Seasonal and spatial differences of building occupancy profiles: Evidence from mobile data

¹ School of Architecture, Tsinghua University, Beijing 100084, China
² Key Laboratory of Eco Planning & Green Building, Ministry of Education, Tsinghua University, Beijing 100084, China
³ Institute for Urban Governance and Sustainable Development, Tsinghua University, Beijing 100084, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
^† Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
^‡ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Building occupancy is a critical determinant of building energy consumption, yet it is often represented by typical, deterministic patterns that overlook potential spatiotemporal variations. Using large-scale mobile data, this study provides an initial examination of how building occupancy varies across seasons, cities, and intra-city locations. Occupancy profiles were generated for over 440,000 buildings across Beijing, Chongqing, and Zhengzhou, of which the core analysis focused on five representative building types: residential, office, retail, hotel, and cultural. Results show that intra-city differences are the most pronounced, exhibiting a clear 'center-periphery' pattern. For example, retail and cultural buildings in Beijing's 2nd Ring display cumulative occupancy density 2.4 times higher than those in the 4th-5th Ring. Inter-city variations display complex patterns potentially associated with urban morphology and economic context. Seasonal differences are most evident in cultural buildings, with spring weekend afternoon occupancy reaching 1.9 times winter levels. The findings reveal significant variability in occupancy, highlighting the limitations of uniform assumptions in building simulations. Accounting for this can enhance the accuracy of occupancy representation, facilitate more precise building energy modeling, and support cross-scale carbon reduction interventions.