Characterizing Urban Structure Using Taxi GPS Data

Characterizing Urban Structure Using Taxi GPS Data

Zhong Zheng (Eindhoven University of Technology, The Netherlands) and Suhong Zhou (Sun Yat-sen University, China)
DOI: 10.4018/978-1-4666-6170-7.ch021
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Scholars have explored urban structure from many perspectives. Developments in ICT have made it possible to discover spatial patterns in activities using big data. The identified patterns allow us to better understand urban structure. This chapter reports the collection of taxi GPS records for a single day in the inner city of Guangzhou, China. Taxi trips are connected to urban space by defining travel intensities. The spatial-temporal distribution of trips shows differences between three time periods (daytime, evening, before dawn). Different types of spatial facilities provide different activity places, the importance of which depends on their location and time of day. The study illustrates how descriptive analyses of taxi GPS data can enhance our understanding of urban space from the perspective of activities.
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The study area is the inner city of Guangzhou, China (Figure 1). Guangzhou is in the process of rapid urbanization and globalization. The inner city consists of Yue Xiu, Tian He, Hai Zhu, Li Wan, Huang Pu and Bai Yun Districts. These are the main transportation areas of the city. The inner city has a population of 7.73 million inhabitants, an area of 1210.2 km2, and 1337 communities. Data used in the study are taxi GPS records, collected on Monday, May, 11, 2009. The data is stored in an Oracle database, which is provided by the traffic research center of Sun Yat-sen University. Every taxi is installed with a GPS collector, which refreshes location information, jointly with time, speed, and carrying status, every 20 seconds (Table 1).

Figure 1.

Study area

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