Got a Ride? — Analyzing the Equity of Ridesharing in Chicago
By: Kirthi Balakrishnan
Tags: Geopandas, Folium, Urban Planning, Chicago, Python

Ridesharing apps like Uber and Lyft have been a game-changer in the transit functions of cities. Being a booming business in Chicago, with the number of registered vehicles used for companies like Uber and Lyft quadrupling over a three-year stretch (Wisniewski, 2018), ridesharing has become an important part of the transportation landscape.

Is ridesharing beneficial?

One of the primary benefits of ridesharing is a reduction in individual car usage. This can be by allowing greater combinations of methods to reach one’s destination, encouraging greater multi-modality in commute decisions.

Lyft spokesman Scott Corielle cited that Lyft Line, Lyft’s shared-ride alternative that places multiple passengers in a single automobile, accounts for approximately forty percent of rides in markets where it is available, which includes Chicago. He additionally mentioned a report that approximately a quarter-million Lyft passengers say they got rid of an automobile owing to the provision of ride-sharing.

Who benefits?

Clearly, there are benefits to having a ride-sharing provision in the city, and it is safe to say that ride-sharing is an asset to the city, mobility-wise (without getting into the whole congestion debate). But are these assets distributed across the city in an equitable manner?

A possible hypothesis is that many of these services are offered at a higher extent in high-income and perhaps predominantly white neighborhoods. We can also take a look at how rideshares have been affected over the past several years, particularly the manner in which it was affected by COVID-19.

The Chicago Transportation Network Providers (TNP) dataset is a comprehensive dataset consisting of data from all the rideshare companies in Chicago. It includes the date of the rides, pickup, and drop-off locations, distance traveled, etc — all in a data-mungable, yet anonymized format. Unfortunately, the data available is quite limited, with the earliest entry being from November 2018. However, there is still enough time before and after the worst of the pandemic for the data to remain reasonably objective (or so we shall assume, for the sake of this experiment).

Despite the short date range, the dataset itself is quite comprehensive and consists of over 200 million rows (RIP your execution speed). So, for the sake of practicality (and because my computer had to crash several times before I learned my lesson), we will be exploring the data from the 1st of October 2020 to the 1st of November 2021 — not that that is a small dataset either. It has over 42 million rows by itself, and that’s after dropping all the invalid values.

This analysis considers the 77 Community Areas of Chicago as the boundaries, using the EPSG:4326 projection. It also happens to be the one that is most compatible with the mapping tools that will be employed for today’s study.

How are rideshares spatially distributed in Chicago?

Heatmap Showing Pickup Location Distribution

Heatmaps are a pretty good way to map densities — well, sometimes. Usually, they tend to be a reflection of the distribution of populations as referenced in the famous XKCD comic, so they’re overall not the best metric to go about measuring datapoint densities. But they usually result in an interesting visual result, especially if the data points are highly scattered — such as the ones in this dataset, where each point corresponds to a pickup location in the city.

So, I decided to give heatmaps a go on folium, and right off the bat, we can see that there are some areas in which ridership seems to be concentrated, and the spatial distribution… scatters towards the outer boundary. This is a pretty strong indicator that using a heatmap was a bad idea. This is clearly just a reflection of population densities.

Let’s try mapping this differently. Regardless, an interactive version of the map can be found here, if you are interested.

The most appropriate way to use maps for this study is in tandem with demographic characteristics, specifically the ones related to the hypothesis — i.e. race and median income. Taking this data from the US Census and modifying it slightly, we can extrapolate the necessary data subsets.

Writing a python script for simple color-ramps to map the Median Income and Percentage of White Populations per Community Area, and superimposing a scaled scatter plot of the number of pickups, we get these two maps: