Hierarchical Modeling of Social Vulnerability Using Spatial Statistics and GIS

Authors: Courtney Thompson*, Texas A&M University, Raymond J Dezzani, University of Idaho
Topics: Hazards and Vulnerability, Spatial Analysis & Modeling
Keywords: hierarchical, vulnerability, spatial statistics, natural hazards
Session Type: Paper
Day: 4/12/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Napoleon B1, Sheraton 3rd Floor
Presentation File: No File Uploaded


Decision makers often conduct vulnerability assessments to identify sources of vulnerability, which allow agencies with limited resources to mitigate areas where hazard impacts are highest. However, these vulnerability assessment methods are often limited in that they are not multiscalar and are often conducted at a single scale of measurement and analysis, employ statistical methods that do not necessarily reflect input data structures, and neglect the impact of spatial effects on classical statistical methods.This research presents a vulnerability assessment methodology that examines the impact of spatially-explicitly and multi-scalar factors on vulnerability using hierarchical generalized linear regression model (HGLM) with spatial components. Results from the model estimations illustrate how spatial effects influence classical, simultaneous autoregressive and HGLM model results in terms of significant variables. Spatial effects may result in variable significance and coefficients that highlight outlier variables, which can lead to over or underestimation of indicator impacts on vulnerability during the planning process. Results also indicate that the unit of analysis is a significant factor when considering different types of indicators of vulnerability and they scale at which they are measured (census block versus census block group). This type of modeling illustrates how indicators behave and interact at different scales and identifies which indicators have significant influence on vulnerability throughout the study area. Enhanced modeling methodologies can lead to better planning for disaster events, as there is a deeper understanding of where vulnerability, not just exposure, is highest and which indicators are the most influentially impactful.

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