As part of my GIS Internship course, I was tasked with developing a professional GIS portfolio to showcase my skills, experience, and growth in the field. This portfolio represents a collection of my best work from the University of West Florida GIS Certificate Program, along with real-world applications from my professional experience.
Throughout this process, I focused on creating a clean, accessible platform that highlights a range of projects including cartographic design, spatial analysis, LiDAR processing, and geoprocessing workflows using ArcGIS Pro, ModelBuilder, and Python. Each project included in the portfolio demonstrates not only technical proficiency, but also the ability to apply GIS to real-world scenarios such as environmental analysis, infrastructure mapping, and disaster assessment.
One of the most valuable aspects of this assignment was reflecting on my progression in GIS—from early coursework to more advanced analytical and applied projects. Building the portfolio reinforced the importance of clear communication, thoughtful design, and effectively presenting technical work to a broader audience.
Overall, this was a highly rewarding experience that helped solidify both my technical skills and my ability to present them in a professional and meaningful way.
For my GIS Day event, I had the opportunity to discuss GIS in two different settings: first with friends while on a weekend trip, and then with my family which including my wife and son.
I had the opportunity to explain what I do as a GIS Analyst to a group of friends who live in the same city I now work for. Like many people outside the field, they had heard of “GIS” before but did not really understand what it meant or how it is used in everyday life. I tried to approach it in a way that connected directly to their daily experiences.
I started by explaining that GIS, or Geographic Information Systems, is essentially a way of combining maps with data to help people better understand what is happening in a specific location. I told them to think of it as layering different types of information, like utilities, zoning, crime data, or infrastructure on top of a map so that patterns and relationships become easier to see and analyze.
To make it more relatable, I pulled up some of the city’s public-facing web maps and dashboards. This really helped bridge the gap between theory and real-world application. For example, I showed them interactive maps where they could view things like flood data, zoning and future land use, and parcel data. I also showed them a city development activity dashboard that presents near real-time data in a visual format, such as charts and maps, which help city staff and the public quickly understand what is going on without digging through spreadsheets. This dashboard shows all the development activities going on in the city and provides statuses and contact information as well as access to documents such as architecture/landscape plans and surveys.
What seemed to resonate most with them was how practical GIS is. They began to see that it’s not just about making maps, it is about supporting decisions that affect their community. Whether it is being part of the city’s emergency response team, assisting code enforcement officers in the field, or allowing residents to explore city data online, GIS plays a role in improving efficiency, transparency, and communication.
By the end of the conversation, they had a much clearer understanding of what I do and why it matters. It was rewarding to see that shift, from thinking GIS is something abstract or technical to recognizing it as a powerful tool that directly impacts the city they live in. This experience also reminded me how important it is to communicate technical concepts in a way that is accessible and relevant to a general audience.
For my discussion with my family I started with the Esri video titled “What is GIS.”Links to an external site. I always thought this was a solid introduction for those outside the field. Naturally this video generated some good questions which I was able to answer more clearly by showing them ArcGIS Pro in action. Since a lot of our data at the City of Port Orange is publicly available on ArcGIS Online I was able to show them some data sets that I will be working with in my position.
The data I showed them included our city’s sewer and water network. Since we live within the city, it was especially impactful to zoom into our subdivision and highlight features they were already familiar with, such as manholes and drainage inlets on our street that they have seen many times. I also introduced them to our stormwater system. Because we have a swale bordering our backyard, along with a few retention ponds and drainage inlets nearby, these served as great real-world examples to demonstrate how GIS is used to map and manage stormwater infrastructure. They were amazed at how the attribute table made it possible to quickly answer questions, like how many drainage inlets exist in the city or within a specific area, and the total length of water lines, all with just a few mouse clicks.
Overall, this was a very successful experience. It not only helped others better understand GIS, but also gave me the opportunity to refine how I communicate its value to a non-technical audience. Seeing their interest and curiosity grow throughout the discussion confirmed that GIS is most effective when it is tied to real-world, relatable examples. Moving forward, I will continue to focus on making GIS accessible and meaningful to those outside the field.
