My Geographic Information Journal

  For this project, I conducted a geospatial analysis of coastal flooding and storm surge impacts using LiDAR and USGS DEM datasets. The study focused on the aftermath of Hurricane Sandy, particularly in Mantoloking, NJ, and Collier County, FL. Using ArcGIS Pro, I processed pre- and post-storm LiDAR to detect elevation changes and map erosion. I also analyzed storm surge impacts using reclassified DEMs to identify affected buildings. One major takeaway was the stark difference in flood predictions between high-resolution LiDAR data and traditional USGS DEMs, highlighting substantial errors of omission and commission when using lower-quality datasets. I learned how DEM resolution and assumptions about storm surge height can significantly affect spatial analysis outcomes. Challenges included aligning datasets collected years apart, accounting for reconstructed areas, and simplifying assumptions like uniform surge height, which limit real-world accuracy. Nonetheless, this study emphas...

One tool I learned in this lab that I found very useful was using the elevation tab in properties to input a cartographic offset to manipulate the visualization of specific features that may be obstructed or out of sight. This allows the creator to focus on certain features or data so that the viewer can more easily see what is being shown. It is pretty simple, but without it one may be lost trying to fixate the viewer on the intended features. Another useful tip I learned was to use "TarIsVis =" and "Length 3D is" expressions during line of sight analysis. These expression will tell you if the target is visible or not (0=not vis, 1=vis) and give you information about distances from the target. I also learned how to use viewshed anaylsis to model the range of visibility in an area, and how to alter that range. You do this by running the viewshed tool to pick up the features you are modeling to create the viewshed. Then you use raster functions to calculate light cov...

  In this project, I created several raster maps from a LiDAR dataset to analyze forest structure and biomass. First, I generated a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) by filtering the LAS dataset to isolate ground and non-ground points, respectively. Using these rasters, I calculated a Height raster to estimate tree heights by subtracting the DEM from the DSM. Then, I created a Canopy Density raster to assess vegetation density by comparing vegetation and ground return counts. One of the main challenges I ran into was locating the “Filters” option for the LAS dataset. Initially, I didn’t see it in the Contents pane and struggled to isolate ground points. I eventually realized that I needed to use a LAS Dataset (.lasd) file and switch to the Appearance tab in the 3D Scene view to access point classification filters like Ground or Vegetation. Once that was resolved, I was able to proceed with creating the DEM and DSM accurately. Another minor issue was un...

   My name is Reese and I am in my fourth set of courses in this GIS program at UWF. I work full time as an Environmental Specialist for a local municipality, which is what my story map is all about ( https://storymaps.arcgis.com/stories/4cf090796d3f4682a018a53db55ee664 Links to an external site. ). I have helped with lots of GIS projects in my environmental career, and taken one GIS course in my Environmental Science bachelors degree before I started this program. I absolutely love geography, history, and maps in general. Because I have done so much GIS work in the environmental and natural resource field already, I decided that I wanted to become even more skilled in this realm, which is why I joined the GIS program here. My goal in going through this program is to be as valuable to my city in environmental and geographic information science as possible. I also want to be the person who is looked to when any complex questions could be answered through GIS.   I...

 

  We were tasked with writing a script that creates a geodatabase, copies all feature classes from a data folder into it, then populates a dictionary with the names and populations of New Mexico county seat cities. This was by far the largest and most complicated script project I have done so far. It was without direction so I had to outline what I was thinking and try lines of commands to see if they would work and put it all together in the outline. One of the main issues I was toward the beginning of my process. I didn’t really understand what setting arcpy.env.workspace was doing. I thought it was just setting a default folder, but it actually controls where ArcPy looks for data and sometimes even affects how tools behave. When it was pointing to the wrong folder, ListFeatureClasses() just returned nothing, and I had no idea why. I also had so many issues with file paths like forgetting to use raw strings (r""), accidentally adding extra slashes, or copying paths from Win...

