RYAN TRAVIS NATION

Research conducted in Summer 2025 on Nihonbashi District of Tokyo, Japan. Analysis included urban flow, heat risk factors, GPS routing, evacuation planning, and vulnerability indexes. Utilized Python Libraries such as Pandas, GeoPandas, and OSMnx in addition to ArcGIS Pro mapping and statistical analysis tools.

Analysis of Affordable Housing stock in Chicago, IL. Investigated physical distribution of units and indexed their zoning classification and proximity to heavy-rail CTA lines. Data cleaning, manipulation, mapping, and analysis were all done in Python.

Compared the performance of the “Big Three” US airline carriers (American, Delta, United) across their individual hub airports, major US city airports, and “non-hub” airports. Various regression analysis techniques were used. All data collection, organization, and analysis was conducted in Python.

URBAN RISK
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TOKYO, NIHONBASHI

AFFORDABLE HOUSING ACCESSIBILITY
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CHICAGO, IL

LEGACY AIRLINE CARRIER PERFORMANCE
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UNITED STATES

Land-use analysis of the tracts surrounding Hartsfield-Jackson Atlanta International Airport. Open-source data from College Park, GA and East Point, GA provided the basis for determining current zoning classifications, property values, and vacant lots. Redistricting recommendations, primarily Transit Oriented Development zones, were then presented to Aerotropolis Atlanta, an airport planning firm. Spatial and data analysis conducted primarily in R.

Preliminary mapping done in order to compare the zoning classification density of various U.S. cities. The selected cities represented varying densities of multi-family residential zones being Austin, TX (Low Density), San Jose, CA (Medium Density), and Seattle, WA (High Density). Findings from this analysis then informed a web-based app for users to determine what levels of housing density would look like in Atlanta, GA, and how these changes would impact socioeconomic trends. All mapping and UX design done in R.

Investigated the anecdotal trend of the “North Side” vs. “South Side” in American cities. Analysis involved geospatial data from the 30 most populous cities in the United States and the distribution of racial groups within each city. Spatial distribution was then correlated between cities to determine if trends were present. Spatial analysis was done in ArcGIS Pro and statistical analysis was done in Python.

HARTSFIELD-JACKSON ATLANTA
INTERNATIONAL AIRPORT
TRANSIT ORIENTED DEVELOPMENT
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ATLANTA, GA

U.S. ZONING DENSITY
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AUSTIN, TX – SAN JOSE, CA – SEATTLE, WA

NORTH v. SOUTH RACIAL DISTRIBUTION
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UNITED STATES