Geo Leah

  In this week's lab, we explored using elevation models to assess the effects of storms and coastal flooding. For the first part, we examined the change in elevation after Hurricane Sandy in New Jersey, which outlines where buildings were destroyed, where land was eroded, and where debris piled up. In the map below, the dark red areas are where the elevation decreased the most, and the dark blue areas are where it increased the most. We also compared a DEM from USGS and a LiDAR-derived DEM of part of the Florida coast to see what areas they would identify as being affected by a 1 meter storm surge. This part of the lab was pretty complicated and introduced me to some new tools, such as Region Group and Extract by Attributes. I struggled with table joins, so I selected by location and calculated fields to add the 1 and 0 values for flooded and not flooded to the buildings table. I was surprised that the USGS and LiDAR DEMs had such large differences between them. The omission and c...

In this lab we completed four Esri modules: Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content Using Scene Layer Packages.  A global 3D scene of San Diego with trees, parks, buildings, and marina, and sunrise illumination 3D Visualization I had no idea you could do 3D visualization in ArcGIS Pro! It’s so easy to add varied and detailed 3D symbology to layers, which we did with a scene of San Diego's buildings, trees, parks, and marina. I really enjoyed adjusting the time of day in global scenes to create different lighting conditions and looking at all the buildings and trees from multiple angles. I learned how to symbolize features using a procedural rule package, how to extrude 2D features based on attributes, and how to change the cartographic offset of point feature to ensure that they are visible on a variable landscape. Line of Sight Analysis In this module I learned how to perform line of si...

In this lab, we made maps of forest height and density for an area of Shenandoah National Park from a LAS point cloud collected using LiDAR. We created both a DEM with the LiDAR detected ground points and a DSM with the non-ground points, then subtracted the DEM from the DSM to get the height of the trees. I enjoyed getting to convert points to rasters and examining these rasters close up, since I don't think we've worked much with rasters yet. The Minus, Plus, and Is Null tools were all new to me too and really demonstrate the power of rasters! The most challenging part of this lab was trying to get the LiDAR scene to show up properly in my layout. It would never load all four quadrants of the points from the LAS cloud. After hours of adjusting the zoom and map frame extent and playing with settings, I was finally able to get this layout, although it isn't as polished as I'd like.

In this week's lab, we explored different methods of crime analysis and hotspot mapping. In Part A, we created a map of the burglaries per 1,000 housing units for each census tract in DC in 2018. I had a lot of trouble with the Calculate Field step. First the values needed to be changed to float in order to be divided. It took me a while to figure out how to use the code block in addition to the calculate field expression in the geoprocessing window. Then the 0 values for the Join_Count field of burglaries kept giving me Null results with warnings because it was unable to calculate with the 0 value. Here’s the code block that finally got it work (very simple for how long it took me to get there!): def calc(join_count, housing):          if join_count == 0:              return 0          else:              return float(join_count) / float(housing) * 1000 In Part B, we c...