  These are the Notebook script output results from this weeks lab pt 2. These results are telling me that my script successfully ran AddXY, Buffer, and Dissolve tools to a hospitals shapefile that I had in a map on ArcGIS Pro.  This was a bit of a difficult task for me. I had to sit and just consider the task at hand for a while. At first, I was just thinking about what I already had, and where I needed to get it, in terms of ArcGIS Pro tools and functions. I am pretty skilled with ArcGIS Pro, so doing these tasks would be pretty easy if I were not scripting it. However, I was trying to keep in mind not only all of the scripting language rules, but also whether I needed to use try-except statements to run through the script and catch issues. I then broke my individual tasks down and focused on each separately. First was addxy coordinates. I understand this function in Arc Pro, but how do I script it in python? I went back into my notes and the textbook and found the main comm...

  This weeks lab we expanded on debugging problems in a script. The first two parts felt pretty natural, like common sense. Making cases of text match each other, being consistent, proper spelling, etc. The third part felt a bit more uncomfortable. We had to find where we thought there was an error and use try-except statements to wrap around the script so the interpreter would catch potential issues, run the script anyways, and tell us where the issues were. This task took me a while to get correct. I had to run some different ideas and go back to the module resources and drawing boards before I finally got this to work right. 

  In this lab we learned how to insert written script into ArcGIS Pro notebook and then find bugs and fix them This was different than anything I have done before, so it took me a while to differentiate a spot where an int needed to be a string. Once I finally figured this out, the rest felt easy. We then had to write our own code to take the written script of the dice game and make it play out and then remove a specified integer from it. A lot of the language, especially complex statements like having the program take the count of the # of times the integer appears and remove them from the list. Some of the ways you put these statements together still feels different, but I am getting the hang of it. I tried to make my script of the game sound a bit more interactive like a video game, so I made it say "Removing..." as if it was actively working on doing this task. 

  I thought this module lab was fairly easy to comprehend and perform, up until creating the flowchart for the math problem. We input an already written script into Python IDLE, which created our course and module folders for us. I thought this was a pretty interesting concept because it standardizes things that would be somewhat time consuming throughout the course. Now we don't have to focus on naming conventions as much, as the entire course folder is already created. The flowcharting, though, I found to be a bit more difficult. I have never done anything like this, but while reading and watching the module materials, it seemed pretty straight forward. Once I actually was presented with the problem of converting 3 radians into degrees, I became confused about how to break it down into a flowchart. Was I supposed to detail each individual number and unit first? It was feeling very foreign to me. So, I went back through the materials and watched some youtube videos about this conc...

  In this lab we learned how to depict elevated precipitation data accurately using both continuous tone and hypsometric tint approaches. We used the hillshade effect to present the data in a way that reflects the elevation in a 3D format. We also overlayed contour lines on top of the hypsometric tint to display the different classes being presented. We learned about the PRISM method of collecting precipitation data with climate and landscape adjustments. 

  This module we learned how to create a choropleth map by using two parts of the data from a set and creating graduated color symbology that reflects the ratio of these two parts. This map was wine consumption to population density in each country. We used the histogram to evaluate the breaks in the data and then utilized classification methods, data exclusion, labeling, and symbology to make the map portray the data in an accurate and interesting way. I used natural breaks (jenks) classification method, which really groups together the data in an accurate way that isn't monotone. It gives the viewer a sense of where the countries group together on this topic. 

This weeks lab taught about types of classification schemes and thematic maps. The task was to create a layout to compare and contrast these different methods and reasons for using each. The first of the two layouts was created by inputting the percentages of Age 65 Up census data for Miami-Dade counties. It uses 4 of the classification schemes; natural breaks, quantile, equal interval, and standard deviation. This evaluation of percentages shows us how the different methods highlight certain aspects of a data field, and that they can be utilized in appropriate situations.   The second layout shows the same census data but with the input being based on the population count normalized by area. This type of data scheme is used to highlight population density. It shows where actual numbers are, not just percentages of various amounts. 

