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Understanding human mobility patterns is important in applications as diverseas urban planning, public health, and political organizing. One rich source ofdata on human mobility is taxi ride data. Using the city of Chicago as a casestudy, we examine data from taxi rides in 2016 with the goal of understandinghow neighborhoods are interconnected. This analysis will provide a sense ofwhich neighborhoods individuals are using taxis to travel between, suggestingregions to focus new public transit development efforts. Additionally, thisanalysis will map traffic circulation patterns and provide an understanding ofwhere in the city people are traveling from and where they are heading to -perhaps informing traffic or road pollution mitigation efforts. For the firstapplication, representing the data as an undirected graph will suffice. Transitlines run in both directions so simply a knowledge of which neighborhoods havehigh rates of taxi travel between them provides an argument for placing publictransit along those routes. However, in order to understand the flow of peoplethroughout a city, we must make a distinction between the neighborhood fromwhich people are departing and the areas to which they are arriving - thisrequires methods that can deal with directed graphs. All developed codes can befound at https://github.com/Nikunj-Gupta/Spectral-Clustering-Directed-Graphs.

Understanding human mobility patterns in Chicago: an analysis of taxi data using clustering techniques