Addison's GIS Journey

 The first part of this lab focused on Boolean suitability in vector and raster. The goal was to create a suitability map modeling mountain lion habitat in Oregon. An elevation DEM was given and used to create a slope raster, then was reclassified into given suitability classes. The land cover feature was reclassified into given suitability classes. The soil polygon was converted to a raster then reclassified into given suitability classes. A distance to rivers raster was created and also reclassified. A distance to roads raster was created and also reclassified. The weighted overlay tool was then used and an equal influence between the 5 created rasters was used. The weighted overlay tool was then ran again with land cover at 20%, soils at 20%, slope at 40%, distance to rivers 10%, and distance to roads at 10%. The map layout below shows the results of the different weighted overlays.

 The lab this week focused on analyzing data, exploring the Marker Symbol Options, performing a raster mosaic, and locating and identifying attributes based on storm damage. A raster mosaic was created within the ArcGIS project and the provided rasters were added for pre and post storm. A domain was then created within the project with the following: inundation, structure damage, wind damage, and structure type. A new feature class was also created with the following fields: inundation, structure damage, wind damage, and structure type. The study area was then analyzed and point features were created based on the assessment. A coastline was then created and a buffer was used to examine patterns of damage in distances from the coastline.  Structure Damage Category Count of Structures 0-100 m from coastline Count of Structures 100-200 m from coastline Count of Structures 200-300 m from coastline No Damage 0 ...

 The lab this week focused on coastal flooding assessment. This included understanding elevation models and how they are used to delineate coastal flood zones, overlay analysis in vector and raster domains, and examining the effects of differences in boundaries. A laz file of Hurricane Sandy date pre and post was downloaded and then converted to las format using a new Spatial ETL tool. The appropriate format and dataset was then chosen in generate workspace which then opened the workbench. Within the workbench the run (green arrow) for the translation parameter values was used. DEMs were then created converting the las to a TIN and then the TIN to Raster tool. The Raster Calculator was then used to subtract the pre hurricane from the post hurricane raster. The map below shows the aftermath of Hurricane Sandy in the Mantoloking, NJ area using the above mentioned process. We also analyzed storm surge in Florida comparing a USGS DEM and a DEM derived from LiDAR. A raster was created f...

This week the assignment was to complete four ESRI 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 the Introduction to 3D Visualization course introduced using 3D data visualization in GIS. You can visualize 3D data in a map view, local scene view, or a global scene view. When visualizing 2D features in a 3D scene you must determine how to draw the features in relation to the ground source. Cartographic offset and vertical exaggeration are tools used to manipulate height variables.  The Performing Line of Sight Analysis focused on how to use the Line of Sight tool to perform an analysis. The tool determines the visibility along sight lines given terrain. This is represented by the input surface, and obstructions which are represented by the input features. The Performing Viewshed Analysis in ArcGIS Pro covered the Viewshed too...

The forestry LiDAR lab focused on creating DEM & DSM, calculating forest height, and calculating forest biomass from LiDAR data. A laz file from Virginia LiDAR application was used and converted to a las dataset. From this the point file information and LAS Dataset to Raster tool were used and a DEM and DSM layer were created. (Figure 1) The DEM and DSM were used in the minus tool to create a height raster layer. A histogram chart was also created to complement the height map to show the distribution of the values. (Figure 2) The LAS to MultiPoint and Point to Raster tool were used to help calculate the biomass density. The is null, con, plus, float, and divide tool were then used to calculate the density. In the divide tool the vegetation count result and the float result were used. (Figure 3) Figure 1 Figure 2 Figure 3

The first week's lab focused on using different techniques in crime analysis. Three hotspot maps of 2017 homicides in the Chicago area were created. These maps were then compared to the 2018 homicides in the Chicago area to determine which map technique was the best for predicting these crimes in the future.   The grid-based thematic hotspot mapping where the 2017 homicides and the homicide grid were spatially joined. The top 20% of grid cells were manually selected and exported to a new feature class.  The kernel density hotspot mapping where the kernel density tool was used and the data was reclassified to the mean values and the data was exported to a new feature class.  The local Moran’s I hotspot mapping where the cluster and outlier analysis tool was used and the high-high clusters were dissolved. I believe that the kernel density hotspot map is best for predicting future homicides as well as being the best representation of the information. The kernel density ...