The COVID-19 has changed people’s lifestyle from many aspects such as the way people commute and their attention to health and fitness, and environmental problems. This report is to understand how residents in Shanghai commute with difficulties and the environmental sustainability challenges and health issues in the post-pandemic rehabilitation background. Taking educational institution in Shanghai as example, this study applied big data techniques with kernel density, Ripley’s K(d) function and 2SFCA analysis to identify the spatial characteristics and accessibility of different institution types under different travel modes such as walking. The results show that the distribution is extremely uneven in Shanghai, area with extreme concentration is observed. In case of educational workplace, although the proportion of street-town with moderate or higher accessibility reached 74.34%, such proportion is only 41.01% for walking mode. Current planning has skewed commuting around educational sites towards more carbon-intensive travel patterns and not conducive for keeping fit and health. Besides, households located in the districts of Chongming, Qingpu and Jinshan commute much longer than those in other regions. Long commute time and less exercise may lead to the wider spread of disease and it’s harmful to residents’ health and sustainable living. In summary, the findings of this paper regarding commuting to educational sites provide a clearer understanding of the health and sustainability challenges for policymakers.
Accessibility of health and fitness services is important for the rehabilitation in the post-epidemic era. The emergence of multi-source data offers new and timely approaches to understanding health and fitness facilities (HFFs) in cities. Taking Shanghai as an example, this paper obtained the HFFs dataset through platforms such as GaoDe, and crawled data were accurately classified into Street level, District level and Municipal level. Based on spatial data mining, the spatial differentiation of current HFFs was understand. The analysis through Kernel Density Estimation (KDE) and the Multi-mode Two-step Floating Catchment Area Method (M2SFCA) indicates that HFFs basically meet the needs of most residents in the city within their service scope. The Lorentz curve results shows that top 20% community residents enjoy up to 73.66% of HFFs. This shows that there is a large difference in HFF accessibility among community residents. This article presents a visualization example of spatial computing in understanding health accessibility and can provide a timely reference for policy making.
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