Relationship between the spatial distribution of SMS messages reporting needs and building damage in 2010 Haiti disaster

Abstract

Just 4 days after the <i>M</i> = 7.1 earthquake on 12 January 2010, Haitians could send SMS messages about their location and urgent needs through the on-line mapping platform Ushahidi. This real-time crowdsourcing of crisis information provided direct support to key humanitarian resources on the ground, including Search and Rescue teams. In addition to its use as a knowledge base for rescue operations and aid provision, the spatial distribution of geolocated SMS messages may represent an early indicator on the spatial distribution and on the intensity of building damage. <br><br> This work explores the relationship between the spatial patterns of SMS messages and building damage. The latter is derived from the detailed damage assessment of individual buildings interpreted in post-earthquake airborne photos. The interaction between SMS messages and building damage is studied by analyzing the spatial structure of the corresponding bivariate patterns. <br><br> The analysis is performed through the implementation of cross Ripley's K-function which is suitable for characterizing the spatial structure of a bivariate pattern, and more precisely the spatial relationship between two types of point sets located in the same study area. <br><br> The results show a strong attraction between the patterns exhibited by SMS messages and building damages. The interactions identified between the two patterns suggest that the geolocated SMS can be used as early indicators of the spatial distribution of building damage pattern. Accordingly, a statistical model has been developed to map the distribution of building damage from the geolocated SMS pattern. <br><br> The study presented in this paper is the first attempt to derive quantitative estimates on the spatial patterns of novel crowdsourced information and correlate these to established methods in damage assessment using remote sensing data. The consequences of the study findings for rapid damage detection in post-emergency contexts are discussed.