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 da...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^(TM) 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 10years. 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.展开更多
文摘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^(TM) 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 10years. 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.
文摘干旱灾害给整个自然灾害体系带来的经济损失最为严重,也是目前检测难度较高的自然灾害之一。SaTScan在灾害时空聚集区的识别中已有应用,但其存在参数设定困难、识别区域不够精确等问题。本文对Moran散点图和局部空间关联指标(Local Indicators of Spatial Association,LISA)进行时空扩展,提出了一种时空Moran散点图的方法,根据研究者对关注现象阈值及置信程度的要求,筛选出符合条件的点,并将其绘制在对应的时空坐标系上,从而得到时空聚集区。以2009—2014年中国干旱时空聚集区识别为例,结果表明:(1)时空Moran散点图识别到的干旱时空聚集区与实际基本相符,验证了方法的有效性;同时,与时空扫描法相比,该方法具有识别结果边界清晰、精确,参数设置容易等优点;(2)2009年和2011年呈现大范围、较强的干旱时空聚集区,2010年和2014年出现局部、较强的干旱时空聚集,而2012年和2013年的干旱时空聚集情况较轻。综合来看,2009—2014年干旱时空聚集区主要出现在云贵川、东北、黄淮地区和长江中下游等地区。
