GIS Portfolio

The final assignment in the GIS Certificate Program was to create a GIS Portfolio. It went as I expected. It is hard to write about yourself and even more tedious trying to decide the layout of information you would like to present and what order to present it in. I had my supervisor look it over, and she felt that it represented me, which was my goal. I wanted to showcase some of my favorite maps and techniques used, while also having my 'About Me' page reflect who I am as a person as someone looking in and not really knowing me from the start. I hope the goal was a success. Please enjoy my portfolio linked below:

Jenna's GIS Portfolio

GIS Day 2022!

My GIS Day was a bit lack of luster, but I tried to see what everyone else was doing. I asked my coworkers if they have ever participated and they said one year they went to a Esri conference in San Diego, but this year the workload overtook the opportunity to do anything but talk about it.

So, on my own, in my little peach colored office, I wanted to see what Esri would be up to, so I found their interactive map where people were posting the location and info for their GIS Day celebrations. I clicked all over the world and quickly became excited to see it in even the most unexpected places. Some people had links and noting their employer names, so I was able to look up various companies and see all the different ways people are using GIS in their lives. Missionary groups are using it, Egyptologists are using it, even locally I had 4. Some were even for children and that is awesome! My solo GIS Day evolved into dinner conversations with my mom telling her all the people and places I could remember that I saw on the map that interested me. Maybe next year, with hopefully some coworkers that are just as excited about GIS as I am I won't have to go solo and can do anything with them. My town is still recovering from the hurricane, so we have been busy, and we have had to produce data on the fly for fire zones, damage assessments, and there are always addressing needs in my ever-growing county. Here are all the GIS Day links that were posted this year.

Topic 3 Module 1: Scale Effect and Spatial Data Aggregation

This week we were involved in scale effects on raster and vector data, gerrymandering. The relationship between scale and geometric properties is that the large-scale maps show fewer properties than the small-scale maps. This is due to the generalization where information is “lost” because fewer vertices are used to represent features. Along with exclusion, where scale matters and can cause a decrease in the level of hydrographic feature detail. After reading the Goodchild, M.F. 2011 article and seeing other Esri documentation on the web I understand that my findings in this lab are as expected and that I have lost detail as the scale changes. The level of detail of features represented by a raster or vector data is often dependent on the cell (pixel) size, or spatial resolution, of the raster/vector. The cell must be small enough to capture the required detail but large enough so computer storage and analysis can be performed efficiently. However, more is not often better especially when considering compuation times and data storage limits. As for gerrymandering, it has a very negative history and is defined by manipulating the boundaries of (an electoral constituency) so as to favor one party or class. Basically it is the redrawing of polygons and can be measured by compactness and community. Below is a screenshot of a district with failing to have district 'compactness'.

Internship Blog Post #3

I chose to update you on my internship. It is going very well, and I am happy to report that I am learning a lot more than I expected. There is so much more going on depending where your internship is, and mine has opened insights into CAD, 911 Operations, Emergency Management, Survey123, and even ArcMap. While ArcMap may be going away one day, I am learning how to use it because that is what my office is using. It is different than Pro, but the same functionality exists. It's just not always in the same spot that we were taught in Pro. All good. I keep the ArcMap help website pinned on my computer just in case. My supervisor sits next to me and we are able to work on things together or separate. She has a few ongoing projects that she hopes we can get more into while I am there. One involves creating a Survey123 to collect data on mile markers down a desolate road, and another project is awaiting data from a local state park, so that we can enter it into 911 CAD, so if someone needs help out there they can find them fast. I have made a few maps already, and that has been fun to interact with the needs of the map and the data they have. I love going to the office there, and everyone is a delight to work with. Hoping good things continue and I learn as much as I can in the little time left.

Module 2.2: Surface Interpolation

This week we covered topics in surface interpolation techniques in GIS, including Theissen, Inverse Distance Weighted (IDW), and Spline. We critically interpreted the results from the techniques to compare and contrast them. The lab consisted of exploring water quality data for Tampa Bay, FL in the Biochemical Oxygen Demand (BOD) in milligrams per liter. Data consisted of 41 sample points in assuming random locations. Determining the best way to accurately represent the data was a bit up to us. Theissen technique uses polygons to define an area of influence around its sample point, so that any location inside the polygon is closer to that point than any of the other sample points. IDW assumes that things that are close to one another are more alike than those that are farther apart. Spline estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. After looking at the statistics of the data for each technique and the overall output for any anomalies I chose IDW interpolation as my image to display below. This is because it is an exact interpolator, good use for water data because of how it works, and there were no adjustments needed to make the data work for the technique. It was the sufficient and accurate way to go for this particular data set in my opinion to show the water quality conditions in Tampa Bay.