 Good morning, Maps and everything that comes with them can be informative and well thought out as possible, but be lacking in the visual appeal that portrays a message or tells the story on the surface when first reaching the audience. These visual appeal can be difficult to achieve when map components look clustered or even too empty in some areas. That is the purpose of doing this lab was to practice strategies and steps to giving a map the needed visual appeal to portray the right message in the easiest way. The easier the map is to interpret for its given audience, the better its message is. With this specific map, I utilized layers from the class repository drive, or from ArcGIS Online, to give a combined look at Florida's main river systems, its large swamp/wetland areas, and its major cities. After adding all of the layers, I went through each and picked out the information I wanted to be showing without clogging up the map. Then went through and made all of their symbology...

 This assignment was to evaluate a well done map and a poor map based off of cartographic design principles.  1. -This was the example of a well designed map I decided to evaluate. Overall this map was created with purpose, and utilized colors and symbols that portrayed that purpose. It used neutral colors for the topic of hazardous fuels treatment, as to not evoke emotions about this from the viewer. It uses topographic design to show a relative size of these areas to the mountainous landscape, also giving the viewer an idea of the lands value. It gave an appropriate amount of information for a college-educated audience, likely working in the energy, conservation, environmental, or land management fields. It did a good job of highlighting the focus-area, and greying out irrelevant areas. Texts were easy to read, but not over-bearing. The symbol for highways and rivers, however, looked too similar and should probably have been different colors. The layout of all the pieces are...

  My name is Reese and I am in my third set of courses in this GIS program at UWF. I work full time as an Environmental Specialist for a local municipality, which is what my story map is all about (  https://storymaps.arcgis.com/stories/4cf090796d3f4682a018a53db55ee664 Links to an external site.  ). I have helped with lots of GIS projects in my environmental career, and taken one GIS course in my Environmental Science bachelors degree before I started this program. I absolutely love geography, history, and maps in general. Because I have done so much GIS work in the environmental and natural resource field already, I decided that I wanted to become even more skilled in this realm, which is why I joined the GIS program here. My goal in going through this program is to be as valuable to my city in environmental and geographic information science as possible. I also want to be the person who is looked to when any complex questions could be answered through GIS.   I...

  This map was created as a LULC for the city of Germantown, Maryland for educational purposes only. I classified the pixels of this image by creating classes from "seed", which involved finding different examples of class types and essentially making all of the similar pixels across the image have the same color. After doing all the required class types, I realized there was some classes being grouped together due to the band combinations being used. I utilized the histogram and mean plot tools to figure out the best band combination (im my opinion) to separate all of the classes and make them easier to pick out. This one ended up being Red - 5, Green - 4, Blue - 3. The biggest issue with these classes is separating the roads from the urban areas, but I think it is somewhat easy to pick out the roads as they are a different shade of purple. The map also has the calculated areas of each land use type, shown in the legend. The lower right map is the Distance Image that I creat...

  This layout was made for educational purposes to learn how to identify features through only descriptions of reflectance properties. I was given criteria for each type of feature to find, such as spikes in pixel values. The first feature (top left) is a very large river or lake of some kind. It was the largest spike on the histogram as well as the furthest left, meaning that it was very large and dark! The second feature (bottom left) is a snowy mountain top. This type of feature has large contrasts in brightness and size depending on what layer is being viewed. The third feature (bottom right) is of a water sandbar area that has large spikes in brightness all the sudden. This is because water is typically very dark, but having a large amount of sand right under the water will quickly cause it to change brightness. 

  This map was for educational purposes to help learn to find data about features of an image through ERDAS Imagine. I learned how to capture a specific location on a larger map and then find area data about that location. Once the new area had been clipped out and created as a new layer, all new data was able to be extrapolated about it. This makes it helpful when looking at an image to understand how much of it actually being covered by these different features, instead of having to guess. 

  This was my first time doing a LULC map. I started on the largest areas I saw, which were the natural areas. To the left of the map was bays and estuaries, streams and canals, forests and wetlands. I clumped some of these together because they looked to all be intertwined into a couple different habitats. I then followed some different streams into the city and found any natural areas within the city. Then I started at the residential neighborhoods in the bottom left of the map and followed it until it was obstructed by a commercial building, industrial complex, or something else other than residential houses. I labeled these other areas as I went. Then I did the main road and some other miscellaneous areas that hadn't been filled out yet. This was an interesting project that taught me about the layout of a city or overall area.