Spatiotemporal Big Data Empower Community Modeling, Monitoring, Evaluation, and Optimization for Sustainable Community Development: A review of challenges and opportunities

Sustainable development goals (SDGs) promoted by the United Nations guide the survival and development of human beings. Sustainable community development is an important part of SDGs, which aims at efficient and equitable utilizations of natural and social resources to construct more livable, productive, and environmentally-sound communities. Despite the growing concern of this issue, there are few review efforts to clarify the concepts, technical development and challenges in facilitating sustainable communities. Accordingly, this study will review the emerging spatiotemporal big data and novel analysis techniques to explore their contributions to community modeling, monitoring, evaluation, and optimization, and, more importantly, the study will point out the great challenges and future directions of applying spatiotemporal data to sustainable community development. For community modeling, existing geographic object modeling struggled to capture the spatial structures, functional services, and temporal changes of communities, and cannot provide accurate and comprehensive geographic representations of communities. For community monitoring and evaluation, previous monitored indicators often exhibit variations in spatial/temporal scales and representations, affecting the integration of multiple indicators and overall evaluation of community sustainability. For community optimization, existing spatial optimization and simulation methods ignored the evolutionary processes of communities, making the optimization results far away from the actual development trends. Addressing these challenges through innovative geographic modeling methods, comprehensive indicator monitoring and evaluation, and novel understandings on community optimization will unlock the potential of spatiotemporal big data to empower sustainable community development initiatives.