Michell's GIS Journey

For this assignment, I created a paper Portfolio designed to showcase my skills, coursework, and experience in Geographic Information Systems (GIS). The purpose of this portfolio is to present my work in a professional format that could be used when applying for internships, jobs, or future academic opportunities in the GIS and environmental fields. I built my portfolio using PowerPoint and organized it in a way that is clear, structured, and easy to navigate. The portfolio includes a cover page, table of contents, an “About Me” section, my resume, and evidence of competence through GIS-related projects. I also included a contact information page and saved the final version as a PDF for professional use. One of the most important parts of the portfolio was the evidence of competence section. In this section, I included examples of coursework where I used ArcGIS Pro to analyze and interpret spatial data. These projects helped me develop skills in map creation, data classification, an...

  For my GIS Day event, I decided to keep things simple and personal by hosting a small, informal discussion at home with my family. Since they don’t have any background in GIS, but have asked me before about what I’m studying and what I do in my internship, I thought this would be the perfect opportunity to explain it in a way they could actually understand. We sat around the table, and I started by explaining what GIS is in simple terms; not just the definition, but how it’s used in real life. I showed them examples they could relate to. I also talked about how GIS is used during emergencies like hurricanes to plan evacuations and send help where it’s needed most. That part especially caught their attention since we live in Florida and experience hurricane season. To make it more interactive, I showed them some maps and explained how layers work, like how you can add or remove different types of information. I also connected it to my internship and explained the kind of projects ...

  As I continue working toward my GIS certificate at the University of West Florida and my Associate degree at Valencia College, I have been reflecting on how the GIS industry connects directly to infrastructure and environmental systems. After I started my internship, I realized how strongly the GIS industry connects to infrastructure, public utilities, and environmental systems. What once felt mostly academic in the classroom became much more meaningful when I saw how GIS is used to manage water, wastewater, and critical infrastructure in real-world settings.  Through my internship with the Orange County Utilities Department , I have been able to see this connection firsthand. In this role, I assist with spatial data editing and mapping projects related to water and wastewater systems. This experience has strengthened my understanding of how accurate spatial data directly impacts public services and environmental management. One of the biggest highlights of my internship h...

  For this assignment, I conducted a job search focused on GIS-related positions to better understand how the skills I am currently developing align with real-world careers. While exploring postings, I noticed that GIS roles can vary widely depending on the organization and industry, even when they share similar titles. One key takeaway from this search is that GIS is rarely a standalone skill . Many positions integrate GIS with planning, data management, sustainability, or coordination work. Several roles emphasized collaboration across departments, data quality assurance, and decision-making support. Another important insight was how education and experience expectations are structured. Many postings prefer a bachelor’s degree and professional experience, which helped me better understand where I currently stand and what steps I still need to take. While I may not meet every requirement yet, I saw that coursework, internships, and hands-on projects play a valuable role in buildi...

  I began my internship with the Orange County Utilities Department , where I support GIS staff on various projects related to mapping requests, data review, and field operations. This internship gives me exposure to how GIS supports real-world utilities, infrastructure, and planning decisions that directly affect residents and public services in the county.  To earn academic credit for this internship, I will be completing the required course components, including the internship work plan, weekly work activities, professional development assignments, participation check-ins, and the final reflection and documentation needed at the end of the semester. As part of the professional engagement portion of this course, I also selected a GIS-related professional user group to join. I chose AWRA Florida (American Water Resources Association) , which focuses on water resources science, management, and policy throughout the state. AWRA Florida offers technical meetings, networking oppo...

  In this lab, I created a supervised land-use classification of Germantown, Maryland, to visualize current land-cover patterns and support sustainable planning. Maryland’s population has grown 30% over the last 30 years, while land consumption has doubled, making it critical to understand how urban and green spaces have changed. I used the dataset and selected a band combination that minimized spectral confusion between classes. Using histograms and mean plots, I created and refined spectral signatures for urban areas, roads, water, grass, deciduous and mixed forests, fallow fields, and agriculture. Adjusting parameters like Spectral Euclidean Distance and Neighborhood helped ensure each signature accurately represented its feature. The final map, recoded into eight classes, includes the classified image, a distance file inset, a legend with colors, class names, and areas, plus essential map elements. This project provided hands-on experience in creating accurate, meaningful land-...

  This map highlights a mountain peak located in the northwest section of the study area. The feature was identified by a small spike in layers 1–4 around pixel value 200 and a large spike in layers 5–6 between pixel values 9–11. To make the summit visually distinct, a false-color band combination emphasizing  Layers 5 and 6 was applied, which clearly contrasts the rocky peak from surrounding vegetated slopes and shadowed areas. Using the histogram and the inquire tool helped confirm the spectral characteristics and locate the feature accurately. This visualization provides a clear representation of terrain variability and the spectral differences between rocky and vegetated areas in the study site.

