GIS 5935 Lab 11: Regression in ArcGIS

This week's assignment consisted of using tools in ArcGIS to perform a regression analysis. Previously, similar assessments to these had been carried out in Excel, but ArcGIS takes it a couple steps further. While in Excel you can determine all of the necessary elements for a regression analysis such as correlation, adjusted R-squared, P-value and everything else for all of your variables, ArcGIS helps to determine the performance of the analysis and which variables should be included or excluded.

The performance of the model is determined using the Ordinary Least Squares tool, which generates a regression analysis using dependent and independent variables from a feature class. The results of this tool can be viewed to help to determine which variables work better, and which ones could be biased or redundant and should possibly be excluded. One way this tool works very well is how it analyzes the residuals and determines spatial autocorrelation and whether explanatory variables are missing. If there is an issue, it advises to use the Spatial Autocorrelation tool on the residuals, which tells whether the residuals are randomly distributed or clustered. This is an especially useful tool for improving models because it's a simple, straightforward method to determine the distribution of each variable and can help pinpoint issues and potential problems with the regression analysis.

GIS 5935 Lab 10: Bivariate Regression

For this assignment, I used a regression analysis to determine the missing rainfall data for Station A between 1931 and 1949. In order to accomplish this, I first had to determine the slope and the intercept coefficient for the relationship between the two sets of available data for the variables. Then I multiplied the the slope with the value of Station B for each year, and added the intercept coefficient to determine the rainfall that was missing for each year for Station A. While something like rainfall is impossible to precisely predict, the statistics used here could be very useful in similar scenarios. It's not very different from recent previous assignments, like surface interpolation. It may not be precise, but it gives you a good idea of what the reality probably is or was.

The results can be seen below:

Year	Station B	Station A
1931	1005.84	1013.45
1932	1148.08	1133.81	Slope:	0.846171
1933	691.39	747.37	Intercept:	162.3421
1934	1328.25	1286.27
1935	1042.42	1044.40
1936	1502.41	1433.64
1937	1027.18	1031.51
1938	995.93	1005.07
1939	1323.59	1282.33
1940	946.19	962.98
1941	989.58	999.70
1942	1124.60	1113.94
1943	955.04	970.47
1944	1215.64	1190.98
1945	1418.22	1362.40
1946	1323.34	1282.11
1947	1391.75	1340.00
1948	1338.97	1295.34
1949	1204.47	1181.53

GIS5935 Lab 8: Surface Interpolation

In the first part of this lab, I compared the results of using Spline and IDW interpolation methods to create a Digital Elevation Model. This was done by using elevation data points as the input for each technique, then running each tool with the same parameters and comparing the results.

Overall, the difference in the results of the two interpolation methods wasn't extremely substantial, but there were some notable differences. Throughout a majority of the data, the difference in elevation between the two datasets is anywhere from 2 to 12 feet. But there are also several places where the difference is 30 to 40 feet. The areas with these larger differences, however, are mostly the areas without elevation data points, which shows how each interpolation process will give slightly different results. 

Below is map layout that shows the areas of difference in the two methods:

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: