Authors: Shakil Kashem*, University of Illinois at Urbana-Champaign
Topics: Hazards, Risks, and Disasters, Geographic Information Science and Systems, Cyberinfrastructure
Keywords: Social Vulnerability, CyberGIS, Big Data, Emergency Response
Session Type: Paper
Start / End Time: 3:05 PM / 4:45 PM
Room: Roosevelt 5, Marriott, Exhibition Level
Presentation File: No File Uploaded
With the increased frequency of extreme climatic events, identifying urban social vulnerability with better precision and in a timely manner is now more critical than ever before. Measuring social and physical vulnerability in different scales is widely applied in hazard and risk study communities (Tate 2012; Cutter and Finch 2008). Several prior studies have attempted to identify the changing patterns of social vulnerability within urban areas over time (Kashem, Wilson, and Van Zandt 2016). However, most of these studies on urban social vulnerability relied heavily on census-based static population and socio-economic indicators ignoring the dynamic movement patterns within the cities. By relying on such static demographic data, it inherently ignores the uncertain geographic context problem (UGCoP) (Kwan 2012) and thereby do not have the expected utility in disaster response and recovery activities. In this paper, we propose an approach for CyberGIS-enabled multi-scale space-time social vulnerability index which incorporates census-based authoritative data with big urban geospatial data to better portray the dynamic nature of vulnerability within an urban area. It takes up the concept of Social Vulnerability Index (SoVI) of Cutter et al. (2003), but instead of relying on a static demographic data, it considers the changing demography by identifying movements of people over time on a typical day. The proposed vulnerability index includes the time dimension of this movement pattern which was ignored in previous studies. Having such framework on a CyberGIS-enabled platform also allows it to harness big urban geospatial data for emergency response decision making.