Module 2.1 Surfaces - TINs and DEMs

This week we laid it all out, literally. Surfaces can be an interesting topic when discussing elevation models and 3D visualizations. We read about TIN and DEM elevation models, compared them, examined their properties, and practiced creating and modifying them. In my exploration of of TINs and DEMs I learned about suitability modeling, how slope, aspect and edges effect the appearance of them, and especially how symbology plays a major role in how the data is shown for a final layout. While these topics and tools such as Raster to TIN, Reclassify, Slope, Aspect, Create TIN, Spline, and Contours are not entirely new, it is necessary to practice more with them for a greater understanding. The screen capture below is a colorful example of exxagerated terrain of Death Valley near the Furnace Creek area. By adding the TIFF image as an elevation surface in a New Scene and increasing the vertical exaggeration to 2.0 it becomes this.

Internship Blog Post #2

This week we were tasked to conduct our own GIS job search. By looking at openings, requirements and things I may need to learn in the future will only give me more tools for my toolbox. I started with Google and checked out Indeed and ZipRecruiter finds, but nothing seemed to fit me right away. I wanted to see if I could find a job posting a little more obscure that combined my archaeology degree, Navy knowledge from being active duty and of course GIS. In that effort I was able to find a company that was seeking Nautical Archaeologists and GIS Analysts, and they happen to do government contracts, so I would even understand some of the lingo there. Dream job material for sure, but location is a little too far from home. Similar companies must exist in Florida, but some key takeaways are to think beyond just searching for GIS Analyst, as there are other names that use ArcGIS Pro. Also timing. We are currently sitting right at the end of a fiscal year and another one beginning, so budgets and upcoming job listings are up in the air. End of year seems to be the word on the street to start seeing more listings. Overall, great assignment and gets our brains thinking ahead for when we will be looking for employment in GIS.

Module 1.3: Data Quality - Assessment

For this lab the goal of the accuracy assessment was to determine the percentage difference between 2 road shapefiles put up against a grid overlay in Jackson County, Oregon. The analysis methodology utilized the readings from Haklay 2010 for the most part, and that entailed using the Clip tool, determining lengths within each grid for each road shapefile and then comparing them to get the difference. Tools also used were Intersect and Summarize Within, along with an Excel spreadsheet to compare the data visually easier. Once I had my two data sets I was able to Intersect them and calculate the percentages in ArcGIS Pro. The layout was created using this combined data set in a graduated color symbology to show where the differences between the -103 and 80 percentage data were the most and least extreme. See layout image below.

Comments: I wanted to showcase the grid symbology, but not leave out the roads. Finding a color combination that did not crowd or take over was difficult, but I am happy with the results.

Module 1.2: Data Quality Standards

In continuation of our module about Data Quality, this week we learned about how to determine the quality of road networks, determining postitional accuracy of two road networks by comparison and the methodology of procedures provided by the National Standard for Spatial Data Accuracy (NSSDA). We were given city data and street data shapefiles of Albuquerque, NM along with orthophotos to help us create reference points. From there we were able to create acccuracy statistics worksheets to create a formal accuracy statement per the NSSDA guidelines. Below is an image of my sampling locations.

Summary of steps: Once the reference points were created, I was able to calculate geometry in the attribute tables for city, streets and reference points to get the corresponding X and Y coordinates. From there I exported the data to Excel and created columns for error_x, error_y, error_xy_sqrd, error_xy, RMSE, Mean, Median, 95th Percentile, Minimum, Maximum, 68th Percentile,and 90th Percentile. The NSSDA statistic is determined by multiplying the RMSE (root mean square root) to a 95% confidence level. 1.7308 for horizontal accuracy and 1.9600 for vertical accuracy. For this project horizontal accuracy was being determined. The following statement is the accuracy statement once I multiplied my street RMSE by 1.7308 and my city RMSE by 1.7308.

Street Map Data: Tested __141.6709___ feet horizontal accuracy at 95% confidence level.

City Data: Tested __17.9350___ feet horizontal accuracy at 95% confidence level.

