Dalton Inman

For the second part of this module, we were tasked to identify a least cost wildlife corridor between two sections of the Coronado National Forest. The goal was to identify potential movement routes for black bears by combining environmental criteria into a suitability model, converting that model into a travel cost surface, and then using corridor analysis to locate low cost connection areas between the parks.  I did this by creating three suitability rasters on a scale of 1 - 10 using the reclassify tool. These were the distance to roads, elevation, and land cover, with values assigned according to their relative importance for black bear habitat. I then used the Weighted Overlay tool to combine these layers, assigning weights of land cover 60%, elevation 20%, and distance to roads 20% creating a suitability map. I then inverted the suitability raster (10 – suitability) to create a cost surface where highly suitable areas became low cost travel zones. I calculated the cost d...

For this module, we were tasked with identifying areas that were the most suitable for future development using a suitability analysis method. We used a weighted overlay to assign relative ratings to each pixel in the study area based on five different criteria: land cover, soil, slope, distance to roads, and distance to rivers.  Each one was reclassified on a 1 to 5 suitability scale, where 5 indicated the best conditions for development. I created two separate suitability maps with different weighting strategies, with the first map having all five criteria equal (each at 20%), while the second map prioritized slope (40%) more heavily, to simulate the increased cost and difficulty of construction on steeper terrain.  Areas numbered as highly suitable (value 5) had characteristics like flat slopes, dry soils, and a far distance from water. When slope was weighted more heavily however, steeper areas were less suitable even if they had other favoring attributes.  Analysis's...

For this module, we were tasked with assessing structural damage caused by 2012 Hurricane Sandy along the New Jersey coastline using aerial imagery provided to us. The goal was to simulate a FEMA style damage assessment by manually digitizing structures and determining damage severity, and even creating a practice survey. The pre-storm and post-storm imagery was relied on heavily to inspect structures in a defined study area within Ocean County, NJ. A Structure Damage feature class was and was used to digitize each structure and classified its level of damage using FEMA coded domains (e.i. Structural Damage, Wind Damage, Inundation, etc.). Parcels were sometimes divided into multiple units (such as duplexes), which meant placing multiple points on a single building footprint to reflect the actual number of properties which can actually be seen from the image below. To analyze the severity of damage to proximity from the coast, I created a new Coastline line feature class and digitized ...

In this lab, we assessed and modeled coastal flooding risk using LiDAR and DEM data provided to us. The assignment focused on understanding elevation driven flooding, specifically to major events like Hurricane Sandy in New Jersey and an assumed 1 meter storm surge in Collier County, Florida . We started of with using pre and post Sandy LiDAR datasets of Mantoloking, NJ. After converting .laz files to LAS, I generated TINs and raster DEMs to visualize elevation change and subtracted the storm DEM from the post storm DEM. This revealed where land was lost to erosion and where sand or debris had been deposited.  Using a provided DEM, I could reclassify the raster to isolate areas at or below 2 meters in elevation, representing areas that has potentially flooded from Hurricane Sandy. After masking the data to the state boundary, I calculated the percent of Cape May County that was impacted and came up with this map below: Shifting to Florida , I compared two DEMs (LiDAR vs. USGS ...

For this weeks module, We completed four Esri training courses specifically on 3D visualization and visibility analysis. The four modules were: Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content Using Scene Layer Packages. In Intro to 3D Vis. course, I learned how to build 3D scenes in ArcGIS Pro. The key takeaway was understanding the difference between global and local scenes, and how to use elevation surfaces to create realistic terrain models. What I thought was really interesting from the course was learning to use 3D symbology which could possibly come in handy if I need to use it in my GIS Internship. In the Line of Sight exercise, we were taught how to use the Line of Sight tool to determine visibility between observer and target points. It felt really helpful to learn how to identify obstructions, adjust observer and target heights, and interpret sight lines in a 3D context since it would ...

In this module, we explored the use of LiDAR data for forestry analysis, focusing on the Big Meadows area of Shenandoah National Park, Virginia. We were taught how to decompress .las files, create Digital Elevation Models (DEM) and Digital Surface Models (DSM), and calculate tree height, canopy density, and biomass estimations from LiDAR data.  The first map I created displays tree heights across Big Meadows, with a color ramp transitioning from blue to yellow to show increasing height.    Tree Height Map w/ Histogram   This map mostly helps with visualizing the spatial variability of the forest structure. The map also has a histogram that shows the distribution of tree heights, with a mean height of approximately 54.4 feet, a median of 56 feet, and a standard deviation of about 20.5 feet. Most trees in the study area ranged between 30 to 70 feet in height, with very few exceeding 150 feet. This would seem to be the trend with what we would be expected from a mature,...

For this lab, we explored 3 different techniques to map and analyze crime hotspots in Chicago using 2017 homicide data. The main objective was to assess how the three different spatial analysis methods: Grid Overlay, Kernel Density Estimation, and Local Moran’s I clustering can be used to highlight areas of high crime concentration and could even be an accurate measure to predict where homicides occurred in 2018.  Each technique approached the concept of a "hotspot" differently. The Grid Overlay method simply counted how many homicides occurred within evenly sized ½-mile grid cells across the city. After ranking the grid cells by homicide count, I selected the top 20% with the most homicides and dissolved them into a single hotspot zone. This technique provided a very targeted result, covering a small area with the highest density of crimes per square mile as compared to the other hotspot maps. Grid Overlay Next, we practiced using Kernel Density Estimation (KDE) to create a ...

Hi everyone! My name is Dalton Inman, I'm a full time marine bio undergraduate at UWF, and just switched from the minor to the certificate in GIS and will be graduating this December! I really enjoyed the introductory classes and wanted to dive deeper into understanding and using GIS for marine conservation and rehabilitation. My aim in taking these GIS Courses for my certificate is to add to my current GIS skill set. I noticed some LiDAR labs later in this course which makes me excited since I've been interested in learning about it. I am currently an intern at the Escambia County Marine Resource Division where I help organize data in Excel and create maps from Boat Data and deployed artificial reef condition data. When I'm not spending time outdoors flying drones or snorkeling in the ocean, I typically spend it inside with my now fiancé!! and my three cats: Kiwi, Pumpkin, and Mango. I hope to learn a bunch from this course and from everyone I meet in GIS!  If you want to ...