Abstract
This study investigates the impact of socioeconomics and demographic factors (e.g., population density, minority rate, age, gender, education, wealth, and crime) and transportation infrastructure (e.g., walk score, transit score, and bike score) on the accessibility of Uber in the city of Philadelphia. K‐means clustering is applied for initial data exploration.
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