Embedded Files
College Mountain Lands
October 2026
For the past two semesters, I have worked part-time as the College Lands Cartographer, collaborating weekly with Professor and College Lands Conservationist Marc Lapin to translate ecological priorities into visual storytelling. Using QGIS to layer spatial data (parcels, habitat blocks, wetlands, and ecological protection zones) and Adobe Illustrator for final design and typography, I produced a large-format poster map documenting nine College-owned properties across Addison County, each paired with a short narrative on its history and ecological significance.
The result is a piece of public-facing environmental communication: a muted, terrain-based design with a restrained color palette keeps dense spatial data legible, while a locator inset situates the lands within the broader county. This will be displayed in multiple locations across campus, combining art, design, geography, and environmental science to educate students and faculty about the college's land holdings and conservation work.
Yellowstone Fires of 1988
October 2025
Created in QGIS for a GIS & Natural Environment course assessment, this map analyzes burn severity from the 1988 Yellowstone fires using Landsat 5 satellite imagery.
I sourced Landsat 5 imagery from USGS EarthExplorer, using pre- and post-fire scenes to calculate burn severity through spectral analysis, isolating changes in vegetation reflectance to classify the landscape into four severity categories: low, moderate-low, moderate-high, and high. I overlaid this severity raster with the official Yellowstone National Park boundary from USGS and fire complex perimeters from the US Forest Service, including North Fork, Clover-Mist, and Snake Complex, to contextualize where the most intense burning occurred relative to named fire events. I then calculated total area burned within each severity class and added park landmarks, such as Old Faithful and Mammoth Hot Springs, as reference points to help orient viewers unfamiliar with the park's geography.
Bangkok Inundation
October 2025; Revised in January 2026
Originally created in October 2025 for my GIS & the Natural Environment course, this choropleth map was built in QGIS and later refined in Adobe Illustrator in January 2026 to improve its visual clarity and design. The project explores how sea level rise is affecting Bangkok, one of the cities experiencing the fastest rates of sea level rise globally.
I used elevation data from deltaDTM to model areas of Bangkok at risk of inundation under projected sea-level rise, then joined this raster analysis with district boundary data from ArcGIS Hub to calculate the area inundated and population affected at the district level. Thailand's national border, sourced from OpenDevelopment Cambodia, provided geographic context, as shown in the inset map, which locates Bangkok within the country. I classified the results into a choropleth showing the number of people inundated per district, using a graduated red color ramp to visually emphasize the districts facing the greatest population impact. After completing the spatial analysis in QGIS, I exported the map to Adobe Illustrator to refine typography, adjust the color palette for better contrast and readability, and integrate the data table, legend, and supporting text into a cohesive layout.
Bhutan Avalanche Analysis
October 2025
Created in QGIS for a GIS & Natural Environment course assessment, this map examines avalanche risk along a proposed road connecting the remote Bhutanese villages of Gasa and Laya, and identifies a watershed suited for a lower-risk tourist route.
I sourced Landsat tiles from EarthExplorer, SRTM data for DEM tiles and watershed shapefiles, prevailing wind data from the Global Wind Atlas, and the proposed Gasa to Laya road alignment from my professor. To model avalanche risk, I calculated three terrain variables within a 1km buffer of the road: aspect (classifying 105 to 195 degrees as high risk, all other values as NoData), slope (classifying 30 to 45 degrees as high risk, all other values as NoData), and EVI, a vegetation index (classifying values below 2.4 as high risk, all other values as NoData). I combined these three criteria to delineate high-risk avalanche zones and calculated the percentage of high-risk terrain within each of the four watersheds crossing the road corridor.
Roughly 169 hectares of high-risk avalanche terrain fall within the 1km road buffer, with risk varying significantly by watershed, from 9.1% in watershed four to 12.3% in watershed two. Since watershed four carries the least avalanche risk, I identified it as the best candidate for the proposed tourist road, and estimated costs for avalanche mitigation, including reforestation and snow fencing, to support the road's development.
M-22 Scenic Highway
May 2024
Developed in QGIS and Adobe Illustrator as my final project for my Cartography course, this map traces M-22, the scenic highway hugging Michigan's Lake Michigan coastline from Manistee up through Traverse City.
This project was personal before it was academic. My dad had just moved to Manistee, right where M-22 begins, and I wanted an excuse to actually learn the geography of a place he now called home. Mapping it felt like a way to get to know the region alongside him, one lake, trail, and small town at a time.
I sourced road, trail, and hydrology data from Michigan GIS Open Data, using QGIS to extract and symbolize the M-22 corridor alongside nearby hiking trails, biking trails, and combined hiking-and-biking routes. I layered in campground locations and towns along the route for practical reference, and used Natural Earth data for broader geographic context. After building the spatial layers in QGIS, I brought the map into Adobe Illustrator to refine the color palette, add the M-22 route marker icon, and finalize typography and layout for a clean, road-trip-ready design.
The result doubles as a personal travel reference, highlighting the lakes, trails, and campgrounds along one of Michigan's most iconic drives, and a small piece of getting to know my dad's new home from a few states away.
Chipman Hill Topography
April 2024
Produced in QGIS and refined in Adobe Illustrator, this topographic map highlights the elevation and terrain of Middlebury's Chipman Hill for my Cartography course.
I used elevation data from the Vermont Center for Geographic Information (VCGI) to generate a hillshade layer, giving the terrain a sense of relief and depth. I then derived contour lines at a fine interval to trace the hill's topography in detail, layering these over a graduated color classification of elevation, ranging from greens at lower elevations to tans and creams above 800 feet, to visually reinforce the rise in terrain. A Natural Earth basemap globe and north arrow were added for geographic context, with cartographic design work in Adobe Illustrator to refine labeling, typography, and the final layout.
Valentine's Day Map
January 2026
Made during a college workshop with a fun challenge: create a complete map in under an hour using a unique projection. I picked the Bonne projection for its naturally heart-shaped curve when centered a certain way, and leaned all the way into it.
I generated the projection and world basemap in QGIS, then brought it into Adobe Illustrator to add a blush color palette, a decorative scatter of hearts, and a playful "hugs" scale bar in place of a traditional distance scale, because why not measure the world in hugs instead of kilometers?
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