打印

作者

Jianhui Lai;Tiantian Luo;Xintao Liu;Lihua Huang;Zidong Yu;Yanyan Wang

来源

CITIES,Vol.136,Issue1,Article 104253

语言

英文

关键字

作者单位

Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, China;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 181 Chatham Road South, Hung Hom, Kowloon, Hong Kong;School of Management and Engineering, Capital University of Economics and Business, 121 Zhangjia Road, Huaxiang, Fengtai district, Beijing, China;Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, China;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 181 Chatham Road South, Hung Hom, Kowloon, Hong Kong;School of Management and Engineering, Capital University of Economics and Business, 121 Zhangjia Road, Huaxiang, Fengtai district, Beijing, China;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China;GIS Centre, Department of Physical Geography and Ecosystem Science, Lund University, Sweden;Geomatics Engineering Lab, Department of Civil Engineering, Cairo University, Egypt;Smart Cities Research Institute, The Hong Kong Polytechnic University, Hong Kong, China;Centre for Advanced Middle Eastern Studies, Lund University, Lund, Sweden;School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China;School of Public Policy and Administration, Chongqing University, Shapingba District, Chongqing 400044, PR China;Erasmus School of History, Culture and Communication, Department of Arts and Culture Studies, Netherlands;School of Economics, Nanjing University of Finance and Economics, Nanjing 210023, China;School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China;School of International Economics and Trade, Nanjing University of Finance and Economics, Nanjing 210023, China;Department of Economics and Management, Party School of C.P.C. Jiangsu Committee, Nanjing 210009, China;Faculty of Geography, Yunnan Normal University, Yunnan 650500, China;Department of Technology and Society, College of Engineering and Applied Sciences, State University of New York, Stony Brook, NY 11794-4404, United States of America;SUNY Korea, Republic of Korea;Stony Brook University USA;Kelley School of Business, Indiana University USA

摘要

Household size and its spatial distributions reflect not only the socioeconomic development in a city but also the rationality of urban resource allocation. Most existing studies rely heavily on census data to explore the potentially influential factors using methods such as macro-statistical analysis and socioeconomic analysis, of which the spatial resolutions and geographic scales are constrained. More importantly, the association between the household size distribution and the built environment is oversimplified or even neglected to some extent. In this work, we use massive mobile phone data combined with travel surveys of Beijing inhabitants' data (TSBI) to infer the household size and analyze the effect of spatial heterogeneity in a finer spatial resolution in Beijing, China. First, the machine learning method (i.e., support vector machine (SVM)) is applied to identify the household relationships of mobile users, and there are around 3.44 million households (families) with different sizes are obtained. Second, we analyze the spatial distribution patterns of household size and its association with built environmental indicators (e.g., public service density, public transportation density, etc.). The results exhibit a heterogeneous effect of the regional built environment on average household size (AHS). For instance, “commercial density” and “administrative density” show a negative impact on household size, while “public service density” and “public transportation density” show positive correlations with household size. As a complement to census data, mobile phone data can be used to obtain the household size in real-time. This paper provides quantified evidence for government departments to allocate facilities in a more targeted, balanced, and reasonable way according to the regional differences in household size, which would potentially support the sustainable urban development.