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作者

Junwen Lu;Suhong Zhou;Lin Liu;Qiuping Li

来源

CITIES,Vol.111,Issue1,Article 102984

语言

英文

关键字

Spatio-temporal behavior;Daily activity;Geographic exposure;Income;Decision tree

作者单位

School of Geography and Planning, Sun Yat-sen University, Guangzhou, China;Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou, China;School of Geographical Sciences, Guangzhou University, Guangzhou, China;Department of Geography, University of Cincinnati, Cincinnati, USA;School of Geography and Planning, Sun Yat-sen University, Guangzhou, China;Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou, China;School of Geographical Sciences, Guangzhou University, Guangzhou, China;Department of Geography, University of Cincinnati, Cincinnati, USA

摘要

This study developed a new approach to link residents' daily travel behavior and their socio-economic attributes by inferring residents’ income level from their activity space, activity sequence, and geographic exposure. A classification and regression tree (CART) analysis on data from Guangzhou, China provided three key outcomes. First, residents' income level can be inferred with considerable accuracy through their daily activity and geographic exposure. Second, geographic exposure and activity sequence variables are useful in the classification process, with vital information possibly overlooked if only mobility features are used. Third, people in different income groups can be identified by different characteristics, with high-income earners identified by their preference for particular socio-economic environments, low-income earners identified by their fixed lifestyles and dependence on affordable and convenient environments, and middle-income earners identified by more comprehensive characteristics. This study has improved our understanding of social group diversity from a spatio-temporal behavior perspective. It's also instructive in merging the attributes of conventional data with those of anonymized big data. This benefits future big data research and the development of policies supported by big data.