Applications for GIS Week 1: Crime Analysis

     This week I learned about how GIS is used for crime analysis to help law enforcement. The module was focused on three different methods used to identify crime hotspots. These methods are grid overlay, kernel density, and local Moran's I. The maps below display hotspots for homicides committed in Chicago in 2017 and show the difference of the results of each method. The grid-based overlay uses aggregated data to determine the hotspots. The kernel density map uses point data and weighs points closer to a search areas center more heavily than those on the edge, creating a smoother data result. This was achieved in ArcGIS Pro by using the kernel density tool. The local Moran's I method uses aggregated data to group clusters of high occurring and low occurring instances, as well as outliers such as a high occurrence surrounded by low occurrences, or vice-versa. These maps were achieved by using a spatial join to combine the homicide statistics with census data and find the density of the crimes by dividing by the number of households in the area.

    The Local Moran’s I map covered the most area and is useful for identifying clusters as well as statistically significant outliers within the data. The grid overlay method provided very precise results showing only the areas with the highest concentration of homicides. I believe the kernel density method is the most useful as it shows more precise clusters due to the kernel smoothing and weighted data to identify patterns of crime.

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