Authors: Mayowa Lasode*, Department of Geography, Texas State University, Edwin Chow, Department of Geography, Texas State University
Topics: Hazards and Vulnerability, Geographic Information Science and Systems, Human-Environment Geography
Keywords: homelessness, social vulnerability, dasymetric modeling, GIS, natural hazard
Session Type: Paper
Start / End Time: 3:05 PM / 4:20 PM
Room: Tower Court A, Sheraton, IM Pei Tower, Second Floor Level
Presentation File: No File Uploaded
Among existing research on social vulnerability, virtually no studies have considered homelessness as a variable in their vulnerability assessments. This study identified the relevance of homelessness as a key index in social vulnerability assessment to inform the public, policymakers and the broader body of literature of its impacts on shaping vulnerability patterns in cities. Homeless data for Austin in 2018 was first disaggregated from the council district level to block group level using dasymetric model in Geographic Information System (GIS). Principal Component Analysis was used to group highly correlated demographic and socioeconomic variables into factors, which were normalized and summed to model social vulnerability with (SOVI_H) and without homeless index (SOVI) for each BG in Austin. The result revealed significant differences in the geographic patterns between SOVI_H and SOVI. The former index, SOVI_H, showed hotspots of vulnerabilities in Downtown and East Austin neighborhoods, depicting a slight shift of social vulnerability westwards of the city. This finding is different from past results of social vulnerabilities in Austin where it used to be predominant in the East. This study shows that incorporating homelessness in identifying social vulnerability can better help researchers and other associated organizations identify the most vulnerable groups when conducting social vulnerability assessments. More importantly, a noticeable pattern in this study suggest that using SOVI variables alone without homeless would have underestimated the vulnerability distribution and thereby under-prepare for the severe disaster to hit those communities.