Internship Blog Post #1

The journey from a GIS Padawan to now has flown by. The journey to find an internship felt like an eternity of highs and lows. I only just signed paperwork to become an intern for my local county's GIS department. I still have orientation to attend (unknown date at this time), but hopefully I will have begun by the end of month. During the interview it was noted the Emergency Management office was hoping they had an intern to assist in the revamp of the 9-1-1 service in the county to an updated version. Then also helping in the GIS department with their daily tasks. It is a small room of 4 people, and they have even smaller offices with stacks of documents as high as their monitors on their desks. There is a big conference table in the middle of the room, which I will be seated at with a laptop and 2 large map printers shoved against the wall. I plan to earn credit by showing up, and working hard for them. I love to learn, so anything they can teach me I will soak in and I hope I can teach them things I know too. They are working with an older ArcGIS Desktop version, but none of them have any direct background in GIS college related courses such as the ones we have been taking the past year. I am thrilled at the opportunity and will not let it go to waste. Leave everything you touch better than you found it has been ingrained in me since I was a Girl Scout in primary school, so this chance should continue the tradition.

In the meantime this week, I joined a local GIS user group called Northeast Florida GIS User Group found at this link. They have a straight forward mission statement of to "provide an educational environment for Geospatial Information Systems (GIS) professionals and students, facilitate the advancement of geospatial initiatives, and the exchange of ideas". They accomplish this through a email mailing list and a LinkedIn account. I hope they provide some great information about this area I call home in the future.

M1: Calculating Metrics for Spatial Data Quality

New class, new tasks. This is Module 1 of Special Topics in GIS. This week we have learned about the difference between accuracy and precision. Accuracy is the absence of error and is determined by comparing a coded value in the database of interest to some independent reference value. For numerical values we can use a metric like the Root Mean Square Error to describe accuracy. Precision is, in this context, the variance of measurement. In other words, how close together are multiple observations of the same coded value? This does not use a reference value, but instead uses a metric like the standard deviation of a sample.

Below you will see 2 things: the first is a map layout from Part A of the lab where we were tasked to show accuracy and precision from projected waypoints with circular buffers of precision estimates. The second thing is the numerical results for horizontal accuracy and precision.

Numerical results: Horizontal accuracy of 4.279 and horizontal precision of 4.293

Module 6: Scenario 4 - Suitability and Least-Cost Analysis

Last, but not least, the final post for Applications in GIS. This scenario (2nd deliverable for Mod 6) was a on your own corridor analysis. As a park ranger in the Coronado National Forest we were asked to carry out a corridor analysis using the two National Forest polygons to create a meaningful corridor between them taking into consideration roads, landscape and elevation. Tools to accomplish this were Reclassify, Weighted Overlay, Cost Distance, and Corridor Tool in that order respecfully. Once the suitability output was achieved, and the corridor analysis completed a meaningful symbology was applied based on the minimum values. See layout below.

Module 6: Analysis C

This module is a multi-part analysis through various scenarios. This is blog post #1. For this analysis we were tasked to estimate how much land would be suitable to build on. The analysis itself was based on land cover, soils, slopes, streams and roads. Every item was reclassified into suitability classes using tools: Reclassify, Euclidean Calculator, Polygons to Raster, Slope, and finally Weighted Overlay to get the final result image below. The Weighted Overlay tool was used twice for 2 different scenario with different weights adjusted or being equal.

Module 5: Damage Assessment

This week we explored the damage assessment aspect of Hurricane Sandy's landfall near Atlantic City, New Jersey. We tracked Sandy, created and designed attribute geodatabases for editing purposes. As well as exploring imagery effects tools to visualize pre/post Sandy imagery. We used parcel data, the imagery, and the crewly created damage assessment files to catelog a street on the New Jersey coastline. General steps to accomplish this included, creating line features from points for the track, adjusting the symbology to look like a hurricane graphic, creating a mosaic dataset for pre and post imagery rasters to be. Then created more data using new feature classes and domains and finally filling in those attribute tables with the points/data for our structures in the study area. Below there is a screenshot of the structural damage points created and a table of the distance to coastline to examine patterns.

Module 4: Coastal Flooding

Well we can't win them all! That is the major lesson from this lab. Attempts to follow the directions were fraught with misaligned data and things that just did not make sense. Luckily I was not alone in my efforts coming to bitter ends. The assignment was to conduct a analysis on storm surge in Florida, by comparing a traditional USGS DEM and a DEM derived from LiDAR. We were to compare the differences in results between the two elevation models assuming a storm surge of 1 meter. The layout below is my best attempt to show this.

