Michaela's GIS Journey

 Blog Post: Tools and Tips from the ESRI Exercises Completing the ESRI exercises has been a informative experience. Here’s a rundown of the key tools and tips I learned during the process, structured around the four specific exercises: Introduction to 3D Visualization (3 Hours) Key Tools: ArcGIS Pro 3D Analyst Extension: Essential for creating and analyzing 3D data, this tool allows for the transformation of 2D data into 3D visualizations. Scene Viewer: A powerful feature in ArcGIS Online and ArcGIS Pro, Scene Viewer enables the viewing and interaction with 3D data. Essential Tips: Realistic Representation: Use texture and elevation data to create more realistic 3D models. Navigation: Familiarize yourself with 3D navigation tools to explore your 3D scenes effectively. Layer Management: Organize your layers efficiently to handle complex 3D data and maintain a clutter-free workspace. Performing Line of Sight Analysis (1 Hour, 15 mins) Key Tools: Line of Sight Tool: Found in ArcGIS Pro, t

 Exploring Forest Canopy with LiDAR In this week's GIS lab, we explored the world of forestry using LiDAR (Light Detection and Ranging) technology. This exercise was particularly interesting for me since I could explore spatial analysis more in line with my Fisheries and Wildlife Science degree. Our goal was to analyze the forest canopy and terrain in Shenandoah National Park, Virginia, providing critical insights for forest management and conservation efforts. Using LAS files and creating DEM from LiDAR data Data Download and Preparation: We began by downloading and decompressing LiDAR data. This step involved using specialized software to handle .las files, which contain detailed point cloud data representing the forest's 3D structure. Creating DEMs and DSMs: We created Digital Elevation Models (DEMs) and Digital Surface Models (DSMs), not pictured, from the LiDAR data. DEMs represent the bare earth terrain, while DSMs include all surface features like trees and buildings.

Crime Hotspot Analysis in Chicago Brief Description of Analysis Steps Grid-Based Thematic Mapping: Performed a spatial join between the grid cells and totalhomicides_2017. Created a new feature class homicide_grid. Selected grid cells with a homicide count greater than zero and exported them to homicide_count. Selected the top 20% of grid cells with the highest counts (62 out of 311) and exported to hom_top20per. Created an integer field DISSOLVE and assigned a value of 1. Dissolved hom_top20per into a single polygon hom_top20per_diss. Kernel Density for Homicides: Ran the Kernel Density tool with: Input Point Features: totalhomicides_2017 Output Raster: homicides_kd.tif Output Cell Size: 100 feet Search Radius: 2630 feet Area Units: Square miles Output Cell Values: Densities Method: Planar Reclassified the raster to select values above three times the mean density (mean = 2.88, threshold = 8.64) and converted it to a polygon hom_kd. Local Moran's I for Homicides: Performed a spati