GIS 5935 Module 4

This week we have been working with Triangular Irregular Networks (TIN's). TIN's are a collection of vectors that are "constructed by triangulating  a set of verticies" (ESRI, 2020). TIN's are commonly used to represent ground surfaces and could be used to create 3D representations of an area. Slopes and contour lines can easily be derived from TIN's. 

Our assignment this week had us work with various TIN's and Digital Elevation Models (DEM's). We used local scenes in ArcPro to display and manipulate TIN's. We worked with varying elevation sources and vertical exaggeration to enhance imagery produced from TIN's. 

Along with working with TIN's we also worked with DEM's. We used a DEM and converted it to a TIN in order to carry out various analysis. One exercise had us run a suitability analysis to find the most suitable location for a ski run. Appropriate sites for ski runs had to have ideal elevation, aspect and slope features. To carry out the results we had to create various rasters and use a variety of tools including reclassify and weighted overlay. The weighted overlay tool combined everything together and allowed us to find suitable sites based on feature properties in the area. Below is a depiction of the result of my suitability analysis. 

The final part of our exercise this week had us compare data that was derived from TIN's. We used the Edit TIN tool to limit the boundaries of a TIN used to analyze the topography of a certain area. We could see how limiting the boundaries of a TIN modified the derived TIN edges and gave a more detailed picture of what the real topography is on the ground. 

Refrences

ESRI. 2020. What is a TIN surface?. https://desktop.arcgis.com/en/arcmap/latest/manage-data/tin/fundamentals-of-tin-surfaces.htm

 Module 3 GIS 5935

In this module we are carrying an assessment of completeness. Completeness is defined by Mordechai Haklay as "the measure of the lack of data" (Haklay, 2010). In our particular case we are comparing two road data sets to determine which one is more complete. We were provided two sets of road data and a grid to overlay over the given area. Our job was determine which road data set is more complete based on which data set has the greatest length in kilometers and which dataset has the greatest length within individual grids. 

To start our analysis I used the intersect tool to join each road data set with the grids layer. Carrying out the intersect joined the attributes tables of the both roads and grids layers and allowed me to split roads and road segments by grid. Once the roads were separated by girds the next step was to quantify all the roads data. I used the dissolve tool to dissolve individual road segments into a single feature within each of the 297 grids. The dissolve tool could be used because we did not need the other associated attribute data for any other analysis. If we needed to preserve other attribute data a different solution would have to be found. Once the total length of roads in each segment was calculated we used formulas to determine the percent difference in the data. A map with graduated colors shows the percent difference in the two data sets examined. Individual gird cells could be tabulated and a statement of completeness could be made. 

Below is my final map showing the percent difference between data sets. 

 

References:

Haklay, M. 2010. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and planning B, Planning & Design, 37(4), 682. 

Survey datasets. Environment and planning. B, Planning & design, 37(4),

682.

 GIS 5935 Module 2

This week we continue working with data accuracy. We were provided two data sets, each depicting the road system for the City of Albuquerque New Mexico. Our job was to use NSSDA data standards to determine the accuracy of the data that was provided and ultimate write an accuracy statement for each set of data provided. 

The first step was to designate at least 20 distinct test locations. These test locations would be the site of where each analysis of the roads would take place. We had to use at least 20 according to NSSDA standards in order to have a valid statistical analysis. Below is a map of my test points. I tried to spread my test points across the data set while finding intersections with 90 degree angles. The 90 degree angles make it a little easier to see where the actual intersection should be. 

After creating test sites we then created new feature classes and digitized the intersections of each intersection at the selected test site. Digitizing required placing a point at the intersection for each data set and a reference point that we selected for each test sight. Once the digitizing was complete we could then add XY coordinates to all of our digitized points. The XY coordinates were then placed in an Excel spreadsheet where all the calculations were carried out. The Sum, Average, Root Mean Square Error and NSSDA error were all calculated using simple formulas in the spreadsheet. Calculations were based on the reference points that we created using provided ortho images. 

The following are my accuracy statements for each of the datasets that were provided. 

Accuracy Statement for ABQ City Data:

Using the National Standard for Spatial Data Accuracy, the data set tested 4.93 meters horizontal accuracy at 95% confidence level.

 

Accuracy Statement for StreetMaps USA Data:

Using the National Standard for Spatial Data Accuracy, the data set tested 56.05 meters horizontal accuracy at 95% confidence level.

As is evident from the accuracy statements the ABQ City data was much more accurate than the SteetMaps USA data. Because we selected and created our own reference points on the fly there is likely some room for improvement but there would still likely be a significant difference between the provided datasets.