Module 4 - Surfaces - TINs and DEMs

 In this lab, we were tasked with creating 3D visualizations of elevation models, creating and modifying a TIN using various datasets, and then comparing a TIN and DEM elevation model to each other based on their properties and derivatives. Another aspect of this lab was to explore contour creation utilizing a TIN and DEM. When creating contours with a TIN, the contours appear more angular, allowing for a straight-line-like contour that gives a uniform look. The DEM contours gave a more natural appearance. The contours appeared to conform to the natural terrain and showcase a downward slope, whereas the TIN contours did not. The reason the DEM contours appear as such is that the DEM is made up of uniform grid cells that store elevation data. The more cells you have, the more detail the DEM, which allows for more precise contours.

Here are the two different elevation models:

DEM

TIN

Module 3 - Data Quality - Assessment

 The goal of the accuracy assessment in this lab was to determine the completeness of road networks within Jackson County, Oregon. To conduct the analysis, the TIGER and Centerline roadways needed to be limited to the grids that were provided to us, which almost equally make up the entire extent of the county, with some outliers along the northern boundary. To do that, I utilized the modify features pane, specifically the clip tool and selecting the contain all lines option to clip out any part of the lines outside the grids. The next thing that needed to be done was to limit each roadway to a grid, even if it extends through multiple grids. This was done by utilizing the Split Features tool within the geoprocessing pane. This tool would split any lines into a new piece of data if the line transected across multiple grids. While these lines are limited to a grid, the appearance of the lines as one line is still present. Once the lines were isolated to their individual grids, it was now necessary to get the length sum for each roadway type in a grid. I utilized the summarize within tool in the geoprocessing pane to summarize the sum of each roadway in a grid. This allowed me to find the sum of both TIGER Roads and Centerline Roads to find the percent difference in each grid. To find the percent difference, I added the sums in Excel and utilized this formula: % 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 = (𝑡𝑜𝑡𝑎𝑙 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑐𝑒𝑛𝑡𝑒𝑟𝑙𝑖𝑛𝑒𝑠 − 𝑡𝑜𝑡𝑎𝑙 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑇𝐼𝐺𝐸𝑅 𝑅𝑜𝑎𝑑𝑠) / (𝑡𝑜𝑡𝑎𝑙 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑐𝑒𝑛𝑡𝑒𝑟𝑙𝑖𝑛𝑒𝑠) × 100%

Once I found the percent difference in each grid, I mapped out the differences in this choropleth map below:

Module 2 - Data Quality Standards

 The purpose of this lab was to determine the quality of road networks and to determine the positional accuracy of two road networks by comparison. To be able to determine the quality and positional accuracy of the road networks, we needed to understand and employ the methodology of procedures set by the National Standard for Spatial Data Accuracy (NSSDA). 

To begin the analysis, 60 test points in total were added to a project area in equal distribution. Twenty test points were placed on ABQ intersections, and 20 test points were placed on USA intersections immediately adjacent to the ABQ intersections. Then, 20 reference points were placed in the immediate vicinity of those intersections to get the most accurate measurement of the actual intersection. Here is what this looked like:

 Then to confirm the accuracy of the ABQ points and USA points, we needed to add point X and Y coordinates to each point to get the coordinates to calculate the accuracy. To begin calculating the accuracy, we needed to export the table to Excel, and once added to Excel, I formatted my table to find these calculations:

Diff in X is X(ABQ) - X(Ref), then Diff in X^2 is the value gotten from subtracting squared; the same steps were done for Y. Then to be able to calculate the sum, average, Root Mean Square Error (RMSE), and the actual NSSDA accuracy, you needed to add the Diff in X^2 and Diff in Y^2 values together. To get the average, I took the sum and then divided by 20, and then to get the RMSE, I square rooted the average, and then to get the final NSSDA accuracy, I multiplied the RMSE by 1.7308 to get the horizontal accuracy. 

To adhere to the NSSDA standards, here is my formal NSSDA accuracy statement:

Using the National Standard for Spatial Data Accuracy, the ABQ data set tested at 24.16 ft or 7.36 meters horizontal accuracy at 95% confidence level.

Using the National Standard for Spatial Data Accuracy, the USA data set tested at 201.52 ft or 61.42 meters horizontal accuracy at 95% confidence level.

Module 1 - Data Accuracy Fundamentals

 

The purpose of this lab was to understand the difference between precision and accuracy, be able to calculate vertical and horizontal position accuracy and precision, and then calculate root-mean-square error and cumulative distribution function. The horizontal precision identified is around 4.16 meters. The horizontal accuracy is difficult to achieve as it is unable to be truly perfect. To measure the horizontal accuracy, you would need precise survey equipment or have landmarks that identify the exact location. The most accurate someone can be is around 1cm of accuracy, and that kind of equipment is expensive. Horizontal precision is measured by multiplying the total number of values by the target percentile. If the percentile was a whole number, then I needed to count the numbers from top to bottom until the number was reached, then I got the average of that number and the one after to get the horizontal precision.