A. Starling's GIS Blog

       For this portion of the last lab in Applications in GIS, we were tasked with determining potential corridors for black bears between two protected areas within the Coronado National Forest, Arizona. Analysis I used the Reclassify tool on the “elevation” raster to indicate the suitability (“New” column) between elevation range: I similarly used the Reclassify tool on the “landcover” raster to indicate the suitability rating (“New” column) of landcover values: I then used the Euclidean Distance tool on the “roads” layer, then used the Reclassify tool to apply distance values. I used the Weighted Overlay tool to combine the output reclassed rasters for land, elevation, and road distance. I used the Raster Calculator tool on my weighted overlay raster with the below expression: (("S4WeightOverlay" - 10) * -1) + 1 I then used the Cost Distance tool on the “Coronado1” polygon layer and separately on the “Coronado2” polygon layer – these would both be “...

In this lab, we were tasked to determine the suitability of an area for housing development. For this, we must consider: l and cover, soils, slopes, distance to streams, distance to roads of the area. These five considerations will determine which areas in the location are suitable for development. Analysis First, I use the Reclassify tool to convert the values within the “landcover” raster into values of suitability. With the Polygon to Raster tool, I converted the “soils” polygon feature class into a raster. I then reclassified using the Reclassify tool turning soil class into suitability ratings . I used the Slope tool to create a slope of the “elevation” raster in degrees, and   the Reclassify tool to class slope (in degrees) into suitability rating. I used the Euclidean Distance tool to create a distance to rivers, and the Reclassify tool to class distance to rivers into suitability rating. I repeated the above steps for the “road” line feature class (creating a d...

       Hurricane Sandy (2012) path     This week’s lab in Applications in GIS allowed us to assess damage from Hurricane Sandy in Ocean County, New Jersey. To do so we used provided pre- and post- Hurricane Sandy rasters and compared both to each out using the Swipe tool. This allowed us to see the damage and determine where each parcel would fall under in categories of Structure Damage, Wind Damage, Inundation, and Structure Type.  ArcGIS Pro screenshot of structure damage in an area of Ocean County, New Jersey I created a new feature class “Structure Damage” as a point feature and digitized each parcel with a point indicating level of damage. I then created a new feature class “Coastline” as a line feature and digitized the coastline parallel to the study area. I used the Multiple Ring Buffer tool to create a buffer around the Coastline feature class with parameters to determine structures impacted within 100 meters, 200 meters, and 300 meters. Then ...

     Module 4 in Applications in GIS  focused on coastal flooding. For this week’s lab, we became familiar with procedures to assess coastal flooding and perform analysis on the differences between LiDAR and USGS data in application. First, we focused on the area of Mantoloking, New Jersey and how Hurricane Sandy (2012) impacted the area before and after the Category 1 storm event. We used data from NOAA for this. We used a Spatial ETL Tool to translate the .laz files that were downloaded from NOAA for the pre- and post- Sandy event into .las files. This helped us to start creating Digital Elevation Models (DEMs) of the before and after event data. Once we converted the .las files into TINs, we then used the TIN to Raster tool and used the Raster Calculator to determine the difference in erosion/accretion before and after Hurricane Sandy. Mantoloking, New Jersey coastal impact from Hurricane Sandy (2012) The red indicates erosion, while the blue indicates acc...

     Module 3 in Applications in GIS  provided a change of pace. This module prompted us to complete selected courses from ESRI (below): Introduction to 3D Visualization Performing Line of Sight Analysis Performing Viewshed Analysis in ArcGIS Pro Sharing 3D Content Using Scene Layer Packages It’s important to know that to create a 3D map, we need another coordinate than the typical x, y coordinates used in mapping. The z- value is needed to create height – not exclusively, as it can represent other attributes such as density or precipitation, but in these exercises, the focus is more so on elevation. We can use a 3D scene in ArcGIS, ensure there is an elevation source, and use extrusion to visualized data in 3D. Line of Sight analysis allows us to determine obstructions if a target point is hidden, as well as the visibility along the line of sight from an observer point. We performed Line of Sight Analysis using a Construct Sight Lin...

     This week in Applications in GIS focused on LiDAR (Light Detection and Ranging) and how to handle LiDAR data in ArcGIS Pro to calculate forest height and forest biomass. Using the decompressed version of the provided LiDAR data of the study area in Shenandoah, Virginia (Fig. 1), we created a Digital Elevation Model (DEM) and Digital Surface Model (DSM) to determine the tree canopy height of the study area. Fig. 1: LiDAR image of Shenandoah, Virginia study area Creating DEM Using the LiDAR .las file, right click in Contents -> Filters -> LAS Points = Ground The LAS Dataset to Raster geoprocessing tool was used to convert the LiDAR data into a raster format. This made a Digital Elevation Model (DEM). See Fig. 2 for this map output. Creating a DSM Using the LiDAR .las file, right click in Contents -> Filters -> LAS Points = Non-Ground The LAS Dataset to Raster geoprocessing tool was used to convert the LiDAR data into a raster format. This made ...

     This week’s lab in Applications in GIS allowed us to practice different hotspot mapping techniques – part of lab requested we use provided data to determine hotspot maps of Chicago, Illinois for 2017 and be able to determine the accuracy of each map to predict the area of homicides in 2018. We created (3) hotspot maps with (3) different methods; Grid-based Thematic , Kernel Density , and Local Moran’s I.   Grid-based Thematic Map (upper left corner, yellow), Kernel Density Map (upper right corner, purple), and Local Moran's I Map (bottom center, pink). Each map depicts hotspots of homicides in Chicago, Illinois in 2017. Grid-based Thematic: A Spatial Join was done by joining the features classes of the Chicago grid and 2017 total homicides (Join Operation: One to One, Match Option: Intersect). A Select By Attributes was used to select the grids that had the homicide count greater than zero and exporting that selected data into a new feature class. In t...