GIS 5935: TINs and DEMs

This week's assignment was working with TINs and DEMs, with emphasis on the differences between the two. The TIN is an interesting data model, being made up of a network of triangles based on elevation points. Each triangle can vary in elevation, but slope and aspect remain the same throughout each one. The number and location of elevation points used to create the TIN are important. In areas with more topographical variance, it's necessary to use more data points, whereas in generally flat areas, not as many are needed. This creates a network of triangles of varying sizes.

The image below is an example of a typical TIN, with symbology shown for the nodes, edges, and contours.

Location-Allocation Analysis

This week's lab was a location-allocation analysis. To carry this out, we were first supposed to run a location-allocation analysis in the Network Analyst extension using a given set of data for distribution centers as the Facilities and customers as the Demand Points. In these results, however there was a number of customers that fell into market areas that differed from the distribution centers. To correct this, we were to reassign the market areas.

To carry out this part of the analysis, I had to use data provided that joined the demand points and the market areas and join this data to the demand points from the Network Analysis to determine which customers were assigned to a different distribution center. From there, I had to use the Summary Statistics tool to count each combination of Facilities and Market Areas to determine the facility that had the most customers in each market area. Finally, I had to create a new feature class for the newly assigned market areas.

Below is an image of the feature class of the new market areas:

GIS5935 Lab 5: Vehicle Routing Problem

For this analysis, we were to run a vehicle routing problem for a day's worth of pickups for a distribution center in south Florida. To do this, we were to first use 14 of the 22 routes (trucks), one depot which was the distribution center, 14 route zones, and there was a total of 128 orders (pickups). Once the VRP was solved with this data, it the analysis was run again to compare the difference with the use of two more trucks. This was done by changing the properties of two more routes to be included in the analysis.

The addition of two more trucks made quite an improvement. First, the original analysis left 6 orders unassigned, while the second one didn't leave any. Also, the second analysis only had one time violation, compared to the 10 that the first one had. The revenue also increased in the second analysis, from 32,000 to 33,625.

The image below shows following the addition of two more trucks: