Michell's GIS Journey

  This week’s lab introduced different methods for working with elevation data and applying them to real-world suitability analysis. I began by creating several raster surfaces from a DEM, including slope, aspect, and reclassified versions of each. These reclassified rasters were then combined into a weighted overlay to build a final ski run suitability map , using weights of 25% aspect, 40% elevation, and 35% slope. The result was visualized in 3D, applying vertical exaggeration, lighting effects, and clear symbology to highlight the areas most suitable for ski runs. Then, I created and explored TIN models as another way to represent elevation. By adjusting the TIN symbology, I was able to view slope, aspect, and contours, as well as examine the edges of the triangles to better understand how the terrain was being modeled. This showed how TINs preserve the accuracy of the original points while still allowing terrain characteristics to be visualized. Finally, I compared the TIN to...

  The objective of this assignment is to learn how to assess the quality of road networks. Specifically, we evaluate the completeness of road networks by comparing the total length of roads across two different datasets: the county Street-Centerlines and the TIGER road network. By examining differences in road lengths at both the county and grid levels, we can identify spatial gaps and better understand the relative coverage of each dataset. To perform the analysis, all datasets were first projected into a common coordinate system to ensure accurate distance measurements. Both road networks were clipped to the study area and intersected with grid polygons, splitting road segments at grid boundaries and assigning them to their respective grid cells. Lengths were recalculated for each segment, and total road lengths were summarized per grid for both datasets. Percent differences were then calculated using the county Street-Centerlines as the base, where positive values indicate...

  This week’s assignment focused on performing a horizontal positional accuracy assessment of two street datasets: the City Streets layer and StreetMapUSA. The goal was to quantify how accurately each dataset represents the true locations of intersections using orthophotos as reference data. By selecting 20 representative test points, digitizing their true locations, and comparing them to the datasets, we calculated RMSE values and generated formal NSSDA accuracy statements. This exercise reinforced key GIS skills, including feature selection, digitizing reference points, coordinate extraction, and error analysis. To assess this, 20 test points were selected across all quadrants of the study area. Reference points were digitized using orthophotos to represent the ‘true’ location of intersections. Coordinates for the reference points and corresponding points from both datasets were recorded. Differences in X and Y coordinates were calculated, Euclidean errors determined, and RMSE va...

 T his week’s assignment focused on evaluating the precision and accuracy of GPS data . To do this, we worked with a set of waypoints mapped multiple times at the same location, then calculated an average position and compared it to a known reference point. By creating buffers around the average location and analyzing both horizontal and vertical errors, we explored how GPS measurements vary and how close they come to the true position. This exercise helped demonstrate the difference between accuracy (closeness to truth) and precision (closeness of repeated measurements), as well as the limitations of consumer-grade GPS receivers. The map shows the projected GPS waypoints, the calculated average location, and circular buffers representing the 50%, 68%, and 95% precision thresholds. These buffers illustrate how far most points fall from the average location, helping visualize the spread of the data. Results: Horizontal Precision (68%): 6.0 m Horizontal Accuracy: 4.3 m Vert...