Module 1 - Crime analysis

    This week, in the first Applications in GIS lab, was about Crime Analysis. 

    In this weeks module it was an application in using GIS for Crime analysis. This was done in utilizing tools in ArcGIS to make hotspot maps of high crime density areas. As you can see below we used grid based thematic mapping, kernel density, and Local Moran's I hotspot analysis. Personally, I believe of the three shown, that kernel density is the best for honing in on the density of crimes.

Grid -based thematic hotspot mapping map
Local Moran's I Hotspot Mapping map
Kernel Density Hotspot Mapping map

    Also, here is some analysis from the lab on these maps and how I got the ones you see above. 

    For grid based thematic mapping (purple), I first joined the grid cells to the 2017 homicides so I could have the homicides in a grid in a new shapefile. I then selected only homicides from the join and made a new selection with the count of every homicide within the grids. I then selected the top 20% percent of the homicides by dividing the total number of records by 5 in order to get the top 20% (62 out of 311). This was done manually. Once done, I then exported these values into a separate shapefile. Once done, I added a new field so I could then dissolve the features into one field and layer. 

    With kernel density (light purple), it started with first making sure the environment was correct for the Chicago boundary. I then used the kernel density tool, making sure the output cell size was 100 and search radius was 2630. With all the settings staying the same from an earlier part in the lab, I then ran the tool and began messing with the symbology. In there I changed it to two breaks, one being 3 times the mean and the other being the maximum value. Once that was done, I reclassified the kernel density so I could have it a raster. Once done reclassing, it was then time to use the raster to polygon tool. Lastly, I then selected all the values on the gridcode that had a value of 2. I then exported it and got the shapefile layer. 

    With Local Moran's I (pink), first joined the grid cells to the homicides so that I could then calculate the number of homicides per 1000 housing units in a new field. Once done I used the Cluster and Outlier Analysis (Anselin Local Moran's I) Spatial Statistics tool. Making sure to use my field I had made earlier as the input field I got the shapefile made. I next used the select tool with the SQL query to only select high-high clusters and export it. Then finally, I dissolved the layer, so the high-high clusters were one field. 

    With those maps done and the calculations I had done for the process summary, I still think kernel density gives the best area analysis.

    Overall, I think this is an excellent first lesson to start learning applications in GIS and I cannot wait to see what Lidar brings for next week.


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