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:

GIS5935 Lab 2: Determining Quality of Road Networks

This lab was a test for the horizontal accuracy of two network datasets for the city of Albuquerque, New Mexico. The first one is a StreetMap USA network, compiled from TIGER 2000 data. The second one, ABQ_Streets is centerline data provided by the City of Albuquerque Planning Department. The independent dataset used was a digitized shapefile for all of the test points (27 intersections), that was created using digital orthophotos from 2006. First, 27 well-defined locations were determined that were intersections in both datasets. Then the "true" intersection was determined using the orthophotos. Then the X and Y coordinates for all three datasets (independent, StreetmapUSA, and ABQ_Streets) were added and and the NSSDA statistics were determined. Sampling locations can be seen below:

Horizontal Positional Accuracy:

Using the national standard for spatial data accuracy, the ABQ_Streets dataset tested 22.908 feet horizontal accuracy at 95% confidence level.

the StreetMap_USA dataset tested 147.857 feet horizontal accuracy at 95% confidence level.

GIS5935: Completeness of Road Networks

This assessment was meant to determine and compare the completeness of two road networks for the same county.
First, the total length of each road network was determined. From there, a grid polygon was used to determine the completeness on a more specific level. This was carried out by using a combination of spatial analysis tools found in ArcGIS including Intersect, Dissolve, and Spatial Join tools. Once the completeness for both networks in each polygon was determined, the differences in length between each network were found for every polygon, and can be seen in the choropleth map below.

GIS5935 Lab 1: Accuracy & Precision

This lab dealt with measuring horizontal accuracy and precision. To measure horizontal precision, an "average" location has to be determined from the given data. After that, distances from the observations to that average must be measured, in order to find what distance corresponds to a specified percentage of the observations or given data. To measure horizontal accuracy, the "true" location must be determined or given, and then the distance to the average needs to be measured.

An example of precision estimates:

In this case, horizontal and vertical precision are at 4.4 and 5.7 meters, respectively, while the horizontal and vertical accuracy have 3.25 and 5.96 meter discrepancies, respectively.  

GIS 5100 Lab 8

For Part B of this lab, I prepared the data for my Hurricane Sandy Damage geodatabase. For this particular step, I needed to create attribute domains for the .gdb. To do this, I manually added the domains in the Geodatabase properties, by right-clicking it in the Catalog and then clicking the Domains tab. This was my result:

For this next part of the lab, I examined the location of the damaged areas in reference to the coastline. To do this, I first created a polyline for the coastline. Then, using the Spatial Join tool, I determined which buildings were within 100, 200, and 300 meters of the coastline. These were my results:

Damage Category            0-100m                 100-200m                            200-300m

No Damage                        0                              0                                              2

Affected                              0                              6                                              17

Minor Damage                  0                              14                                           5

Major Damage                  10                           21                                           2

Destroyed                           15                           5                                              1

Total                                      25                           46                                           27

GIS5100 Lab 7

This week's lab involved working with DEMs to analyze coastal flooding. To determine the following flood zones for Honolulu, I created two new rasters from the original DEMs using the Less Than tool for sea level rises of 3 and 6 feet. To determine the area, I multiplied the cell count by the cell size. To get the depth of the flood zones,  I used the Extract by Attributes tool followed by the Minus tool. This was my final result for Part A: