Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.

With decades of urbanization, housing and community problems (e.g., poor ventilation and lack of open public spaces) have become important social determinants of health that require increasing attention worldwide. Knowledge regarding the link between health and these problems can provide crucial evidence for building healthy communities. However, this link has heretofore not been identified in Hong Kong, and few studies have compared the health impact of housing and community conditions across different income groups. To overcome this gap, we hypothesize that the health impact of housing and community problems may vary across income groups and across health dimensions. We tested these hypotheses using cross-sectional survey data from Hong Kong. Several health outcomes, e.g., chronic diseases and the SF-12 v. 2 mental component summary scores, were correlated with a few types of housing and community problems, while other outcomes, such as the DASS-21–Stress scores, were sensitive to a broader range of problems. The middle- and low-income group was more severely affected by poor built environments. These results can be used to identify significant problems in the local built environment, especially amongst the middle- and low-income group.

COVID-19 resurgences worldwide have posed significant challenges to the formulation of preventive interventions, especially given that the effects of physical distancing and upcoming vaccines on reducing susceptible social contacts and eventually halting transmission are still unclear. Using anonymized mobile geolocation data in China, we devised a mobility-associated social contact index to quantify the impact of both physical distancing and vaccination measures in a unified way such that the gap between intervention measures and disease transmission can be explicitly bridged. This index explained 90% of the variance in the changing reproduction number of infections across the COVID-19 outbreak in Wuhan, and was validated in six other cities of different population densities. Our simulations showed that vaccination combined with physical distancing can contain resurgences without relying on mobility reduction, whereas a gradual vaccination process alone cannot achieve this. Further, for cities with medium-population density, vaccination can shorten the duration of physical distancing by 36%-78%, whereas for cities with high-population density, infection numbers can well be controlled through moderate physical distancing. These findings provide guidance on tailoring and implementing comprehensive interventions for cities with varying population densities.