetpoebnedleensst vthaarinab1l0e,sawndertehneotot lceorlalinnceeasr.wTehreeraelflofroeu, nthdetGo WbeRgmreoadteerltwhaans a0b.1l,eitnodbiceautitnilgiztehdattothe exnaminienientdheepceonrdreelnattivoanrsiabbeltews eweenrue rnboatncfoolrlimneaanr.dTehaecrhefoofrteh,etheexpGlWanRatomroydvealrwiaabsleasb. le to be utilized to examine the correlations between urban form and each of the explanatory variables
An increasing number of studies devoted to investigating the determinants of urban form, while the majority of them overlooked the possible spatial heterogeneity existing in the determinants of urban form

Summary

Introduction

Urban form is the spatial structure of urban area, which is believed to significantly affect the spatial configuration of urban land use, transportation, and infrastructures [1]. Over the last few decades, substantial progress has been made in studying urban form [2]. Population density is widely used to reflect urban expansion, and is believed to be the most intuitive indicator of urban form [4,5]. With the development of geographical information system (GIS), urban form began to be popularly measured on the neighborhood scale. Song and Knaap measured the urban form of Portland by analyzing land use mixtures, density, and accessibility [6]. Remote sensing (RS) technologies and landscape metrics make it possible to measure urban form from a macro perspective. Different urban form is likely to result in diverse economic, social, and ecological effects [8], due to which a range of previous studies have investigated the impacts of urban form on a series of socioeconomic and environmental factors, such as carbon emissions [9], air pollution [10,11,12], and urban–rural income [13]

Results

Discussion

Conclusion