A Sociodemographic Analysis of Equitable Distribution of Alternative Fueling Stations

Amid the global emphasis on sustainable transportation, the distribution and accessibility of alternative fueling stations in the U.S. have become focal points of interest. Analyzing data up to as recent as  2023, this study identifies a dynamic evolution in fueling choices, with electric vehicle charging stations notably gaining prominence. By integrating the stations' dataset with the expansive Smart Location Database (SLD) that captures a myriad of diverse attributes like housing density, land use, and sociodemographic attributes, this study aims to unveil patterns and equity in the spread of these stations. Leveraging machine learning, the study employs equity analysis metrics like the Gini coefficient to assess the evenness of station distribution across diverse sociodemographic sectors. Concurrently, geospatial methodologies offer visual depictions of station densities, highlighting booth concentrated zones and potentially underserved areas. Delving deeper, Topographical Data Analysis (TDA) algorithms will be harnessed to discern clusters of areas characterized by analogous sociodemographic attributes, providing insights into the distribution dynamics of fuel stations within the clusters. This study aims to furnish insights that can steer policy and infrastructural initiatives, championing equitable access to eco-friendly transportation alternatives for the entire U.S. population.