Lab 5 - Spatial Accessibility Modeling

This week’s exercise was on spatial accessibility modeling, and had three parts. In Part A we worked through a number of GIS tutorials learning the different types of network analysis using the Network Analyst extension. In Part B, we measured the straight-line distance from psychiatric hospitals in Georgia to different counties to determine who had access to those services. In Part C, we performed a network analysis determining the accessibility of community college campuses to potential students before and after a specific campus closed.

In Part A, I worked through the tutorials assigned (as well as the two optional extra ones mentioned in the lab assignment), and I really got a feel for how Network Analyst works for each type of analysis. Each tutorial focused on a different type of analysis: the best route, closest facility, service area, location-allocation, origin-destination (OD) cost matrix, and the vehicle routing problem. Working on these tutorials and viewing the ESRI video provided helped me learn not only the concept, but applying it in ArcMap. I was especially impressed by how customizable the analysis can be, down to whether U-turns are allowed or not or the time of day if I wanted to simulate traffic.

In Part B, we wanted to determine the accessibility of psychiatric hospitals to the population of Georgia. Using a spatial join, I determined the distance from each county to the nearest hospital. From here, I could set a reasonable distance from the nearest hospital to be “accessible” and determine how many people lived within that area. Here, I wanted to create a Cumulative Distribution Function (CDF) of the data. I opened the DBF file in Excel and began to manipulate the data. Deleting fields unnecessary for the analysis helped make the table not as cumbersome to work with. Adding and manipulating the fields wasn’t so bad, but I am a bit rusty with creating graphs in Excel and it took probably more time than it should have. After creating a new field called cumulative percentage (which was the cumulative percentage of all the counties of Georgia as we moved through the table), I created a scatterplot graph of the distance of the nearest psych hospital (in miles) vs. the cumulative frequency (in %). I then converted the census tracts to centroids and performed a spatial join to determine the distance from each census tract to the nearest hospital. The end product here is another CDF of accessibility to the hospitals. This showed the accessibility of psych hospitals to those over the age of 65 and to those under the age of 65.

Part C was quite tricky. For this accessibility analysis, I wanted to consider two scenarios. One was the accessibility of a population to a total of 7 community college campuses. The other was for a total of 6 campuses after one of the original 7 had closed. We wanted to determine the impact the closure would have on the community college system. Using the Network Analyst extension, I created service areas of 5, 10, and 15 minutes from each campus for both scenarios. Below is my map of the service areas of the two scenarios side-by-side, with the campus to be closed labeled.

Next I used the closest facility analysis for both the 7 campus and the 6 campus scenarios, using the campuses as the facilities and the block group centroids as the incidents. The main output here is the travel time from each centroid to the nearest campus. At this point, it became a little tricky, as we needed to create a field for FIPS in order to join the tables. After joining the two tables, I was able to use the select by attributes tool to determine the number of college age residents within the service area boundaries, both before and after the closure of the campus. Then we wanted to answer how this closure affected travel times of those residents. Using another spatial join, I created a layer focusing only on students for whom the campus to be closed was closest and which campus would be closest after it had closed. After opening the DBF file with Excel, I was able to determine the number of students for whom the closed campus was closest (and how long they needed to travel to it), and where and how far the closest campus was to those potential students after they were displaced by the campus closure. I then created a CDF comparing travel times before and after the closure. As expected, travel times are consistently longer after the closure of that campus.

I learned a lot during this lab, and I enjoyed working with spatial accessibility metrics. The biggest difficulties of this lab were figuring out some of the processes required to answer some of the analysis questions. Once I was able to do that though, the calculations required came rather quickly. Another obstacle for me personally was creating the CDFs in Excel. I haven’t used Excel to graph often, so it took some time for me to arrange the extent of the data correctly. I enjoyed learning about the different accessibility analysis techniques, and I thought the tutorials and especially the ESRI video were excellent.