Module 3: Visibility Analysis

This week we were tasked to complete 4 Esri courses through ArcGIS Online. By passing the quizzes at the end of the courses the certificate was awarded. These exercises were intended to give us more knowledge on visibility analysis. The first course was Introduction to 3D Visualization and I learned navigate 3D scenes, manipulate height variables in a data visualization, how to convert a 2D map in to a 3D local scene, applying 3D symbology and applying realistic enhancements to local and global scenes. The second course was Performing Line of Sight Analysis where I used tools Construct Sight Lines, Line of Sight, and Add Z Information to see parade routes through a city to determine the lines of sight for observers and visibility between observer points and the parade route. The third course was Performing Viewshed Analysis, where I modeled new lighting in a campground. Tools to accomplish this were Viewshed, and we modified field values, and modeled the light coverage until the light coverage was improved. The fourth and last course was Sharing 3D Content from data in the city of Portland, Oregon. To accomplish this we displayed the 2D data as 3D layers, and converted the 2D data to 3D data using the Layer 3D to Feature Class and Feature to 3D By Attribute tools.

As you can see numerous tools were implemented and I learned a great deal from the Esri courses. They have so much information packed into not only the online portion, but the directions for the exercises are full of tips and tricks that I will use in the future. For instance, many items were manipulated in just the properties of the item. Tools were used here and there to accomplish tasks, however it was interesting to see more ways data can be changed using only the properties dialog box. Another trick I learned was in the symbology of 3D items and how to configure the properties to better reflect the environment, including sunlight shadows and the ways water can move.

Here are my resulting certificates to make this post more appealing to the eye.

Module 2: Forestry and LiDAR

This week we were given a .las LiDAR file with data from the Big Meadows area of the Shenandoah National Park in Virginia. We learned how to decompress .las files, create DEMs from LiDAR, create DSMs from Lidar, and calculating forest height from LiDAR. Tools used were LAS Dataset to Raster, Minus, LAS to Multipoint, Point to Raster, IS NULL, Con, Plus, Float, and Divide. From there, the data gained was used to display 3 maps as seen below.

The first map shows tree heights with a distribution bar graph below it.

The second map is of canopy density.

The third map is a LiDAR elevation map with a LiDAR_Derived DEM below it.

Comments: This week ran smooth with almost no issues that I could tell and becoming more aquainted with everything ArcGIS can do is a plus that will only help me in the future.

Module 1: Crime Analysis

For this lab the task was to show hotspot analysis using 3 methods (Grid-Based Thematic Mapping, Kernel Density, and Local Moran's I) The below screenshots show the hotspots of homicides from 2017 in Chicago, IL.

First up is Grid-Based: Based on a grid of 1/2 mile cells clipped to the Chicago city boundary, the end result is the grids with the top 20% overall highest count of homicides. Utilizing Spatial Join, Select by Attributes, and Dissolve tools.

Second is Kernel Density: This map shows the density of homicides that are three times the mean of the data. Utilizing Kernel Density, Reclassify, and Select by Attributes tools.

Last is Local Moran's I: This map uses crime counts and number of homicides per 1,000 housing units for each census block group. The result is spatial clusters of the high-high clusters that are in close proximity to other areas with a high homicide rate. Utilizing Spatial Join, Cluster and Outlier Analysis, and SQL Query tools.

Comments: Other than input and output data any remaining parameters in the tools used were left as default per lab instructions.

About Me

My name is Jenna Clevinger, and I joined the UWF GIS Certificate Program in hopes of combining my BA in Anthropology with GIS for an exciting new career. I gained an interest for maps while I was enlisted in the U.S. Navy for 8 years as a Quartermaster. During my time I learned all about navigation and was able to have some interesting land and underwater archaeology volunteer opportunities in my travels. I hope to gain enough knowledge from the program to get my foot in the door of a surveying or related GIS career. I have drive, passion and willingness to learn. Having a GIS related career my goal, but you never know where life will take you, so I am open to exploration along the way. My Story Map is here.

Module 6: Working with Rasters

I made it! Last assignment in computer programming is a wrap. This week we were tasked with to create a raster output that identifies areas with a particular set of parameters: slope, aspect, and land cover type. The below screenshot is from ArcGIS Pro showing my raster from the Python script I wrote.

Comments: This lab was a bit more straight forward for me. Maybe I picked up a few things along the way in this class, or for once the lab instructions were more similar to the exercise this go around. Either way, script works, raster looks nice, and now I can tackle the next items on my GIS agenda.