GIS 5100 Module 4

Overview: This module's lab involved using three different hotspot techniques in ArcGIS Pro to determine crime densities within areas in Washington DC and Chicago. 
See figures below for a brief overview of the process of creating each hotspot type.

Local Moran's I Hotspot map is shown above.

Moran's I process summary: 
1)   Join the boundaries (census tracts) to the points (number of homicides in 2017): Right click on the polygon layer, in this case census tracts, select Spatial Join. The target feature will automatically be set to the polygon layer, set join features to the points, in this case number of homicides in Chicago in 2017. Set operation to ‘one to one’, which avoids duplicating point features, ‘keep all target features’ box is left checked, and set the match option is set to ‘completely contains’, which will produce a column within the census tracts layer that holds a count of all point features that fell within that polygon.
2)     Calculate the homicide rate: Homicide rate per 1,000 households is calculated by adding a double field to the new output layer, the aggregation of the points to the census tracts. The new field is calculated using the field calculator tool, using the formula: (!Join_Count!/!total_households!)*1000 in Python 3.
3)     Calculate the Anselin Moran’s I: Using the Anselin Local Moran’s I tool, and the homicide rate per household field as the input field. This produces an output layer with five classes for cluster types: High-High, High-Low, Low-High, Low-Low, and Not Significant. High-High clusters are isolated using a search query and exporting the resulting selection.
4)     Dissolve the High-High features: The resulting feature class is dissolved into a single polygon using the Dissolve geoprocessing tool, where the field containing the High-High classification (note that all values in this data set are High-High) is dissolved. 
Kernel Density Hotspot map is shown above.
Kernel Density process summary:
1) Use the kernel density geoprocessing tool: Setting the input points as your point features, in this case location of homicides in Chicago in 2017, Area units to Square Miles, output cell values to 'Densities', output cell size was set to 100, and search radius was set to 2630. This creates a hotspot map with values ranging from the lowest to highest values.
2) Select high density areas: This is a manual selection that is up to the map makers discretion, but for this purpose, high density areas are considered those with a value of 3 times the average or greater. This selection was performed by first setting the display in symbology, and then reclassifying the data using the Reclassify geoprocessing tool. Two classes are set, one for those with a value that is 3 times the average and above, and one that is below 3 times the average. The resulting raster is then converted to a polygon using the Raster to Polygon geoprocessing tool. The areas that contain the classification for being of high density, or those identified as having 3 times or higher the mean, are selected and that selection exported as a new layer.
Grid Overlay Hotspot map is shown above.
Grid Overlay process summary:
1) Join point features (homicides in 2017) to your grid feature: Spatially join your grid to your points, setting your join as one to one, and match option to 'completely contains'. This produces as layer layer with your grid polygons that contains a count of all point features that fall within each grid. 
2) Select those grid cells which contain point values greater than 0: This can be done using the Select by Attribute geoprocessing tool, using a query within this tool and then outputting the resulting selection as a new layer. You can also accomplish the same thing by creating a new definition query in your layer, outputting the resulting selection as a new layer, and clearing the definition query after. 
3) Select a top quintile: Select the top 20% of grid cells by manually sorting the values within your layer, determining how many values make up a quintile, and then selecting that number of values with your mouse. Export the selection as a new layer and clear selection in your original layer. 
4) Dissolve the grid cells: Use the Dissolve geoprocessing tool to dissolve the grid cells into a single feature. This is done by setting a field that you do not need, in this case a new field was created with the value of '1' applied to every row. The result is a layer with an output of 1 feature, the grid dissolved into a single feature.



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