The industry topic I chose focused on the role of GIS in public safety and emergency managment. I selected this topic because, in my new role as a GIS
Analyst, I will be part of the City of Port Orange, Florida’s Emergency
Management Team. This was an interview with Richard Butgereit, GISP, Information Management Section Head at Florida Division of Emergency Management. The interview provided several important insights that are
especially relevant to this work, including the following:
Richard talked about the importance of
coordination and information sharing among emergency management agencies. He
stressed that successful disaster response relies heavily on collaboration, not
just individual agency efforts.
He also emphasized that proactive
planning and preparedness efforts were especially important and required the
most time. Emphasis on risk assessment, training, and pre-event planning being
key for success. He reinforces the idea that emergency management is an ongoing
process, not something that begins only when a disaster strikes.
Finally, he emphasizes the growing role of data and technology in
emergency management decision making. He made it a point to say that one of his
jobs in emergency management was to provide data in as many ways/formats as
possible. Even though this interview is likely slightly dated, the idea of
providing data in multiple formats is still very relevant in 2026. I would
think, as technology and user expectations have expanded, emergency managers
now need to deliver the same information through dashboards, web maps, mobile
tools, and raw datasets. While the formats have changed since this interview,
the responsibility to make data accessible to different users has likely only
grown.
As mentioned above, I am pleased to share that I have recently accepted a GIS Analyst position with the City of Port Orange, FL. I am excited to begin this new role and look forward to serving the citizens of my hometown. I believe this position will be a great fit, and I am eager to apply the skills I developed at UWF while continuing to grow as a GIS professional.
I recently updated my LinkedIn profile, which had not been revised in several years. I added the courses I completed at UWF and ensured that my blog/portfolio site is clearly visible and easily accessible in both the contact information and education sections. I also refined my listed skills to better reflect my GIS experience, particularly in my recent roles as an Environmental Scientist and GIS Cadastral Mapper. These updates helped create a cleaner and more professional overall presentation.
Additionally, I have begun ESRI training on creating ArcGIS StoryMaps. In the coming weeks, I plan to continue expanding my knowledge through additional StoryMaps training and ArcGIS Online coursework as I prepare to create and maintain web applications in my new role.
This week we were tasked with either revisiting our initial GIS dream job or conducting a new GIS job search to check out what types of jobs are currently available.
Since I have been researching and checking GIS jobs for several years now I initially thought I wanted a job that would utilize my background in archaeology but since beginning the certificate program at UWF over time I have started to lean more towards a GIS position that is more broad. One that I could utilize the variety of the skills I have now acquired through the program at UWF and recent positions I have held since starting at UWF. As I was recently searching for jobs I came across a GIS Analystposition in my hometown of Port Orange, FL.
The position appealed to me because the job description described much of what I was looking for since it wanted someone who had a diverse background to support various city departments. Some of the essential functions included developing and maintaining web maps, applications, and interactive custom dashboards, converting various data types, querying SQL databases and communicating internal and external technical resources to resolve conflicting data issues and provide guidance on best methods to apply results derived from GIS driven data to name a few. Most of these I have experience in either from coursework or from prior positions I have held. I think the only function I don't have much experience in is creating and maintaining ArcGIS web maps, applications and interactive custom dashboards. As far as the skills, knowledge and abilities listed I also checked most of those boxes with either coursework or prior positions I have held.
In addition to being the type of GIS job I have been searching for, the idea of potentially using GIS to help support my hometown's community efforts in various functions is very exciting to me.
Needless to say I applied for the position and interviewed for it this past Monday morning. I'm happy to say that I received an offer later that afternoon and accepted the position!!!
This semester I am completing my GIS Internship course as my
final requirement for the GIS Graduate Certificate at UWF. I recently held a position
as a Cadastral Mapper so I am using that experience for my internship and will
continue to take some ESRI classes during the semester.
