Dalton Inman

In this lab, we explored Triangulated Irregular Networks (TINs) and Digital Elevation Models (DEMs) to understand how each can represent terrain and how they might be used for analysis. We started by draping a satellite radar image of Death Valley over a TIN surface, then used a vertical exaggeration to highlight subtle terrain features. This made it easier to see the relationship between jagged land and elevation patterns. Then we were tasked to build a ski run suitability model using a DEM with three types of rasters: elevation, slope, and aspect. Each factor was reclassified based on ideal ski conditions and then I used the weighted overlay specifically to combine the three reclassified rasters into a final suitability map as well as give weight to each type (25% aspect, 40% elevation, 35% slope), with the most suitable ski run areas appeared being along the upper mountain slopes. In the third part, I explored TIN symbology by experimenting with slope, aspect, contours, and triangle...

For this module, we were tasked with comparing the completeness of two road datasets for Jackson County Street Centerlines and TIGER Roads. We used a 1 km x 1 km grid and analyzed which dataset had more total road coverage both across the entire county and within each individual grid cells. We started with both datasets and projected the, into the same coordinate system and clipped to the county boundary to focus only on relevant roads. Each road network was intersected with the grid so that road segments were split wherever they crossed a cell boundary, then a new field was added to calculate the length of each segment in kilometers. I used the Spatial Join tool to sum the total road lengths for each grid cell for both datasets. These totals were combined into one table and a percentage difference was calculated to determine which dataset was more complete within each cell. I used the Select By Attributes tool was then used to count the number of cells. This resulted in Street_Centerl...

For this module, we were tasked with learning how to apply the National Standard for Spatial Data Accuracy (NSSDA) to find positional accuracy between two datatsets. We compared the City of Albuquerque street dataset with the StreetMap USA dataset , using orthophoto imagery as reference for accuracy. To start we had to make the study area easier to analyze so I divided the study area into four quadrants so the test points would be evenly distributed across the region. At least 20 points of intersections, 5 being in each quadrant, and making sure that the points were at least 10% of the study area’s diagonal apart. For each point, I created three versions, one digitized on the orthophoto, one snapped to the City street dataset, and one snapped to the StreetMap USA dataset.   After assigning IDs and adding XY coordinates using, I exported the tables into Excel to calculate the NSSDA accuracy at the  95% confidence level.  City of Albuquerque dataset: ...

In this module, we were tasked to explore GPS accuracy and precision using waypoint measurements. We worked with GPS waypoint coordinates collected at the same location and converted them into point data for mapping and then calculated the average position of the points and determined the radius needed in a series of ringed buffers to capture 50%, 68%, and 95% of the waypoints. After creating our buffer rings, we compared our average waypoint to a surveyed reference point to evaluate the relative accuracy and precision of the measurements. Horizontal precision measures how close repeated measurements are clustered together, while horizontal accuracy measures how close those measurements (or their average) are to the the reference location.   My results showed that the horizontal precision of the GPS points was 4.5 m , and the horizontal accuracy compared to the reference point was 3.24 m . This shows that most of the points fell within about 4.5 meters of the average location,...