Assessing Influential Factors of Winter-Related Property Damage in Arkansas

Keywords: property damage, winter, geospatial modeling, socioeconomic, demographic, Arkansas, ice storms
Abstract Type: Poster Abstract

Abstract

Storm Data, a database published monthly by the National Weather Service with records of numerous types of weather events, reports on property damages caused by natural hazards. Since 1996, the database has included winter-related events. While this property damage data is considered incomplete, no other unique, public datasets report winter-related property damage at the county level. From 1996 to 2024, Arkansas ranked #1 in this winter-related property damage. To identify and quantify the factors that are driving the property damage and potentially reduce Arkansas’ vulnerability to it, geospatial modeling tools are used on three groups: “2000” (01/1996 - 06/2005), “2010” (07/2005 - 06/2015), and “2020” (07/2015 - 06/2024). The dependent variable is calculated as the total winter-related property damage during the period divided by the decade’s population, referred to as “damage per capita”. Through literature review, seventeen potential independent variables, including storm characteristics, demographic information, land cover features, and climatological data, are identified. Next, the Explanatory Regression tool from ArcGIS Pro is used to determine which variables are influential to the damage per capita for each group. Then, using the influential factors, the performance of ordinary least squares, spatial-lag, spatial-error, geographically weighted regression, and multi-scale geographically weighted regression models are compared. Lastly, using the most appropriate model, the selected variables’ influence on damage per capita is quantified by analyzing model coefficients for each group. Results show that different factors contributed to winter-related property damage per capita over space and time in Arkansas.