Geographer · GeoAI Director · Studio Founder
I'm a geographer who builds spatial applications for complex datasets. PhD in Geography from Kentucky, Director of Geospatial Technology & GeoAI at GeoConvergence — and founder of NeverLost, a studio that turns messy public data into clear geographic insight.
NeverLost Studio
A family of mapping products built on the same methodology — take a rich public dataset, find the spatial signal in it, and make the insight accessible. Each app has its own domain and audience, all under the NeverLost umbrella.
In development
sundays.neverlost.studio
NFL travel intelligence
Geographic analysis of every NFL team's road schedule. Week-by-week travel distance, trip difficulty scoring across four components (rest window, distance, altitude, weather exposure), cumulative season load profiles, and an interactive great-circle route map — including all 9 international games across 3 continents.
In development
saturdays.neverlost.studio
CFB travel burden · recruiting geography
The Sundays methodology applied to college football, with a second spatial lens: recruiting. Where do programs pull talent from geographically? How does conference realignment reshape travel burden? Two stories told together across 130+ FBS programs.
In development
reportcard.neverlost.studio
NAEP state achievement · mapped over time
The Nation's Report Card reimagined as a spatial product. State vs. state performance comparison across grades, subjects, and decades. The geographic patterns in achievement gaps that aggregate statistics hide. Built for educators, policy researchers, and journalists who know the data but haven't seen it this way.
Coming soon
energy.neverlost.studio
EIA data · infrastructure geography
The spatial story of American energy — infrastructure, generation mix, pricing patterns, and the transition from fossil fuels to renewables — told through geographic data from the U.S. Energy Information Administration.
The methodology
Every NeverLost product follows the same four steps. The datasets change; the spatial reasoning doesn't.
01
Public data that's rich, underexplored spatially, and used by a real audience — NFL schedules, NAEP results, EIA infrastructure records.
02
A derived index that requires domain knowledge to build correctly. Trip difficulty score, achievement contour, recruiting range — not just a map of raw data.
03
ETL, PocketBase, deck.gl, MapLibre. A lightweight stack that loads once and filters in memory — fast, self-contained, deployable anywhere.
04
Free tier for headline indices. Paid tier for full data, week-by-week breakdowns, and export. Gating at the database level, not just the UI.
Research
Six peer-reviewed publications, 350+ citations. The through-line is applying spatial methods to problems others haven't framed geographically — which is also what NeverLost does.
Biogeomorphic Impacts of Invasive Species
Annual Review of Ecology,
Evolution & Systematics
Identification of Understory Invasive Exotic Plants with Remote Sensing in Urban Forests
Int'l Journal of Applied
Earth Observation
Expert Systems Model for Kentucky Arrow Darter Habitat in the Upper Kentucky River Basin
Papers in Applied Geography
Intermodal Network Model of Coal Distribution in the United States
Transportation Research Record
Expert Systems Archaeological Predictive Model
Transportation Research Record
Soil Deepening by Trees and the Effects of Parent Material
Geomorphology
About
Credentials
I'm a geographer by training and a builder by inclination. My PhD research focused on the spatial dimensions of biological invasion, soil dynamics, and habitat modeling — but the real question underneath all of it was the same: how do you turn messy spatial data into something a decision-maker can actually use?
That question drove me from academia into industry, where I now lead geospatial technology and GeoAI at GeoConvergence. It also drives NeverLost — the studio I run through Shouse Solutions LLC, where the same methodology gets applied to NFL schedules, NAEP test scores, college football recruiting maps, and energy infrastructure.
The datasets change. The approach doesn't. Find the spatial signal, design a metric that requires real domain knowledge to build correctly, and put it in front of people who can use it.
I'm based in Louisville, KY. I also work on AI integration in K–12 education — helping school districts and edtech teams think clearly about what generative AI actually means for instruction and administration, and building tools when the existing ones don't fit.
Get in touch
A 30-minute call is usually enough to figure out if there's a fit — whether that's a NeverLost app, AI integration for a school district, a geospatial problem, or something that doesn't fit neatly into any of those boxes.