International Journal of Geographical Information Systems· 2014
Detecting the dynamics of urban structure through spatial network analysis
- 528citations
- 2014year
Short summary
A network analysis of Singapore's 2010-2012 smart card fare data reveals emerging polycentric urban structures and distinct neighborhood borders not aligned with administrative boundaries.
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Key points
- Urban areas are modeled as nodes in a weighted directed graph, with edge weights representing travel volume between them.
- Graph centralities are used to detect urban centers and hubs, while community structures reveal socioeconomic clusters and neighborhoods.
- Analysis of 2010-2012 travel data shows Singapore is developing into a polycentric urban form with emerging subcenters.
- The identified community structures and borders do not align with existing administrative divisions.
AI-generated from the title and abstract; the full text is not read.
Abstract
Urban spatial structure in large cities is becoming ever more complex as populations grow in size, engage in more travel, and have increasing amounts of disposable income that enable them to live more diverse lifestyles. These trends have prominent and visible effects on urban activity, and cities are becoming more polycentric in their structure as new clusters and hotspots emerge and coalesce in a wider sea of urban development. Here, we apply recent methods in network science and their generalization to spatial analysis to identify the spatial structure of city hubs, centers, and borders, which are essential elements in understanding urban interactions. We use a ‘big’ data set for Singapore from the automatic smart card fare collection system, which is available for sample periods in 2010, 2011, and 2012 to show how the changing roles and influences of local areas in the overall spatial structure of urban movement can be efficiently monitored from daily transportation.In essence, we first construct a weighted directed graph from these travel records. Each node in the graph denotes an urban area, edges denote the possibility of travel between any two areas, and the weight of edges denotes the volume of travel, which is the number of trips made. We then make use of (a) the graph properties to obtain an overall view of travel demand, (b) graph centralities for detecting urban centers and hubs, and (c) graph community structures for uncovering socioeconomic clusters defined as neighborhoods and their borders. Finally, results of this network analysis are projected back onto geographical space to reveal the spatial structure of urban movements. The revealed community structure shows a clear subdivision into different areas that separate the population’s activity space into smaller neighborhoods. The generated borders are different from existing administrative ones. By comparing the results from 3 years of data, we find that Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city’s master plan.To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.
The authors' abstract, as published at the source. International Journal of Geographical Information Systems, 2014 · DOI ↗
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Field: Transportation
TransportationSocial Sciences