时空Moran散点图及其在中国干旱时空聚集区识别中的应用

Spatio-temporal Moran Scatter Plot and its Application in Identifying Drought Spatiotemporal Aggregation Areas in China

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。

Drought, as one of the most difficult natural disasters to identify, causes the most serious economic losses in the entire natural disaster system. Drought in spatio-temporal aggregation areas deserves more attention than its changes and patterns because it usually causes greater damages. SaTScan provides a method for identifying spatio-temporal aggregation areas of disaster. However, there are also some defects, such as difficulty in parameter setting and inefficiency in boundary identification. For instance, the maximum scanning window in SaTScan needs to take many attempts to get a fine result. Moran scatter plot provides a method for the identification of spatio aggregation areas, which could solve problems that SaTScan faces above, but it could only identify aggregation areas in space. In this study, we proposed a method named Spatio-temporal Moran Scatter Plot based on Moran scatter plot and Local Indicators of Association(LISA), which could select scatters according to the threshold of concerned phenomenon and the LISA confidence level required by the researcher.This method plotted scatters in spatio-temporal coordinate system to get the spatio-temporal aggregation areas.This study took droughts in China from 2009—2014 as an example to identify the spatio-temporal aggregation areas, the results showed that:(1) Spatio-temporal drought aggregation areas identified by the proposed method were almost consistent with the truth, which demonstrated the effectiveness of the method. Besides, compared with the spatio-temporal scanning method, the method proposed in this study was not only easier in parameters setting but also clearer and more accurate in boundary identification;(2) The large-scale, strong drought spatiotemporal aggregation areas occurred in 2009 and 2011. In 2009, the main aggregation areas concentrated in Shanxi, Shaanxi, Beijing-Tianjin-Hebei, eastern Tibet, northeastern Inner Mongolia, Liaoning, Jilin, and YunnanGuizhou-Sichuan area;in 2011, the main aggregation areas concentrated in Huanghuai area, the middle and lower reaches of the Yangtze River, and Yunnan-Guizhou-Sichuan area. The local-scale, strong drought spatiotemporal aggregation areas occurred in 2010 and 2014. In 2010, the main aggregation areas concentrated in Yunnan-Guizhou-Sichuan area;in 2014, the main aggregation areas concentrated in Eastern Inner Mongolia,Liaoning, and other areas in Northeast China. The slight drought spatio-temporal aggregation areas occurred in2012 and 2013. In 2013, some mild drought clusters occurred in western Inner Mongolia, southern Gansu,Ningxia, southern Shaanxi, and northern Henan. In general, the spatio-temporal drought aggregation areas concentrated mainly in Yunnan-Guizhou-Sichuan, Northeast China, Huanghuai Region, and the middle and lower reaches of the Yangtze River.