To conclude, the objective of finding a socioeconomic cause for violent crime in the city of Chicago was achieved. Using OLS regression analyses demonstrated a relationship between variables. Testing these regressions with the Moran’s I test proved the presence of spatial autocorrelation and the relationship was not of random chance. Geographically weighted regression was used to test the relationship of variables locally. Running this different type of linear regression demonstrated the relationship between crime, income, and employment in another way and further confirmed the relationship. Low income and low employment are responsible for violent crime in Chicago and create areas that hold the potential to create even more. I believe these variables can be addressed over time with the goal of reducing the amount of violent crime produced in the city of Chicago. Steps need to be taken in order to create a safer city.
Sources:
“ArcGIS Pro.” Select Features by Location-ArcGIS Pro | ArcGIS Desktop, pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/interpreting-ols-results.htm.
“EDN: ESRI Developer Network.” Geographic Coordinate System, edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_statistics_toolbox/what_is_a_z_score_qst_.htm.
“Help.” What Is SQL?-Help | ArcGIS Desktop, desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/geographically-weighted-regression.htm.
“Moran’s I: Definition, Examples.” Statistics How To, 14 Nov. 2017, www.statisticshowto.datasciencecentral.com/morans-i/.
“Spatial Autocorrelation and Moran’s I in GIS.” GIS Geography, 16 Feb. 2018, gisgeography.com/spatial-autocorrelation-moran-i-gis/.