Christa's GIS Archive

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...

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...

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...

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...

We're starting the last Module 5 on Suitability and Least Cost analysis, where we walk through several hypothetical situations to identify the best-suited conditions for construction as well as to identify conservation zones. I started these last labs with high hopes of finishing at a decent time, so I wouldn't be juggling work, classes, and traveling -- but alas, this colossal titan of a final lab separated into 4 parts/scenarios got to me and has followed me out of state. Haha! For this first half of the lab, we created two maps: equal-weighting and slope-priority, which offer different perspectives for potential construction in Jackson County, Oregon. We reclassified several rasters: landcover, soils, slope, roads, and rivers. We then used the Weighted Overlay tool to set values in a range from 1-5, with all 5 layers weighted equally at 20% each for the equal-weighted map. Afterward, we applied alternative weight values to each of the previous 5 layers, which were  more sl...

We're back again on the East Coast, specifically in New Jersey, to continue where we left off. This week, we're building on Module 3's flooding assessment with Module 4: Coastal Damage Assessment. Thankfully, this lab did not throw me into a whirlwind in comparison to last week -- things went very smoothly! Module 4 consisted of 3 parts: tracking Hurricane Sandy from its formation and pathway until it made landfall, a Survey123 damage assessment form, and a case study in New Jersey's local community where structural damage was analyzed. Part 1 provided background on Hurricane Sandy. We prepared point and polyline data of the storm to track its pathway. This part brought me back to the Cartography course thanks to its section on symbology and labeling. I felt confident with this part since I had already made a fair amount of custom icon points for the choropleth map of wine consumption in Europe, where I created a unique grape icon from scratch. It's fulfilling to ha...