Title
Variability In And Mixtures Among Residential Vacancies At Granular Levels: Evidence From Municipal Water Consumption Data
Abstract
Unprecedented urbanization in China has directly resulted in residential vacancies, which has seriously stunted sustainable development, a part of China's new-type urbanization plan. Understanding the various types and mixes of residential vacancies is critical for the advancement of our knowledge of speculative urbanism and for devising vacancy-mitigation policies, but this issue remains insufficiently studied. Using municipal water consumption data, this study proposes a feasible and general-purpose framework for providing innovative insights into the variability in residential vacancies at the household level and the mixture of residential vacancies at the building level. This framework was applied to the city of Changshu, China, and four categories of vacant residences at the household level were identified: seasonally vacant residences, long-term vacant residences, newly built residences and occasionally vacant residences. The first category is closely related to tourism and seasonal industries, while the last three exhibit a Matthew effect. In addition to revealing significant and intensifying spatial clustering and three patterns of changes in vacancy mixtures (i.e., emergence, disappearance, and increases or decreases), the results identify particular types of vacant residences at the building level (e.g., extremely low-entropy long-term multihousehold buildings). The insights from this study can contribute to devising customized policies for alleviating residential vacancies.
Year
DOI
Venue
2021
10.1016/j.compenvurbsys.2021.101702
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Keywords
DocType
Volume
China's new-type urbanization, Residential vacancy, Municipal water consumption, Granular level, Variability, Mixture
Journal
90
ISSN
Citations 
PageRank 
0198-9715
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Yongting Pan101.01
Wen Zeng200.34
Qingfeng Guan3168.64
Yao Yao400.68
Xun Liang500.34
Yaqian Zhai600.34
Shengyan Pu700.34