We use data on human mobility obtained from mobile applications to explore the activity patterns in the neighbourhoods of Greater London as they emerged from the first wave of COVID-19 lockdown restrictions during summer 2020 and analyse how the lockdown guidelines have exposed the socio-spatial fragmentation between urban communities. The location data are spatially aggregated to 1 km2 grids and cross-checked against publicly available mobility metrics (e.g. Google COVID-19 Community Report, Apple Mobility Trends Report). They are then linked to geodemographic classifications to compare the average decline of activities in the areas with different sociodemographic characteristics. We found that the activities in the deprived areas dominated by minority groups declined less compared to the Greater London average, leaving those communities more exposed to the virus. Meanwhile, the activity levels declined more in affluent areas dominated by white-collar jobs. Furthermore, due to the closure of non-essential stores, activities declined more in premium shopping destinations and less in suburban high streets.
我们使用从移动应用程序中获得的人员流动数据，探索大伦敦各街区在2020年夏季新冠肺炎第一波封锁限制后的活动规律，并分析封锁指南如何暴露了城市社区之间的社会空间分割。位置数据在空间上汇总到以1平方公里为单位的网格上，并与公开移动性指标（如谷歌新冠肺炎社区报告、苹果移动趋势报告）进行交叉核对。然后将它们与地理人口分类联系起来，以比较具有不同社会人口特征的地区的平均活动下降。我们发现，与大伦敦的平均水平相比，少数民族占主导地位的贫困地区的活动减少得更少，使这些社区更容易感染病毒。与此同时，白领工作占主导地位的富裕地区的活动水平下降更多。此外，由于非必需品商店的关闭，高档购物目的地的活动减少较多，郊区商业街的活动减少较少。.
Keywords: COVID-19; location data; regression analysis; smartphone applications; socioeconomic inequalities.
© Urban Studies Journal Limited 2021.