Space-Time Clustering with the Space-Time Permutation Model in SaTScan™ Applied to Building Permit Data Following the 2011 Joplin, Missouri Tornado

Abstract

Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to (1) compare the observed versus expected frequency (chi-square) of permit issuance before and after the EF5 2011 tornado; (2), determine if significant space-time clusters of permits existed using the SaTScan™ cluster analysis program (version 9.7); and (3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event, and one (residential) showed significance for nine of the 10 years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.