M1 - Crime Analysis

Crime Hotspot Analysis in Chicago

Brief Description of Analysis Steps

Grid-Based Thematic Mapping:

  • Performed a spatial join between the grid cells and totalhomicides_2017.
  • Created a new feature class homicide_grid.
  • Selected grid cells with a homicide count greater than zero and exported them to homicide_count.
  • Selected the top 20% of grid cells with the highest counts (62 out of 311) and exported to hom_top20per.
  • Created an integer field DISSOLVE and assigned a value of 1.
  • Dissolved hom_top20per into a single polygon hom_top20per_diss.

Kernel Density for Homicides:

  • Ran the Kernel Density tool with:
    • Input Point Features: totalhomicides_2017
    • Output Raster: homicides_kd.tif
    • Output Cell Size: 100 feet
    • Search Radius: 2630 feet
    • Area Units: Square miles
    • Output Cell Values: Densities
    • Method: Planar
  • Reclassified the raster to select values above three times the mean density (mean = 2.88, threshold = 8.64) and converted it to a polygon hom_kd.

Local Moran's I for Homicides:

  • Performed a spatial join between census tracts and totalhomicides_2017, resulting in hom_morans_sj.
  • Added a float field for crime rate and calculated the homicide rate per 1000 housing units.
  • Ran Local Moran's I tool with:
    • Input Feature Class: hom_morans_sj
    • Input Field: Calculated crime rate
    • Output Feature Class: hom_morans_run
  • Selected high-high clusters and exported to hom_morans_hh.
  • Dissolved hom_morans_hh by the COType IDW field.

Results

Each hotspot map provides unique insights into the spatial distribution of homicides in Chicago:
  • Grid-Based Thematic Mapping: Offers a straightforward visualization of high-crime grid cells. It's useful for identifying general areas with high homicide counts, though it may lack detailed spatial variation within the cells.
  • Kernel Density: Produces a smooth surface that reveals broader patterns of crime concentration. This method is excellent for visualizing overall crime density but can miss smaller, high-density clusters.
  • Local Moran’s I: Identifies statistically significant clusters of high crime rates. This method is powerful for pinpointing specific high-risk areas, making it highly effective for targeted law enforcement interventions.

Conclusion

Among the three methods, Local Moran’s I stands out for its ability to identify precise clusters of high crime rates, making it a powerful tool for focused law enforcement efforts. Each method, however, provides valuable insights that can be tailored to the specific needs of the analysis. Combining these approaches can lead to a comprehensive understanding of crime patterns, facilitating more informed and effective policing strategies.

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