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Networks and Urban Spatial Structure

Several years ago, a group of researchers did a study on urban spatial structure. They used passengers’ transportation record in Singapore to detect roles that different local areas were playing, influences they have and how their regional functions were changing overtime. The researchers mapped out their findings, which showed that Singapore in the short time period which the data covered, has been developing rapidly to be polycentric and according to the authors, the formation of new subcenters and communities is align with the city’s master plan.

The researchers’ analysis was based on graph theory which we discussed in class. They built a weighted directed graph using the big transportation data, where each node denoted an urban area, edges represented the possibility of travel between any two areas, and the weight of edges were defined as volume of travel. With the properties of each node and edge, they sorted out urban centers and hubs. Beyond this, the authors used “Infomap” algorithm to detect socioeconomic clusters in the network. According to the authors, the algorithm divides the nodes into communities that are highly structured, corresponding to the minimum entropy of the partitioned graph. In this way, urban spatial structure is revealed. This research is a very good example of applying network analysis method to spatial issues.

Source: http://dx.doi.org/10.1080/13658816.2014.914521

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