There and Back Again

The first parts of this lab were relatively easy, but I ran into problems at the end with ArcGIS not recognizing the provided data, I got around this by snagging it from the R drive instead of Google Drive, starting a parallel project, and adding the layers to my current project from there. The first part was analyzing coastal flooding impacts using LiDAR and USGS DEMs that were provided. I downloaded and converted pre and post-Hurricane Sandy LiDAR data, built TINs and DEMs, and calculated the elevation change. I created a raster by subtracting pre and post values and used that to create a final map to show post Sandy changes. For storm surge analysis, I reclassified rasters based on elevation thresholds, then excluded low elevation areas not connected to the Gulf using Region Group and Extract by Attributes. I converted these to polygons and performed spatial joins with building footprints. I created fields to track whether buildings intersected each flood zone, then used Sele...

This has been one of my favorite weeks in the program so far. We did 4 different ESRI online courses: Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content Using Scene Layer Packages. In introduction to 3d Visualization, we learned to use 3D symbology to insert features like 3D trees, and moving waves, extrude buildings to an appropriate height from table data, and use a global scene to get realistic sun position and shadowing using coordinate data and time. In Performing Line of Sight Analysis, we learned to create lines of sight from observer points and check them for obstructions, and bound them by distance. We also discussed settings where it's appropriate to include the curvature of the earth and atmospheric refraction in calculations. In this example, we used fictional observers to a parade route: In Performing Viewshed Analysis, we went through an exercise where we looked at the heigh...

This was a frustrating lab, because I had trouble getting the initial LAS file to convert and render. It was provided in the module and we had instructions for getting it from the Virginia LiDAR site, but I had trouble with the first not loading and the latter not being accessible because of display setting issues on my computer. Once I got this figured out with help from UWF staff, I was able to do most of the lab in a short sitting. I used the LAS Dataset to Raster tool twice, first filtering to ground points for the Digital Elevation Map, and then to non-ground for the DSM. Using the Minus tool to subtracting the DEM from the DSM gave me a height raster with values ranging from around -5 to 163. This was the basis for the tree height map, and the DEM was a separate map. I created a histogram from the height raster to analyze vegetation height distribution. I used “value” in the histogram after some trial and error. I ran the LAS to Multipoint tool twice, for ground and vegeta...

The analysis followed a lab guide to explore burglary and homicide patterns in Washington, DC. First, I filtered the original crime dataset using a SQL query to isolate burglary incidents and exported this subset as a new feature class. I then spatially joined burglary points to census tracts to calculate burglary counts per tract, and used a Python formula to calculate burglary rates per 1,000 housing units. For the second part we created three hotspot maps of murders in Chicago. I created a grid based map of murders, isolating the top 20%. This involved dividing areas with homicides by 5 and selecting the top fifth from the total list, which seemed imprecise, because a lot of values equal to values in the top 20% were cut off, solely by their list rank despite identical values. I also created a kernel density map and used symbology to isolate values 3x the mean, and used geoprocessing tools to create a new polygon. The third hotspot map I created used local Moran’s I, ...