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.

Comments

Post a Comment

Popular posts from this blog

M1 - Python Environments & Flowcharts

Image
  Python Module 1 Recap: From Scripts to Flowcharts and Zen Principles Converting 3 Radians to Degrees To convert 3 radians to degrees, we use a simple formula: degrees = radians * 180 / pi Below is the flowchart illustrating the conversion process: Running the calculations in IDLE gives us:  ≈ 171.887 Interpretation of the Zen of Python The "Zen of Python" showcases the philosophy and guiding principles of Python programming. It emphasizes simplicity, readability, and clarity in code. In other words, “Zen of Python” promotes straightforward and elegant solutions over complicated ones. By valuing explicitness over implicitness, the basis of Python encourages clear and understandable code. Moreover, The principles highlight the importance of practicality and avoiding unnecessary complexity— why use 4 lines of code when 1 will do the same thing? This mentality helps to balance between purity and practicality. Overall, the “Zen of Python” advocates for writing code that is easy ...
Read more

GIS Map Projections

Image
  This week's lab exercise looked at the importance of map projections in GIS, especially when using multiple datasets. The map above shows four counties in Florida that are displayed using the Albers Conical Equal Area, NAD 1983 UTM Zone 16N, and NAD 1983 HARN State Plane Florida North FIPS 0903 (US Feet) map projections. As the table within the map shows, the areas of the counties vary depending on the map projections. The best map projections are ones that shows the least distortion between map features. For example, The UTM projection is best when used the map features fit within one UTM zone, which is not the case for showing the entire state of Florida. The same can be said for the Florida State Plane that is dependent on the region of interest. Therefore, the map projection would need to be Albers since it minimizes distortion between its standard parallels.  Overall, this lab illustrates how important it is to use accurate map projections since it can cause distor...
Read more

ArcGIS Pro Orientation

Image
  Module 1 Lab Assignment This first lab assignment's objective is to prepare and show what is expected for future lab assignments. The primary learning outcomes involved: Getting familiar with accessing the ArcGIS software through Argo Apps. Assessing data storage drives. Preparing project folders for each project. The map shown above is a visualization of the Cities and World Countries data files found in the OverviewArcGIS file in the R drive. In order to view the shapefiles in a way that is informative, we had to add a Folder Connection to the data we downloaded as well as update the symbology to show graduated colors for the field POP_2007 with Natural Breaks and 7 classes. It is important to use a single color from light to dark when using sequential data. While I wish this had been a smooth and uncomplicated process, I struggled with learning UWF's Argo Apps since that is how we access ArcGIS Pro without downloading it to our personal computers. While I am familiar with ...
Read more