I have been a member of a few groups since starting my GIS
journey at UWF in 2023. I have been a Friend of FLURISA since I started the
program in which I have attended some webinars when available. This past year I
attended the Central Florida GIS Workshop at the Ocean Center in Daytona Beach,
FL. This is a great event filled with presentations on a variety of GIS topics,
ESRI trainings, a map and app gallery, and exhibitors representing private sector companies as well as public sector organizations. This workshop is a great way
to learn about a variety of GIS topics and applications as well as network with
other GIS professionals. I was able to meet Amber Bloechle and Dr. John Morgan
at the workshop, so that was great to connect in person!
Since I have some experience working with the Brevard County
Property Appraiser as a Cadastral Mapper, I decided to join the Florida
Association of Cadastral Mappers (FACM). FACM is dedicated to promoting, educating, and
assisting in the development of cadastral mapping in Florida. They also offer a
series of courses designed to support and educate cadastral mapping professionals.
In addition, since beginning the program in 2023 I have
maintained an ESRI Professional Plus/Student user license. This has allowed me
to take ESRI courses during down times and stay familiar with ArcGIS Online. Also included in the license is the ArcUser publications which are great for keeping up on the GIS industry. I
highly encourage anyone in this program to take advantage of this license as the
cost is minimal at $100/yr.
The sixth and final module in Special Topics in GIS covered the topics of scale effect and spatial data aggregation. Understanding how we represent geographic data is crucial in GIS. In this lab, we explored scale effects on vector data, resolution effects on raster data, and how these concepts connect to the issue of gerrymandering.
Vector data (points, lines, and polygons) can appear very different depending on the scale. At a small scale (zoomed out), features may look simplified or smoothed. At a larger scale (zoomed in), more detail is visible, which can reveal complexities or errors not seen before. This affects how spatial relationships are interpreted, and can even change analysis results when aggregating or comparing areas.
Raster data stores information in grid cells. The resolution refers to the size of these cells, high resolution means smaller cells and more detail, while low resolution uses larger cells, which may miss important features or patterns. Resolution impacts the accuracy of measurements like land cover, elevation, or temperature, especially when zooming or resampling.
The final topic we explored was gerrymandering, which is the manipulation of political district boundaries to benefit a specific party or group. It often results in bizarrely shaped districts that dilute or over-concentrate voters. This undermines fair representation. In this part of the lab, the Modified Areal Unit Problem (MAUP) was explored in the context of political districts. According to ESRI, MAUP refers to a type of statistical bias that can arise during spatial analysis of aggregated data, where applying the same analysis to the same data yields different results depending on how the data is grouped or zoned.
One way to measure gerrymandering is through compactness. A common metric is the Polsby-Popper score, which compares a district's area to its perimeter. A lower score suggests a less compact (and potentially gerrymandered) shape. Below is a screenshot of a North Carolina District 12 that had the lowest Polsby-Popper score in the continental U.S. according to my calculations. Its irregular shape suggests it may have been drawn with intent beyond geographic or community boundaries, a potential sign of gerrymandering.
By examining the geometry of voting districts and understanding how scale and resolution affect spatial data, we can better identify and challenge distortions in political representation.
Overall, this lab was a great experience highlighting how the way we structure and visualize spatial data through scale, resolution, and boundary choices can deeply influence analysis and real-world outcomes. From population patterns to political fairness, understanding these effects is essential for responsible GIS work and informed decision-making.
The fifth module in Special Topics in GIS introduced the topic of surface interpolation. In this lab, we explored the use of several interpolation techniques to create continuous surfaces of water quality across Tampa Bay. Interpolation is valuable because it allows us to estimate values between sampling points and better visualize spatial patterns. However, each technique approaches the problem differently and produces distinct results.
Thiessen polygons assign each location to the nearest sample point, which is simple to apply but results in abrupt boundaries that don’t reflect smooth changes in water quality. Inverse Distance Weighting (IDW) provides a more gradual surface, giving greater weight to nearby samples and reducing the blocky appearance of Thiessen. Spline goes further by fitting a smooth, curved surface through the data, producing a visually appealing result but sometimes creating unrealistic peaks or sinks in areas with clustered high values or sparse sampling. These differences highlight the importance of choosing an interpolation method that matches both the data characteristics and the purpose of the analysis.
Below is a screenshot of my results using the Spline Tension technique:
Overall, this exercise showed that while all interpolation methods can create useful surfaces, their assumptions and behaviors vary widely. Understanding these differences is key to interpreting the results and making informed choices for an analysis. This lab gave me a stronger understanding of how interpolation works and why the method you choose matters. It was interesting to see how the same water quality data could look so different depending on the approach.
The fourth module in Special Topics in GIS introduced the topics of creating, editing, and analyzing TINs and DEMs. The goal was to explore how different elevation data models represent terrain and how these representations can be used in spatial analysis.
One of the portions of this week's lab involved utilizing a DEM to develope a 3D ski run suitibility map. By calculating three critical terrain variables: elevation, slope, and aspect, these layers was reclassified to reflect suitability for downhill skiing. For example, higher elevations scored more favorably, slopes between 20° and 45° were rated highly suitable, and north-facing aspects were given priority. These reclassified layers were then combined in a weighted overlay, where elevation was weighted most heavily, followed by slope and aspect.
The result was a ski run suitability map (shown below), which highlights the best areas for potential ski development. Areas in darker colors represent more favorable conditions, while lighter areas represent less suitable terrain. This exercise not only illustrated how elevation data can be modeled differently with TINs and DEMs, but also how those models support real-world decision making when combined with spatial analysis.
Another portion of the lab provided a point feature class that was used to create a TIN model. Contour lines (100m) were then visualized by modifying the symbology. Next, using the Spline tool, the point feature class was used to create a set DEM based contour lines. The two sets of countour lines were then analyzed and compared. Below is a screenshot shot witht he DEM based contour lines depicted in blue and the TIN based contour lines in grey.
Overall I found this week's module to be very helpful in my understanding of elevation models. We had touched on some of these types of elevation models and tools in previous coursework assignments but being able to apply them to real life analysis has been very beneficial.
The third module in Special Topics in GIS continued the focus on spatial data quality. This week’s task was an exercise in assessing the quality of road networks. We were asked to apply similar methodologies to those introduced in the assigned readings, such as Haklay (2010), How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. The assignment consisted of conducting an accuracy assessment for completeness using two road centerline datasets: one compiled by the Jackson County GIS department and the other from TIGER (2000).
The analysis began by calculating the total length of each dataset for the entire county. To ensure valid length comparisons, both road layers were first projected into the same coordinate system with meter units.
Next, using a provided grid that divided the county into 5 × 5 meter cells, the roads were intersected with the grid using the Pairwise Intersect tool. This step ensured that road segments were split at grid boundaries and attributed to the correct cells. The total road length for each cell was then calculated for both datasets using the Summary Statistics tool.
The results were then joined back to the grid layer so that each cell contained length values from both datasets. A percent difference field was added, and a formula was applied to compute relative completeness. This allowed me to identify and count how many cells favored the Jackson County centerlines versus the TIGER centerlines.
Finally, a choropleth map was created to display spatial patterns of completeness. Symbology highlighted cells where one dataset contained more road length than the other, as well as a neutral class for near-equal differences.
The visualization illustrates the percent difference in road length between the TIGER roads dataset and the county street centerlines. The calculation was based on the formula:
Cells where TIGER roads contained more total length than the county centerlines are symbolized in shades of red and orange, while cells where county centerlines were longer are shown in shades of green. This highlights areas where one dataset is more complete than the other.
The second module in Special Topics in GIS focused on data
quality standards with an exercise on determining the horizontal positional
accuracy of two road networks in the city of Albuquerque, New Mexico. Our
findings were to be reported in accordance with the National Standard for
Spatial Data Accuracy (NSSDA). We were provided two polyline shapefiles that
represented road centerlines from the city of Albuquerque and StreetMap USA as well as a mosaic of orthophotos of the study area.
The NSSDA standard requires at least 20 test points within our study area, with each point separated by more than one-tenth of the area's horizontal distance. The NSSDA
value is an accuracy measurement of our Root Mean Square Error at the 95%
confidence interval. To
help achieve the requirements I used the split tool to divide the study area into four
quadrants and then bookmarked each quadrant which reduced the need to zoom in and out to check the spacing of my points.
Next step was to begin finding "good"
intersections that contained data from both the Albuquerque and Street Maps
data sets. After identifying my
"good" intersections it was then time
to determine what I thought were the "true" reference points at the
intersections using the orthophotos. Below is a screenshot showing my referenceor "true" locations of the intersections according to the orthophotos.
Next step was to use the Add XY Tool to determine X and Y coordinates for each of the points for all
three datasets. I then exported the three attribute tables to excel files using the Table To Excel tool. Following the NSSDA horizontal accuracy statistic worksheet, the independent (true) points from the orthophotos X and Y coordinates were compared to the test points datasets. Calculations were then completed to determine the accuracy statistics for the two test datasets. Below are the results from my worksheet for the StreetMap USA NSSDA
value calculations:
The final column of the table shows the calculated squared error distance. The values are summed and then averaged. The NSSDA horizontal accuracy is calculated by multiplying the Root Mean Square Error (RMSE) by 1.7308. Below are my final accuracy statements for each of the two datasets.
Tested 13.01 ft (3.96 m) horizontal accuracy at 95% confidence level for the Albuquerque Streets data set.
Using the National Standard for Spatial Data Accuracy, the Albuquerque Streets data set tested to 13.01 (3.96 m) feet horizontal accuracy at 95% confidence level.
Tested 312.95 ft (95.38 m) horizontal accuracy at 95% confidence level for the Street Map USA data set.
Using the National Standard for Spatial Data Accuracy, the Street Map USA data set tested to 312.95 ft (95.38) feet horizontal accuracy at 95% confidence level.
The first module for Special Topics in GIS covered aspects of spatial data quality with focus on defining and understanding the difference between precision and accuracy. According to the International Organization for Standardization's (ISO) document 3534-1, accuracy can be defined as the "closeness of agreement between a test result and the accepted reference value". This document also defines precision as the "closeness of agreement between independent test results obtained under stipulated conditions" (ISO, 2007).
In Part A of the lab assignment, the precision and accuracy metrics of provided data were determined. When determining precision, a distance (in meters) that accounts for 68% of the repeated observations was calculated. When determining accuracy, the average waypoint was measured from an accepted reference point. Below is my map product showing projected waypoints, the average location, and circular buffers corresponding to 50%, 68%, and 95% precision estimates. A "true" reference point was later added to determine a horizontal distance to the established average waypoint location.
Horizontal accuracy refers to how close a measured GPS position (or the mean of many positions) is to the true location on the ground. It is typically reported as the distance between the GPS-derived position and a known reference point.
Horizontal precision, on the other hand, describes how tightly repeated GPS measurements cluster together, regardless of whether they are centered on the true location. Precision is often expressed as the radius within which a certain percentage of positions (e.g., 68% or 95%) fall.
My horizontal
precision (68%) was 4.5 m and my horizontal accuracy of 3.25 m produced a
difference of 1.25 m. I would say that this would not be a significant difference
because it sits within the 68% precision radius. My results for vertical accuracywere as
follows with my mean waypoint elevation coming in at 28.54 and the mean elevation for the "true" reference point being 22.58. This is roughly
a 5.96 m difference which I would think is significant at least in some cases.
In Part B of the lab assignment, the RMSE metric was calculated, along with a cumulative distribution function (CDF). The CDF describes the probability of a random variable taking on a given variable or less, showing a more complete error distribution instead of selected metrics. For this portion we were provided another dataset where we used Excel for the analysis. Here we calculated minimum, maximum, mean, median, root square mean, and the 68th, 90th, and 95th percentiles. The final portion of the lab consisted of displaying the dataset using a cumulative distribution function (CDF) graph which is displayed below.
Overall, I really learned a lot in this lab and had the opportunity to brush up on my Excel skills which I have not utilized for a while. I am looking forward to building upon what I learned in this module.
In Module 6, we learned about Suitability and Least Cost Path Analysis. We were introduced to performing suitability analysis using both vector and raster analysis tools. We prepared our data for suitability analysis using different approaches, such as Boolean and scoring, and adjusted specific parameters using scoring and weighting. Additionally, we performed least-cost path and corridor analysis using cost surfaces as well.
In Scenario 2, our task was to perform a suitability analysis for a land developer. We analyzed several variables including proximity to roads, elevation slope, proximity to rivers, and land cover type. These variables were reclassified and ranked based on the value of each cell in the raster. The raster layers were then combined using the overlay tool. Finally, we were required to create a map layout comparing the results from the two alternatives. Below is my final product.
In this module, we delved deeper into the impact of Hurricane Sandy, focusing on damage assessment. To complete this task we learned how to create raster mosaics for pre and post hurricane, created attribute domains to categorize damage, and utilized our skills to decide how to summarize the data from the damage assessment.
To begin we used meteorological data to create a storm track map showing Sandy's progression from below the Caribbean islands on October 22nd, 2012 until it made landfall on the northeast coast of the United States as a Category 1 hurricane on October 29th, 2012.
To complete our damage assessment we used our pre and post imagery to assess the damage to structures in our study area. Each property in our study area had to be identified and categorized according to the domains that we created. Each domain provided a choice for what level of damage each property appeared to have based on our remote imagery. After assessing the damage and symbolizing the data we were tasked with creating a polyline feature class representing the pre-storm coastline. I then created a multi-ring buffer establishing a 100m, 200m, and 300m buffer. This was used to summarize the data and help discern and identify patterns. Below are my results:
This week's module for Applications in GIS we were tasked with conducting three different coastal flood analyses. With the use of digital elevation models we delineated coastal flood zones. These analyses utilized both raster and vector data to ultimately achieve our desired result. This lab proved to very challenging for me as I had some issues early on that prohibited me from completing all the steps.
The initial analysis consisted of creating a raster of coastal New Jersey post hurricane Sandy showing erosion and buildup. Below is my map for this analysis where the darkest red shading represents the greatest erosion and the darkest blue represents the areas of the most buildup.
For the final portion of the lab we were tasked with analyzing storm surge in Collier County, Florida. Two DEMs were initially used one by the USGS and another LiDAR derived used to create 1 meter storm surge models. These were to be used to analyze and determine the number of buildings that would be affected by a 1 meter storm surge. Unfortunately, I was unable to complete this objective in its entirety. I was unable to understand the Region Group tool so I skipped it so my data isn't as accurate as it should be. Below is a map showing my two storm surge models.
Overall, this was a challenging model for me because I had some issues understanding some instruction starting in analysis 2. After getting behind I struggled to regroup after becoming frustrated. That said, I was able to get back on track and make some progress with analysis 3 even though I was unable to complete it in its entirety.
In this week's module for Applications in GIS, we were introduced to numerous 3D visualization techniques using ArcGIS Pro. Using ArcGIS Pro you can visualize your data in 3D by using a 3D scene. Using 3D scenes can be a powerful tool to enhance the visualization of your data by adding realistic environmental effects making it more attractive to your audience.
Our assignment for this week was to complete a series of ESRI training courses that introduced us to numerous 3D data visualization techniques. The courses we were assigned included the following courses:
1. Introduction to 3D Visualization
2. Performing Line of Site Analysis
3. Performing Viewshed Analysis in ArcGIS Pro
4. Sharing 3D Content Using Scene Layer Packages
In the first course, Introduction to 3D Visualization we completed 4 different exercises that taught us about visualizing 3D data in three different views: a map view, a local scene view, or a global scene view. We learned why and when the different views should be utilized, to most effectively display data. We also learned how to determine elevation types, cartographic offset, vertical exaggeration, and extrusion types and methods. The results of a couple of the exercises can be viewed below:
In the above image, we applied realistic enhancements to a downtown San Diego, California local and global scene such as illumination effects, enhanced tree symbology, and detailed buildings.
In the above image, we extruded 2D features by attributes creating a 3D scene. The building parcels shown are not depicted by actual height but rather by property value with yellow representing residential properties, pink commercial properties, and the two gray parcels public housing.
In the second course, Performing Line of Sight Analysis we performed a line of site analysis using a DEM of Philadelphia, Pennsylvania, a parade line, and locations for observers to determine which locations along the route could be observed by security personnel from two observation points. By using ArcGIS 3D analyst tools we were able to determine the optimal line of sight comparing lines greater than 1,100ft and 600ft. Below is a screenshot taken during the process of the analysis.
In the third course, Performing Viewshed Analysis in ArcGIS Pro we created a local scene depicting the hypothetical placement of a new lighting system for a campground in Eastern New York. Using the Viewshed tool, we modeled the range visibility at 3 meters and 10 meters above the surface. Below are the results of the analysis.
The above image shows the results of the 3 meter analysis.
The above image shows the results of the 10 meter analysis.
In the final course, Sharing 3D Content Using Scene Layer Packages we learned how to author a 3D scene using a set of data from the city of Portland, Oregon. Using methods similar to previous courses we converted 2D buildings and trees to 3D features. Our extruded building polygons were converted to multipatch features and the trees were converted to 3D symbology. Finally, we created a scene layer package from our multipatch feature data (the buildings) and published it as a hosted scene layer using ArcGIS Online. Below are two screenshots from the exercises.
The image above depicts the city of Portland global scene with multipatch building features and symbolized trees.
The image above depicts a scene from the published hosted scene layer from ArcGIS Online.
Overall, I really enjoyed these courses and learned a lot of new tools and methods that have provided me with a base knowledge of the 3D visualization capabilities of ArcGIS Pro.
In the second module for Applications in GIS we were introduced to a more in depth look into LiDAR. LiDAR stands for "Light Detection and Ranging", and while it has been a remote sensing method traditionally used in forestry science, I was first introduced to it while working the field of archaeology. This week's module focused on how LiDAR is applied in forest management and earth science in general.
In this week's lab we worked with LiDAR data from the Shenandoah National Park, Virginia, to examine tree height and tree canopy density. The LiDAR data we used was obtained from the USGS in the form of an .las file, and then converted to a digital elevation model (DEM). To do this, we used the LAS Dataset to Raster tool in ArcGIS Pro. Next we created a forest height from the DEM and created a map with an accompanying chart showing the tree height. To finish we created a canopy density layer using the LAS to MultiPoint tool, Point to Raster tool, IS NULL tool, Con tool, Plus tool, and finally the Divide tool.
Below are the maps I created as a result of the data that was generated throughout the process of the lab.
In the first module for Applications in GIS we were introduced to some mapping techniques employed in crime analysis. These techniques are used to determine hotspots in criminal activity that can be utilized to determine crime rates, identify crime patterns, and compare the reliability of hotspot mapping to predict future crime.
For this module, we were required to create maps using crime data, specifically focusing on homicides in Chicago in 2017 and 2018. Below are three distinct maps illustrating the distribution of homicides in 2017. These maps included: a grid overlay thematic map highlighting areas with the highest 20% of homicides in Chicago, a kernel density map showcasing areas with homicides occurring at three times the mean average, and a local Moran's I map delineating areas aggregated by meaningful boundaries.
