GIS5935: DEM Accuracy

The purpose of this lab was to determine the accuracy of a Digital Elevation Model. In order to do this, first, "true" test points had to be acquired. For this project, this consisted of field data collected using high-accuracy survey methods. The test points were essentially combined with the DEM using the Extract Values to Points tool in ArcGIS. Then, the elevation of the DEM at each point was compared to the true elevation. Using an Excel spreadsheet, the DEM's elevation was subtracted from the field data to find the difference, and this was used to find statistics for the Room Mean Square Error, the 95th percentile, and the 68th percentile. The results can be observed and compared to each Land Cover classification to find trends and consistencies within each type and help to determine any bias. Below are the results for this particular analysis:

Land Cover:                         A                             B                             C

Sample Size:                       48                           55                           45          

Accuracy 68^th:                    0.001                     0.023                     0.049                               

Accuracy 95^th:                    0.027                     0.194                     0.233

RMSE:                                   0.105                     0.181                     0.246    

                                              

Land Cover:                        D                             E                              Combined

Sample Size:                       98                           41                           287

Accuracy 68^th:                    0.051                     0.035                     0.029

Accuracy 95^th:                    0.214                     0.147                     0.185

RMSE:                                   0.394                     0.199                     0.276     

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:

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.