Dalton Inman

For this lab, we used three different interpolation methods: Thiessen , Inverse Distance Weighting (IDW) , and Spline , to map Biochemical Oxygen Demand (BOD) across Tampa Bay, and compared how each method handles spatial variation and data distribution. The easiest and most simple method was Thiessen interpolation , which assigns each location the value of its nearest sample point, which means the interpolated surface matches the exact values at the sample sites. This wasn't the best type of interpolation though as it creates abrupt transitions between the polygons which made it not as good at modeling the continuous environmental data like BOD. Spline interpolation fits a curved surface that must pass directly through all of the input points. This created a smoothed, and honestly, very visually appealing interpolation, but seemed to be overestimating or underestimating values in areas with low data. This is what was happening in certain splots of the study area which is wh...

In this lab, we explored Triangulated Irregular Networks (TINs) and Digital Elevation Models (DEMs) to understand how each can represent terrain and how they might be used for analysis. We started by draping a satellite radar image of Death Valley over a TIN surface, then used a vertical exaggeration to highlight subtle terrain features. This made it easier to see the relationship between jagged land and elevation patterns. Then we were tasked to build a ski run suitability model using a DEM with three types of rasters: elevation, slope, and aspect. Each factor was reclassified based on ideal ski conditions and then I used the weighted overlay specifically to combine the three reclassified rasters into a final suitability map as well as give weight to each type (25% aspect, 40% elevation, 35% slope), with the most suitable ski run areas appeared being along the upper mountain slopes. In the third part, I explored TIN symbology by experimenting with slope, aspect, contours, and triangle...

For this module, we were tasked with comparing the completeness of two road datasets for Jackson County Street Centerlines and TIGER Roads. We used a 1 km x 1 km grid and analyzed which dataset had more total road coverage both across the entire county and within each individual grid cells. We started with both datasets and projected the, into the same coordinate system and clipped to the county boundary to focus only on relevant roads. Each road network was intersected with the grid so that road segments were split wherever they crossed a cell boundary, then a new field was added to calculate the length of each segment in kilometers. I used the Spatial Join tool to sum the total road lengths for each grid cell for both datasets. These totals were combined into one table and a percentage difference was calculated to determine which dataset was more complete within each cell. I used the Select By Attributes tool was then used to count the number of cells. This resulted in Street_Centerl...

For this module, we were tasked with learning how to apply the National Standard for Spatial Data Accuracy (NSSDA) to find positional accuracy between two datatsets. We compared the City of Albuquerque street dataset with the StreetMap USA dataset , using orthophoto imagery as reference for accuracy. To start we had to make the study area easier to analyze so I divided the study area into four quadrants so the test points would be evenly distributed across the region. At least 20 points of intersections, 5 being in each quadrant, and making sure that the points were at least 10% of the study area’s diagonal apart. For each point, I created three versions, one digitized on the orthophoto, one snapped to the City street dataset, and one snapped to the StreetMap USA dataset.   After assigning IDs and adding XY coordinates using, I exported the tables into Excel to calculate the NSSDA accuracy at the  95% confidence level.  City of Albuquerque dataset: ...

In this module, we were tasked to explore GPS accuracy and precision using waypoint measurements. We worked with GPS waypoint coordinates collected at the same location and converted them into point data for mapping and then calculated the average position of the points and determined the radius needed in a series of ringed buffers to capture 50%, 68%, and 95% of the waypoints. After creating our buffer rings, we compared our average waypoint to a surveyed reference point to evaluate the relative accuracy and precision of the measurements. Horizontal precision measures how close repeated measurements are clustered together, while horizontal accuracy measures how close those measurements (or their average) are to the the reference location.   My results showed that the horizontal precision of the GPS points was 4.5 m , and the horizontal accuracy compared to the reference point was 3.24 m . This shows that most of the points fell within about 4.5 meters of the average location,...

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 ...

For this class, we were asked to do anything we want for GIS Day, so I decided to host my own little information session with my family at home. I realized that even though I tell my family the gist of what I do and want to do in the future, they didn't actually understand the extent. From their words "I make maps about the ocean" which I thought was extremely funny. I thought it might be meaningful to share some of the GIS work I’ve been doing in my courses and Internship so I can really show with them what I really want to do in the future (and also to have a nice little dinner out of it).  The event was all informal with using my laptop connected to the TV showing ArcPro software and my blogposts. I made part of the event to my sister who is a police officer, by showing the crime analysis work I recently completed for my GIS4048 class and how these techniques could help police departments identify patterns and predict areas of high crime risk which she thought was genu...

As I continue my GIS internship at the Escambia County Marine Resource Division, I’ve had the opportunity to expand my skills in geospatial data pertaining to marine and environmental management. Early in my internship, I was tasked on creating over 15 GIS maps based on boating data that identified accident sites, citations, and warnings. Here is an example of one of the maps I created:  More recently, I’ve moved to my other projects which were SAAD involves mapping SAAD (Suspected Abandoned and Derelict) Vessels in Bayou Chico. This is one of the more data heavy assignments I’ve taken on since it not only involves using data from years of collection, but also organizing the vessels that may have incorrect input data (Like Vessel name or registration). There are also pictures of most of the vessels that I can use to reference between vehicles. The reason why I'm working on this is these vessels pose environmental hazards and also complicate navigation for other boaters, so org...

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 ...