Part of the assignment included providing a hypothetical policy recommendation to a police chief on which hotspot map would be the best for predicting future homicides. I would suggest using the Kernel Density map because it would allow for a concentration of resources in areas with the highest density of homicides and has an almost optimal prediction rate. With Kernel Density, one could pinpoint areas with triple the mean homicide rate, whereas with the grid overlay, one could only identify areas in the top 20%. Although Local Moran I's clusters high values within aggregated boundaries, the area might appear broad on the map and could limit police time and resource availability.
Overall, this was a great learning experience that exemplified how GIS principles and technology can be applied to help solve real-world problems. I'm looking forward to learning more GIS applications in the coming modules.
My name is Rick Schmidt and I was born and raised in northwestern Pennsylvania in a small town called Franklin. In 2011, my wife and I relocated to Port Orange, Florida. We have a 6-year-old son (soon to be 7!) that keeps us busy. He is currently interested in learning the principles of engineering and anything outdoors also recently taking an interest in fishing. I joined the UWF graduate certificate program after comparing a handful of programs concluding that UWF was the most robust, offering more than others that I looked into. I earned a bachelor’s degree in anthropology in 2006 from Clarion University of Pennsylvania where I was introduced to GIS through taking an intro class. I was able to apply the skills I learned in that class using ArcMap while employed with the US Forest Service in the Allegheny National Forest where I worked as an archaeologist for three years.
This is my 5th course in my journey to obtain my GIS graduate certificate here at UWF having just finished the GIS Programming course. Much has changed in the last few months as I ended up accepting a position as an Environmental Scientist at a small environmental firm. I have learned so much through this program and look forward to finishing strong and applying my GIS knowledge in my new position.
Outside of working and school, I spend time studying the bible, playing basketball and occasionally coaching my son’s sports teams with my wife. In my free time I also enjoy photography as a hobby and volunteer at a local archaeological site in Ormond Beach, Fl. I also serve on the Volusia County Historic Preservation Board.
I look forward to working with everyone this semester!
Module 6 for GIS Programming tasked us with writing a script that gathered geographic data from a river shapefile and writing the data to a TXT file. This required us to build upon what we have learned utilizing a search cursor to iterate over the river shapefile geometries, using nested loops, with the end goal being a populated TXT file containing the name, x,y, coordinates, OID, and the number of vertices for each row of the features in the rivers shapefile.
The increased complexity of this assignment was a little intimidating and I found that sticking to the template and flowchart was crucial for success. I got off track a few times and ended up making some mistakes that required me to back track. I thought that I was finished on a few occasions but found I had overlooked some crucial details. One of these was that my nested for loops were incorrect and another was that my TXT file was not being saved in my results folder.
Overall, this was a challenging module and a great finish to the course. I have certainly learned a lot in a short time period and look forward to continuing to build upon what I have learned. Below is my flowchart and a snippet from the resulting TXT file.
The topic for Module 5 for GIS Programming was exploring and manipulating data. In this module we covered a lot of ground. Some of the major outcomes were learning how to work with different types of cursors to manipulate data, working with list and describe functions, as well as creating and population dictionaries. Another useful topic we covered was how to use search cursors with SQL expressions.
Our task for our lab assignment was to create a script that created and populated a new file geodatabase followed by creating and populating a dictionary containing the names and population of the cities in new Mexico that are county seats.
The initial steps of the task went pretty smooth but I did get hung up when I forgot to update my environment after creating the new file geodatabase. Another thing that gave me some temporary issues was creating my SQL expression in the search cursor. I was able to get through with some help from the exercise document and ESRI documentation and much trial and error. I also I had some issues placing the \n character to create proper space between messages but after more trial and error I was able to things where I wanted them. I finished adding my print statements and GetMessages which gave me a few minor issues but again with trial and error and some tips from the discussion board I was able to get them where I wanted for the most part.
Below are the screenshots of my script output and a flowchart I created to guide myself through the creation of my script.
Overall, this was a really challenging task and I spent a ton of time trying to complete it. There was a lot of trial and error but through the process I can say that I am starting see Python on a more holistic level.
