The Uncertain Geographic Context Problem in Identifying Activity Centers Using Mobile Phone Positioning Data and Point of Interest Data
People aggregate at different areas in different times of the day, thus forming different activity centers. The identification of activity centers faces the uncertain geographic context problem (UGCoP) because people go to different places to conduct different activities, and also go to the same place for carrying out different activities in different times of the day. In this paper, we employ two kinds of novel dynamic data, namely mobile phone positioning data and Point of Interest (POI) data to identify the activity centers in a city in China. Then mobile phone positioning data is utilized to identify the activity centers in different times of a working day, and POI data are used to show the activity density variations at these activity centers to explain the temporal dynamics of geographic context. We find that mobile phone positioning data and POI data as two kinds of spatial-temporal data demonstrate people’s activity patterns from different perspectives. Mobile phone positioning data provide a proxy to represent the activity density variations. POI data can be used to identify activity centers of different categories. These two kinds of data can be integrated to identify the activity centers and clarify the UGCoP.
KeywordsActivity center UGCoP Mobile phone positioning data Point of interest
This research was supported by the National Science Foundation of China (No. 41471378, 41231171, 41171348), and Shenzhen Scientific Research and Development Funding Program (JCYJ20121019111128765, JCYJ20130329144141856). Weifeng Li would like to thank the support from the Francis SK Lau Research Fund.
- Cervero R (1991) Land uses and travel at suburban activity centers. Transp Quaterly 45(4):479–491Google Scholar
- Erickson F, Schultz J (1997) When is a context? Some issues and methods in the analysis of social competence. In: Cole M, Engestrom Y, Vasquez O (eds) Mind, culture, and activity: seminal papers from the laboratory of comparative human cognition. Cambridge, pp 22–31Google Scholar
- Openshaw S (1984) Concepts and techniques in modern geography number 38: the modifiable areal unit problem. Geo Books, NorwickGoogle Scholar
- Phithakkitnukoon S, Horanont T, Di Lorenzo G, Shibasaki R, Ratti C (2010) Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Human behavior understanding. Springer, pp 14–25Google Scholar
- Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 186–194Google Scholar
- Zhou X, Yue Y, Yeh AGO, Wang H, Zhong T (2014) Uncertainty in spatial analysis of dynamic data—identifying city center. Geomatics Inform Sci Wuhan Univ 39(6):701–705 (in Chinese)Google Scholar