GIS Portfolio - Michael Malone

  This weeks module focused on running flooding and damage analysis in New Jersey and in Florida (seen above) This required turning LAS files into TIN and manipulation with the Raster calculator. The Region Group tool was a new tool used to exclude the low lying areas not attached to flood zone immediately on the coast. The most difficult part for me was setting up the symbology for the structures seen above. I ended up setting the primary symbology to Unique Values and then using the expression builder to designate the above classes that searched for the query fields created earlier in the assignment. 

 This weeks lab was formatted differently then previous modules. The assignment required that four Esri learning courses be completed with minimum of 80% on each of the final quizzes . The courses required were as follows:  Introduction to 3D Visualization,  Performing Line of Sight Analysis,  Performing Viewshed Analysis  in ArcGIS Pro,  Sharing 3D Content Using Scene Layer Packages. The overall theme in this module was the use of 3D layers within a local or global scene. In manipulating 3D data the most used tool was Feature To 3D By Attribute, which converted 2D data to 3D using a specific attribute. For our purposes the Z attribute was used to determine the height of the 3D symbol. The Create 3D Object Scene Layer Package tool was used to package and export the scene for publishing. For Line of Sight analysis Construct Sight Lines was used to draw lines from an observer to the target feature. These lines were then run through the Line of Sight tool to determine which positions on t

 This weeks module focused on using LiDAR data to determine the height and density of a section of Shenandoah National Forest in Virginia. Mostly this involved converting the LiDAR data to a raster and running various tools to achieve the desired datasets. New tools included LAS Dataset To Raster, LAS to MultiPoint, Con, Plus, Float, and Divide. Additionally a histogram was created to display the heights of cells in the forest. Density and DSM: Height with Graph: LiDAR:

  Grid Overlay Kernel Density: Local Moran's I: This weeks module focused on crime analysis using the grid overlay, kernel density and local Moran's methods. The grid overlay method (image one) involved layering a grid of half mile cells over the city of Chicago, isolating the cells in the top 20% of total homicides in 2017 and using that to compare it to locations of all the homicides in 2018 to determine where homicides where recurring. The kernel density method (image two) creates a density map by searching for clusters of points in an area and assigning points closer to the center of the search area higher weight values. All area total homicides numbering more than three times the average were isolated. Local Moran's I (picture three) determines whether points inside a cell are either clustered or dispersed and then determines whether or not they are statistically significant by comparing each cells value to the cells around it. All cells with high clustering and high s