No abstract

Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

The built environment closely relates to the development of COVID-19 and post-disaster recovery. Nevertheless, few studies examine its impacts on the recovery stage and corresponding urban development strategies. This study examines the built environment’s role in Wuhan’s recovery at the city block level through a natural experiment. We first aggregated eight built environmental characteristics (BECs) of 192 city blocks from the perspectives of density, infrastructure supply, and socioeconomic environment; then, the BECs were associated with the recovery rates at the same city blocks, based on the public “COVID-19-free” reports of about 7,100 communities over the recovery stages. The results showed that three BECs, i.e., “number of nearby designated hospitals,” “green ratio,” and “housing price” had significant associations with Wuhan’s recovery when the strict control measures were implemented. At the first time of reporting, more significant associations were also found with “average building age,” “neighborhood facility development level,” and “facility management level.” In contrast, no associations were found for “controlled residential land-use intensity” and “plot ratio” throughout the stages. The findings from Wuhan’s recovery pinpointing evidence with implications in future smart and resilient urban development are as follows: the accessibility of hospitals should be comprehensive in general; and the average housing price of a city block can reflect its post-disaster recoverability compared to that of the other blocks.

No abstract

No abstract