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GIS5935 Module 3.1: Scale Effect & Spatial Data Aggregation

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The final week of the GIS Special Topics course and we get to end it on Scale Effect & Spatial Data Aggregation. We got to explore how vector and raster data can be affected by scale and then how gerrymandering affects boundaries and communities. The first part of the lab gave us different hydrographic features of varying scales to learn about the relationship between scale and the line and polygon features of waterbodies throughout Raleigh, North Carolina. As expected, geometric features of the water lines and polygons were more defined at larger scales versus small scale, but it was cool to see just how simplified features became and how to calculate that through area, perimeter, and polygon counts! We also dived into DEM resolution in King Range Wilderness of California and how as the DEM cell size changed, from 1 m up to 50 m, the resolution and average slope calculated from that DEM dropped then gradually showed signs of tapering off. Part 2 brought us back to a country scale,...
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GIS5935 Module 2.2: Surface Interpolation

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Having an extra week to work on this lab was a blessing, especially after returning from a conference! Getting going in this lab took a little longer than expected since traveling interrupted a lot of momentum, but resetting now has allowed me to sit down and (ironically!) dive deeper into surfaces, specifically Surface Interpolation. The main interpolation methods we used in this lab were Thiessen, Inverse Distance Weighted (IDW), and Spline (regularized and tension). We got to interpolate elevation points throughout East-Central Kansas to start, then we found ourselves back in the waters of Florida’s Tampa Bay to evaluate Biochemical Oxygen Demand as a water-quality parameter. It was interesting to see how differently each method mathematically processes   the sampling points and interpolates them to create raster representations  of the data. Inspired by the multiple  “S” letters in its name, Thiessen, in my mind, is a s uper s imple interpolation method because...
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GIS5935 Module 2.1: Surfaces - TINs & DEMs

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Halfway through the course, we reach Module 2.1, which focuses on TINs & DEMs. While I mainly use DEMs in my work, this lab was a great opportunity to become more familiar with Triangulated Irregular Networks (TINs) and how they convey elevation. As a refresher, a "triangulated irregular network (TIN) is a data model commonly used to represent terrain heights" (Bolstad & Manson, 2022, p. 61). Our lab uses both TINs and DEMs to address elevation in California's Death Valley in a four-part series. In Part A, we used the Death Valley TIN as an elevation source, then overlaid a radar image on it with some vertical exaggeration. This allowed us to get used to working with both 2D and 3D spaces. Screenshot of Part A TIN and radar image overlay. From there, we delved deeper into 3D applications in Part B, where we assessed ski run suitability using elevation, slope, and aspect rasters. We used a starting DEM to create the TIN for the 3D elevation surface, as well as the ...
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GIS5935 Module 1.3: Data Quality - Assessment

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A long weekend this week has been a nice way to catch up on several things in life, and makes staying on track in this course a little more easier. Speaking of long, this week was all about assessing road network quality through completeness. Now, how were road networks deemed complete? Well, we went at lengths for that. Literally! Haha. Using a similar approach to the Haklay (2010) study, we compared two road networks, TIGER Roads and Street Centerlines, in Jackson County, Oregon by their total lengths in kilometers. Throughout the entire Jackson County, it was found that the TIGER Roads network had a longer length of roads than the Street Centerlines network, specifically 576.9km more. As mentioned in Haklay's study, completeness was deemed by Van Oort as the "comprehensive... coverage of real-world objects" (Haklay, 2010, p. 685). In our case, we analyzed further than just total lengths alone to get a full understanding of road network completeness. Like in Haklay...
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GIS5935 Module 1.2: Standards in Data Quality

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The next Special Topic in GIS for Data Quality is Standards, particularly in regard to positional accuracy! Using two different road datasets in Albuquerque, New Mexico, we gained a better understanding of how to determine and verify the horizontal accuracy of these road networks, based on the National Standard for Spatial Data Accuracy (NSSDA). One of these road networks was from the city of Albuquerque, while the other is from TeleAtlas and ESRI. Orthophotos from 2006 were used to ground these roads to a true reference. Screenshot featuring the 20 Sampling Locations for the New Mexico Study Area. Out of the gate, as the lab prefaced, the city street data already looked visually more accurate to the ortho imagery than the Street Map USA data. As described by Bolstad and Manson (2022, p. 617), the NSSDA consists of 5 steps for calculating horizontal accuracy; here's how I applied them to each step. 1.) Identify test points, which 20 points were created at various road intersections...
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GIS5935 Module 1.1: Fundamentals & Spatial Data Quality

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With the dawn of the next season, I'm hoping life will be calmer this Fall semester and that I won't be juggling too much on top of classes. We began the Special Topics in GIS course in mid-August, jumping right in with the first lab of Module 1.1: Fundamentals. This first module's lab covered Spatial Data Quality in two parts: precision versus accuracy and how to calculate them, as well as root-mean-square error (RMSE) and the cumulative distribution function (CDF). Precision and accuracy are foundational concepts crucial for distinguishing and properly interpreting data. Refreshers like this one are always welcome in my book. Speaking of books, as described by Bolstad and Manson (2022, p. 612), precision is the degree to which values are uniform and tightly clustered around an average; accuracy, on the other hand, is how close the value(s) are to the true benchmark value. In the first part of the lab, Part A, we used data points near Tampa, Florida, consisting of 50 waypo...
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GIS5100 Module 5: Suitability & Least Cost - Part 2

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It's the last leg of the race to the end, Module 5 - Part 2. This last part of Module 5 consisted of the corridor analysis , which showcased the migration path of black bears between two sections of Coronado National Park. Several tools were utilized, similar to the first Module 5 blog post. Reclassify was used to get our values where we needed them based on the provided suitability values. To note, we had to ensure the rasters were set to a 30cm cell size. Then, we applied the Weighted Overlay tool to specify percentages for each raster (landcover was 60% while elevation and roads distance were set to 20%), followed by the Raster Calculator tool and inverting the values to prepare for the final corridor tool, which accounted for the Coronado polygon layers . The map below represents my final results. Coronado National Park Black Bear Movement. A corridor analysis of the travel pathways black bears are likely to take based on roads, elevation, and land cover. Overall, I do wish I h...
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