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作者

Zhengbin Dong;Yanwen Yun;Yeran Sun;Fenglong Wang

来源

CITIES,Vol.121,Issue1,Article 103493

语言

英文

关键字

Building use;Subjective wellbeing;Neighborhood environment;Uncertainty problem;Big data

作者单位

Institute of Chinese Financial Studies, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Qingyang District, Chengdu, Sichuan 610074, China;College of Economics, Jinan University, No. 601 Huangpu Road West, Guangzhou 510632, China;Department of Geography, College of Science, Swansea University, Swansea SA28PP, United Kingdom;School of Geography & The Center of Administrative Division and Spatial Governance, South China Normal University, Guangzhou 510631, China;Institute of Chinese Financial Studies, Southwestern University of Finance and Economics, 55 Guanghuacun Street, Qingyang District, Chengdu, Sichuan 610074, China;College of Economics, Jinan University, No. 601 Huangpu Road West, Guangzhou 510632, China;Department of Geography, College of Science, Swansea University, Swansea SA28PP, United Kingdom;School of Geography & The Center of Administrative Division and Spatial Governance, South China Normal University, Guangzhou 510631, China

摘要

This paper examines the effects of mixed and dense building use patterns on life satisfaction of local residents. The analysis is empirically conducted by using a combination of a cross-sectional individual survey data and point-of-interest big data in Beijing. The results show that mixed building uses are positively associated with subjective wellbeing, but residents tend to be less satisfied with dense building use patterns. Additional results quantify evidence that the building use-wellbeing association needs to be contingent upon human lived experiences for consumption and social interaction in the uncertain geographical context. Findings of this study suggest that the hybrid application of location-based big data and traditional survey in urban contexts provides an alternative channel for recovering the built environment-wellbeing associations at fine geographical scales.