Applications in GIS - Mod 5
This week's lesson was in four parts that resulted in two maps. We started with organizing and documenting our work by setting up our project folders, defining our Map Document Properties, including assigning our default geodatabase, and setting our environments.
Part 2 involved prepping our first map for analysis by adding the datasets we would need, creating an Address Locator and geocoding addresses for the police stations in our area of interest. During this process we had only one address that was unmatched so that shouldn't have taken very long, but I realized after completing the next step, symbolizing the main highways, that locating that unmatched address would have gone much more quickly if I had done that first. After that I took the time to read through the rest of the lab instructions again to make sure there wasn't anything else I wanted to do in reverse order.
Part 3 had us analyze crime in Washington DC and police stations by crime proximity. We started by creating a graph of all the crimes in DC and the number of occurrences reported for each one. Next we used the Multiple Ring Buffer to create three buffers of .5, 1 and 2 miles around each police station. This, with the point feature of our Crimes layer, allowed us at a quick glance to see how well the police stations were located in comparison to where crimes were reported. But we needed something better than a quick glance to run an analysis so we did a spatial join that joined the Crimes with the buffer zone in which they occurred. We now had an attribute table associated with our buffers that allowed us to run queries. With this we were able to determine the percent of crimes that were reported in each buffer zone and determine most are within one mile of a police station. The next analysis we ran was to determine how many crimes each police station was nearest to. This was represented by using graduated symbols for the police stations.
The last step was to propose a location for a new police substation based on the results of our analysis. Our analysis showed there were a 127 reported crimes that occurred outside the 2 miles radius of a police station, mostly in three different areas. One of those areas included a cluster of 73 crimes so that seemed to me like the best place to locate a new police station.
Part 4 was creating density maps for three types of crime;
burglaries, homicide and sex abuse. We did this using the Spatial Analyst tool Kernel Density. I chose to go with a 1500 square kilometer radius for my search parameters for all three crimes and classified the results with natural breaks and 5 classes to display the results, showing the most crimes per square mile occurred in the darkest areas. This layer sat above a census block layer symbolized by population per square mile to show the density of the population, and beneath that was a layer of roads. Setting up the transparency of the layers to show all the information was a bit difficult. To get a clearer picture of how the density of crimes compared to the density of population was easy enough to do with the Swipe tool from the Effects tool bar, but it was more difficult to make this information clear in a print out or image file that couldn't be manipulated. Still, with a side by side comparison of the three crimes it is easier to see the population density showing through under the frames for homicides and sex abuse. This clearly showed most burglaries tend to occur in the more densely populated areas, whereas homicides tend to occur in the outer edges of the most densely populated areas, and sex abuse between the two, more in the mid-range of population density.
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