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Identifying Urban Residents’ Activity Space at Multiple Geographic Scales Using Mobile Phone Data

by Lunsheng Gong 1,2, Meihan Jin 3,4, Qiang Liu 3,4, Yongxi Gong 3,4,* and Yu Liu 5
1
Laboratory for Urban Future, Peking University (Shenzhen), Shenzhen 518055, China
2
School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China
3
Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
4
School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
5
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 241; https://doi.org/10.3390/ijgi9040241
Received: 26 February 2020 / Revised: 28 March 2020 / Accepted: 10 April 2020 / Published: 12 April 2020
Residents’ activity space reflects multiple aspects of human life related to space, time, and type of activity. How to measure the activity space at multiple geographic scales remains a problem to be solved. Recently, the emergence of big data such as mobile phone data and point of interest data has brought access to massive geo-tagged datasets to identify human activity at multiple geographic scales and to explore the relationship with built environment. In this research, we propose a new method to measure three types of urban residents’ activity spaces—i.e., maintenance activity space, commuting activity space, and recreational activity space—using mobile phone data. The proposed method identifies the range of three types of residents’ activity space at multiple geographic scales and analyzing the relationship between the built environment and activity space. The research takes Zhuhai City as its case study and discovers the spatial patterns for three activity space types. The proposed method enables us to achieve a better understanding of the human activities of different kinds, as well as their relationships with the built environment. View Full-Text
Keywords: activity space; multiple geographic scales; mobile phone data; point of interest; anchor point activity space; multiple geographic scales; mobile phone data; point of interest; anchor point
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Gong, L.; Jin, M.; Liu, Q.; Gong, Y.; Liu, Y. Identifying Urban Residents’ Activity Space at Multiple Geographic Scales Using Mobile Phone Data. ISPRS Int. J. Geo-Inf. 2020, 9, 241.

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