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作者

William Thackway;Matthew Ng;Chyi-Lin Lee;Christopher Pettit

来源

CITIES,Vol.135,Issue1,Article 104192

语言

英文

关键字

作者单位

City Futures Research Centre, Level 3, Red Centre, West Wing, UNSW Sydney, 2052, Australia;City Futures Research Centre, Level 3, Red Centre, West Wing, UNSW Sydney, 2052, Australia;School of Architecture and Urban Planning, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu 210093, China;Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY 13210, United States;Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Laboratory of Micrometeorology, University of Salento, S.P. 6 Lecce-Monteroni, Lecce 73100, Italy;University of Belgrade, Faculty of Transport and Traffic Engineering, Belgrade, Serbia;Manchester Metropolitan University, Manchester, United Kingdom;Anotec Engineering, Motril, Spain;School of Architecture, Tianjin University, Tianjin, China;School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, China;Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK;School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong;School of Resource and Environmental Science, Wuhan University, Wuhan 430072, China;Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China;Shenzhen Municipal Planning & Land Real Estate Information Centre, Shenzhen 518034, China;Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison, Madison 53706, USA;School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China;Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China;School of Geographic Sciences, East China Normal University, Shanghai 200241, China;Department of Geography, University of Tennessee, Knoxville, TN 37996, USA;Department of Geography, Lab for Landscape Ecology, Humboldt Universität zu Berlin, Berlin, Germany;Department of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research UFZ, Leipzig, Germany;St. Peter's College, University of Oxford, New Inn Hall Street, Oxford OX1 2DL, United Kingdom;Department for Continuing Education, University of Oxford, Rewley House, 1 Wellington Square, OX1 2JA, Oxford, United Kingdom

摘要

In an era of rapid urbanisation and increasing wealth, gentrification is an urban phenomenon impacting many cities around the world. The ability of policymakers and planners to better understand and address gentrification-induced displacement hinges upon proactive intervention strategies. It is in this context that we build a tree-based machine learning (ML) model to predict neighbourhood change in Sydney. Change, in this context, is proxied by the Socioeconomic Index for Advantage and Disadvantage, in addition to census and other ancillary predictors. Our models predict gentrification from 2011 to 2016 with a balanced accuracy of 74.7 %. Additionally, the use of an additive explanation tool enables individual prediction explanations and advanced feature contribution analysis. Using the ML model, we predict future gentrification in Sydney up to 2021. The predictions confirm that gentrification is expanding outwards from the city centre. A spill-over effect is predicted to the south, west and north-west of former gentrifying hotspots. The findings are expected to provide policymakers with a tool to better forecast where likely areas of gentrification will occur. This future insight can then inform suitable policy interventions and responses in planning for more equitable cities outcomes, specifically for vulnerable communities impacted by gentrification and neighbourhood change.