A new insight into land use classification based on aggregated mobile phone data

Abstract

Land-use classification is essential for urban planning. Urban land-use types can be differentiated either by their physical characteristics (such as reflectivity and texture) or social functions. Remote sensing techniques have been recognized as a vital method for urban land-use classification because of their ability to capture the physical characteristics of land use. Although significant progress has been achieved in remote sensing methods designed for urban land-use classification, most techniques focus on physical characteristics, whereas knowledge of social functions is not adequately used. Owing to the wide usage of mobile phones, the activities of residents, which can be retrieved from the mobile phone data, can be determined in order to indicate the social function of land use. This could bring about the opportunity to derive land-use information from mobile phone data. To verify the application of this new data source to urban land-use classification, we first construct a vector of aggregated mobile phone data to characterize land-use types. This vector is composed of two aspects: the normalized hourly call volume and the total call volume. A semi-supervised fuzzy c-means clustering approach is then applied to infer the land-use types. The method is validated using mobile phone data collected in Singapore. Land use is determined with a detection rate of 58.03%. An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.

Keywords:

• land use
• mobile phone data
• classification
• FCM
• Singapore

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Corrigendum

Acknowledgments

The authors wish to thank SingTel for providing the dataset. This study was funded through supports from the National Natural Science Foundation of China (Project Number: 41171345 and 41231171), a grant from National Key Technology R&D Program of China (No. 2012AA12A403). We further thank the MIT SMART Program, the Center for Complex Engineering Systems (CCES) at KACST and MIT CCES program, the National Science Foundation, the MIT Portugal Program, the AT&T Foundation, Audi Volkswagen, BBVA, The Coca Cola Company, Ericsson, Expo 2015, Ferrovial, GE and all the members of the MIT Senseable City Lab Consortium for supporting the research. The authors would also like to express gratitude to Kristian Kloeckl, Sebastian Grauwin, Michael Szell, Markus Schlapfer, Chaogui Kang) for their valuable inputs.