This week, I worked with a classified land cover image in ERDAS IMAGINE to explore how land cover types can be analyzed and mapped. I began by opening the file and examining its attribute table. To better understand the extent of each land cover type, I added a new Area field to the table, which allowed me to calculate the area each classification occupies in the image. After saving the updated layer, I used the Inquire Box tool to select a smaller region of interest within the image. I then exported this selected area as a new image. Once the new image was created, I added a new Area field again to ensure the area values reflected only the smaller region. I then moved to ArcGIS Pro to begin the map-making process. I added tm_subset.img to a new map, adjusted the symbology, and edited the class labels so that each land cover class displayed both its name and the amount of area it occupies. After setting up a layout with a title, legend, north arrow, scale bar, and my name, I exported t...

  This week’s assignment focused on learning how to classify land use and land cover (LULC) from high-resolution satellite imagery and then evaluate how accurate those classifications were. We worked with an aerial image of Pascagoula, Mississippi, and created our own data by digitizing different land use types. First, I created a new polygon feature class and digitized different land cover areas by identifying patterns of tone, texture, shape, and association. I assigned each polygon a Level II LULC code and a short description. My final categories included residential (11) , commercial and services (12) , deciduous forest (41) , forested wetlands (61) , non-forested wetlands (62) , lakes (52) , streams and canals (51) , and bays and estuaries (54) . Learning to recognize these features based on visual cues made me more aware of how land use reflects both natural environments and human activity. Next, I conducted a ground truth accuracy assessment . I created 30 sample points arou...

  This week’s assignment focused on learning how to interpret aerial photographs using different visual cues such as tone, texture, shape, size, shadow, pattern, and association . Even though aerial images may seem like just pictures from above, this lab helped me understand how much information can be extracted when we look closely and intentionally. In Exercise 1 , I worked with a grayscale aerial photo to identify differences in tone and texture . I created a feature class for tone and labeled five areas ranging from very light to very dark. I also created another feature class for texture, identifying areas from very fine to very coarse. This process showed me how brightness and surface roughness can indicate different types of land cover. For example, paved areas appeared very light, while water bodies showed up very dark, and forests had more coarse textures. It made me realize that even without color, we can still interpret the landscape fairly well. In Exercise 2 , I pract...

 Scale Effects on Vector Data: In vector data, the scale at which features are represented can dramatically affect spatial analysis results. Larger scales (zoomed in) capture more detail, while smaller scales (zoomed out) generalize features. For example, analyzing counties versus ZIP codes can produce different statistical outcomes because aggregation over larger areas smooths local variability, a phenomenon known as the Modified Area Unit Problem (MAUP). Resolution Effects on Raster Data: Raster data are made of grid cells, and the cell size determines the spatial resolution. High-resolution rasters capture fine details but require more storage and processing power, whereas low-resolution rasters generalize information and may hide local variation. This affects analyses like slope, elevation, or population density surfaces, where resolution can change both visual and statistical results. Gerrymandering: Gerrymandering is the practice of drawing political district boundaries to fa...

Inverse Distance Weighting (IDW) Interpolation This week’s assignment focused on using surface interpolation methods to visualize water quality in Tampa Bay. The goal was to estimate BOD concentrations across areas where sampling points were sparse, using techniques like IDW, spline, and Thiessen. Interpolation methods are useful for creating continuous surfaces from discrete sampling points. IDW assumes that nearby points are more similar and produces smooth, gradual transitions. Spline generates very smooth surfaces that can sometimes exaggerate peaks and valleys. Thiessen divides the area into polygons where each location is assigned the value of the nearest point, creating distinct zones rather than smooth gradients. Comparing these methods shows that each represents the data differently, highlighting the importance of choosing an approach based on the data characteristics and analysis goals.

  This week’s lab introduced different methods for working with elevation data and applying them to real-world suitability analysis. I began by creating several raster surfaces from a DEM, including slope, aspect, and reclassified versions of each. These reclassified rasters were then combined into a weighted overlay to build a final ski run suitability map , using weights of 25% aspect, 40% elevation, and 35% slope. The result was visualized in 3D, applying vertical exaggeration, lighting effects, and clear symbology to highlight the areas most suitable for ski runs. Then, I created and explored TIN models as another way to represent elevation. By adjusting the TIN symbology, I was able to view slope, aspect, and contours, as well as examine the edges of the triangles to better understand how the terrain was being modeled. This showed how TINs preserve the accuracy of the original points while still allowing terrain characteristics to be visualized. Finally, I compared the TIN to...

  The objective of this assignment is to learn how to assess the quality of road networks. Specifically, we evaluate the completeness of road networks by comparing the total length of roads across two different datasets: the county Street-Centerlines and the TIGER road network. By examining differences in road lengths at both the county and grid levels, we can identify spatial gaps and better understand the relative coverage of each dataset. To perform the analysis, all datasets were first projected into a common coordinate system to ensure accurate distance measurements. Both road networks were clipped to the study area and intersected with grid polygons, splitting road segments at grid boundaries and assigning them to their respective grid cells. Lengths were recalculated for each segment, and total road lengths were summarized per grid for both datasets. Percent differences were then calculated using the county Street-Centerlines as the base, where positive values indicate...

