Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology

Tara Smith,J Sandoval

doi:10.3390/socsci8020050

Abstract

The current study spatially examines the local variability of robbery rates in the City of Saint Louis, Missouri using both census tract and block group data disaggregated and standardized to the 250- and 500-m raster grid spatial scale. The Spatial Lag Model (SLM) indicated measures of race and stability as globally influencing robbery rates. To explore these relationships further, Geographically Weighted Regression (GWR) was used to determine the local spatial variability. We found that the standardized census tract data appeared to be more powerful in the models, while standardized block group data were more precise. Similarly, the 250-m grid offered greater accuracy, while the 500-m grid was more robust. The GWR models explained the local varying spatial relationships between race and stability and robbery rates in St. Louis better than the global models. The local models indicated that social characteristics occurring at higher-order geographies may influence robbery rates in St. Louis.

Highlights

IntroductionData Analysis (ESDA) is a data-driven approach that incorporates quantitative techniques and visual outputs to expose spatial patterns and relationships (Anselin and Getis 1992)
Analyzing crime can be accomplished by using different spatial techniques
The current study examines the following research questions: First, does census tract or block group data better explain the relationship between percent African American and robbery rates, and percent homeownership and robbery rates? We hypothesized that the census tract level of data provided a better explanation of the relationships between race and stability and robbery rates

Summary

Introduction

Data Analysis (ESDA) is a data-driven approach that incorporates quantitative techniques and visual outputs to expose spatial patterns and relationships (Anselin and Getis 1992). ESDA tools offer an opportunity to examine data for spatial outliers, trends, patterns, clustering, or relationships that indicate areas for further investigation (Anselin 1999). Different types of ESDA techniques are applied to explore specific spatial processes related to crime. Hotspot mapping methods are used to locate where crime concentrates or clusters in an area (Eck et al 2005). These methods indicate where crimes may cluster in space but do not explain why they occur there (Caplan et al 2012). Spatial autoregressive models account for spatial autocorrelation within models and indicate statistically significant global relationships (Anselin and Bera 1998), while Geographically Weighted Regression (GWR) models explore the local effects of explanatory variables on specific crimes in a study area (Fotheringham and Rogerson 2009)

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