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作者

Yuerong Zhang;Stephen Marshall;Mengqiu Cao;Ed Manley;Huanfa Chen

来源

CITIES,Vol.112,Issue1,Article 103157

语言

英文

关键字

Urban structure;Big data analytics;Urban planning;Community detection;Network analysis;London

作者单位

The Bartlett School of Planning, University College London, United Kingdom;The Bartlett Centre for Advanced Spatial Analysis, University College London, United Kingdom;School of Architecture and Cities, University of Westminster, United Kingdom;Department of Statistics, London School of Economics and Political Science, United Kingdom;School of Geography, University of Leeds, United Kingdom;The Alan Turing Institute, United Kingdom;The Bartlett School of Planning, University College London, United Kingdom;The Bartlett Centre for Advanced Spatial Analysis, University College London, United Kingdom;School of Architecture and Cities, University of Westminster, United Kingdom;Department of Statistics, London School of Economics and Political Science, United Kingdom;School of Geography, University of Leeds, United Kingdom;The Alan Turing Institute, United Kingdom

摘要

Cities are continuing to develop and are grappling with uncertainties and difficulties as they do so. It has therefore become essential to understand how urban spatial structure changes, particularly with the increasingly available sources of ‘big data’. However, most studies mainly focus on delineating the spatial structure and its variations. Only a few have investigated the incentives behind the movement dynamics. To identify the urban structure of Greater London and uncover how it co-evolves with socio-economic and spatial policy factors, this study applies network community detection, using smart card data derived from the years 2013, 2015 and 2017, respectively. Our findings show that, firstly, between 2013 and 2017, London's urban structure moved towards a more polycentric and compact pattern. Secondly, it is found that Greater London can be clustered into five communities based on the characteristics of passengers' travel patterns. Thirdly, the dynamics of structural change in different urban clusters differ both in terms of changing intensity and potential motivation. In addition to spatial impact and spatial strategic policies, our results show that employment density and residential densities are also the main indicators that affected the interaction between Londoners in different areas on various levels.