  This week’s assignment focused on performing a horizontal positional accuracy assessment of two street datasets: the City Streets layer and StreetMapUSA. The goal was to quantify how accurately each dataset represents the true locations of intersections using orthophotos as reference data. By selecting 20 representative test points, digitizing their true locations, and comparing them to the datasets, we calculated RMSE values and generated formal NSSDA accuracy statements. This exercise reinforced key GIS skills, including feature selection, digitizing reference points, coordinate extraction, and error analysis. To assess this, 20 test points were selected across all quadrants of the study area. Reference points were digitized using orthophotos to represent the ‘true’ location of intersections. Coordinates for the reference points and corresponding points from both datasets were recorded. Differences in X and Y coordinates were calculated, Euclidean errors determined, and RMSE va...

 T his week’s assignment focused on evaluating the precision and accuracy of GPS data . To do this, we worked with a set of waypoints mapped multiple times at the same location, then calculated an average position and compared it to a known reference point. By creating buffers around the average location and analyzing both horizontal and vertical errors, we explored how GPS measurements vary and how close they come to the true position. This exercise helped demonstrate the difference between accuracy (closeness to truth) and precision (closeness of repeated measurements), as well as the limitations of consumer-grade GPS receivers. The map shows the projected GPS waypoints, the calculated average location, and circular buffers representing the 50%, 68%, and 95% precision thresholds. These buffers illustrate how far most points fall from the average location, helping visualize the spread of the data. Results: Horizontal Precision (68%): 6.0 m Horizontal Accuracy: 4.3 m Vert...

 In this analysis, I modeled potential movement corridors for black bears between two protected areas within the Coronado National Forest. Using data layers such as land cover, elevation, and roads, I developed a habitat suitability model that reflects black bears’ preferences and movement constraints. To begin, I reclassified land cover, elevation, and proximity to roads into suitability scores, where lower scores indicate more favorable habitat. These criteria were combined using a weighted overlay, giving the most weight to land cover, followed by elevation and distance to roads. The combined suitability map was then inverted to create a cost surface, representing the ease of movement across the landscape. Next, I converted the protected area boundaries into raster format and applied cost distance analysis to calculate the minimum movement cost from each park across the landscape. Summing the two cost distance outputs highlighted areas with the lowest cumulative travel cost, whi...

  In the first part of this week’s GIS assignment, I performed a land suitability analysis using a weighted overlay method to evaluate potential areas for development based on a combination of environmental and infrastructure-related criteria. The goal was to classify land into five suitability categories, from 1 (least suited) to 5 (most suited), using raster data and weights assigned to different input layers. The analysis involved two main scenarios. In the first scenario, I applied equal weights to each factor: land cover, soil type, slope, distance to streams, and distance to roads. This provided a baseline for comparison. In the second scenario, I applied alternative weights, assigning a higher importance to slope (40%) and reduced weights to the other factors. This alternative approach reflected a scenario where terrain steepness plays a more critical role in site selection. The final results were visualized in a side-by-side map layout, with consistent symbology and classif...

  In the wake of Hurricane Sandy’s landfall in New Jersey, we conducted a GIS-based structural damage assessment focused on a section of the impacted coastline. The goal was to digitize damage data using FEMA-relevant categories and determine patterns in relation to distance from the shoreline. This assessment provided crucial insights that could support insurance claims, emergency response planning, and future risk mitigation strategies. To complete the analysis, we first created a polyline feature class to represent the pre-storm coastline using historical imagery. Then, using post-storm aerial photos, we manually digitized points for each structure along a specific stretch of the coast and classified them into five damage categories: No Damage, Affected, Minor Damage, Major Damage, and Destroyed. We created buffer zones at 100-meter intervals from the coastline to segment the structures into distance bands and used spatial analysis tools to calculate the number of structures per...

  This week’s GIS assignment walked us through a complete spatial analysis pipeline, starting with post-disaster building damage detection and finishing with coastal flood modeling using LiDAR data. We began by creating a Spatial ETL Tool, which allowed us to automate data translation and transformation between different formats. Using this, we compared pre- and post-disaster building data to detect structural changes after a hurricane event. The differences were clear: some buildings were missing in the post-disaster file, which likely indicates they were damaged or destroyed. Visual comparison of layers helped us identify red polygons representing possible destruction zones. The second half of the assignment focused on coastal flooding risk in Florida using DEM rasters. We used both LiDAR and USGS elevation data to identify areas at or below 1 meter above sea level.  We also performed a building analysis on the flooding areas, which revealed key differences between the two e...