Delimitating central areas of cities based on road density: a case study of Guangzhou City

Qingnian Zhang; Xueqiu Lu

doi:10.1117/12.813172

10 November 2008 Delimitating central areas of cities based on road density: a case study of Guangzhou City

Qingnian Zhang, Xueqiu Lu

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 714627 (2008) https://doi.org/10.1117/12.813172
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

The central area of a city is an important functional unit in many urban studies. It is a region where business concentrates and municipal facilities densely distribute. Traditionally, statistics of economic and social phenomena can be used to delimitate their boundaries. However, traditional methods based on economic and social investigation are labor-intensive and sometimes inaccurate. Alternatively, road networks acting as a kind of infrastructure reflect the association of locations. Thus the concentration of road networks indicates the congestion of social-economic activities and municipal facilities to some extent. Based on density analysis of road networks, the area where roads densely distribute is recognized as the central area of a city. Taking Guangzhou City as an example, the road network was studied on a set of spatial scopes, and the central area was delimitated and analyzed. Results showed that the road-density-based delimitation had to be adjusted according to the road system, and the delimitated area was consistent to the real central area to some extent. Since road data is much accessible, road-based method is useful and practical when short of socialeconomic data.

Citation Download Citation

Qingnian Zhang and Xueqiu Lu "Delimitating central areas of cities based on road density: a case study of Guangzhou City", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 714627 (10 November 2008); https://doi.org/10.1117/12.813172

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