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作者

Cheng Liu;Yu Deng;Weixuan Song;Qiyan Wu;Jian Gong

来源

CITIES,Vol.95,Issue1,Article 102482

语言

英文

关键字

Gentrification identification;Threshold;K-means clustering;Housing reinvestment;Displacement

作者单位

School of Public Administration, China University of Geosciences, No. 388 Lumo Road, Wuhan, China;School of Environment, The University of Auckland, Private Bag 920190, Auckland, New Zealand;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11A, Datun Road, Chaoyang District, Beijing 100101, China;Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, China;Urban Studies Program, Simon Fraser University, Suite 2100, 515 W Hastings St, Vancouver, BC V6B 5K3, Canada;School of Public Administration, China University of Geosciences, No. 388 Lumo Road, Wuhan, China;School of Environment, The University of Auckland, Private Bag 920190, Auckland, New Zealand;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11A, Datun Road, Chaoyang District, Beijing 100101, China;Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China;School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, China;Urban Studies Program, Simon Fraser University, Suite 2100, 515 W Hastings St, Vancouver, BC V6B 5K3, Canada

摘要

Gentrification can be identified via a threshold-based method and/or a machine-learning approach. The former, which is simple and theoretically sound, is complementary to the latter, which is objective. In view of a lack of research on exploiting the strengths of both approaches, this study compares a threshold-based method to K -means clustering. Using the city of Auckland as a case study, we find that both approaches are in accord with each other. The maximum degrees of similarity (falling in the range 0–1) between the identification results of both approaches are 0.80 and 0.56 for binary and three-level identification, respectively. By comparison, it is evident that the threshold-based set of identification rules delineates gentrification more accurately. For example, a census tract with a confluence of housing reinvestment and at least one aspect of social upgrading is more likely to be identified as gentrified. Moreover, gentrification in Auckland assumes various appearances. Retaining a simple and universal conceptual and analytical framework for gentrification helps us focus on the essentials of this urban phenomenon: reinvestment and displacement.