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• W2913519645 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." @default.
• W2913519645 created "2019-02-21" @default.
• W2913519645 creator A5020437648 @default.
• W2913519645 creator A5047366198 @default.
• W2913519645 date "2019-02-13" @default.
• W2913519645 modified "2023-09-26" @default.
• W2913519645 title "Examining the Local Spatial Variability of Robberies in Saint Louis Using a Multi-Scale Methodology" @default.
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• W2913519645 doi "https://doi.org/10.3390/socsci8020050" @default.
• W2913519645 hasPublicationYear "2019" @default.
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