Spatial Inequity in Private Transportation Access as Determined by Ride-share Demand, Vulnerability, and Inclement Weather in Chicago, 2021
By: Lizzie Lee
Tags: Ridesharing, Data, Chicago, Data Science, Data Visualization

Spatial Inequity in Private Transportation Access as Determined by Ride-share Demand, Vulnerability, and Inclement Weather in Chicago, 2021 Lizzie Lee Nov 23, 2021·5 min read

Chicago experienced its ninth snowiest February on record this year, braving through a total of 21.6 inches of snow within the span of the 28 days, with average high temperatures nearly eight degrees below normal levels for that time of year. At the same time, private transportation network companies (TNCs), the likes of Lyft and Uber, responded to a surge of demand from Chicagoans.

A quick look at the 2021 monthly trends of ridership provided by TNCs (Figure 1) shows a steep incline between the start of February and the very beginning of March. When considering that the warmer weathers intuitively yield to higher rates of ride-share trips for those better able to enjoy venturing outside in more favorable conditions, as well as the general uptick as the chaos of the COVID-19 pandemic may start to stabilize, the sudden change in demand between the days of February 2021 calls for more insight.

Figure 1. Monthly ride-share trips in Chicago, January to September 2021

Utilizing information provided by the Chicago Data Portal on ride-share trips provided by TNCs, I will address the question: Is the spatial relationship between the distribution of increased demand in services provided by private TNCs and the spatial trends of vulnerability, as assessed by the Chicago COVID-19 Community Vulnerability Index (CCVI), negatively correlated during the coldest month this year in Chicago? In other words, is there higher demand in areas of lower vulnerability?

In setting my hypothesis, I anticipate that the answer will prove to show that the benefits of this alternative mode of transport is spatially exclusive to areas of lower vulnerability; this study will be yet another example of inequities in access to transportation for the most vulnerable in urban settings.

Due to the limited data and capacity, this study does not conduct a network analysis to more accurately portray “costs” to show the actual networked distance of travel between the start point and destination. In effort to better understand the spatial distribution of the demand, this study focuses on trip start locations only and seeks to correlate by community areas the change in demand for ride-share trips in February 2021, as well as see the corresponding CCVI of the respective community area. The CCVI was chosen to reflect broader social vulnerability that is both measured through the indicators of the CCVI and thematically relevant to the scale of time for the study — surely, 2021 is not yet free of the grasp of the pandemic. Moreover, the study area is divided into Community Areas, given that it spans a larger distance than Census Tracts and in better alignment to the publicly available information disclosed in accordance with the City of Chicago’s Open Data Portal Team’s statement on privacy. Provided below is a reference map (Figure 3) for the Chicago city boundary as divided by the appropriate Community Areas.

Figure 3. Reference map of Chicago’s Community Areas

Findings

Trend lines of demands for ride-share in February (Figure 2) parallel a timeline of a brutal and volatile winter in Chicago. For example, on February 15th, there was a massive snowstorm that trapped many indoors; those travelling — and driving — that day and those immediately before and after did so by involving higher degrees of risk. The days that were relatively temperate enough to go venture out but still miserably freezing align with the four peaks as shown.

Figure 2. Total ride-share trips per day in Chicago, February 2021

Within the 28 days of the month, the average trips taken per day were estimated around 112,633 with a considerably broad range between the minimum and maximum — 1,962 and 160,650 trips, respectively (Table 1).

Table 1. Descriptive statistics of ride-share trips taken per day in Chicago, February 2021

Deploying the use of geospatial analytic tools with Python, the study yielded a matrix of choropleth maps of ride-share data per day, that further highlight in brighter colors areas of higher density of demand (Figure 4). Immediately, it shows a fair continuity of the spatial distribution of demand, brightest in the community areas nearest to the hub of the city.

Figure 4. Rates of daily ride-share trips per Community Area in Chicago, February 2021

Furthermore, the correlation scatter matrix (Figure 5) below depicts the relationships among the three variables of interest: community area (“pickup_community_area”), number of ride-shares (“travel_id_counts”), and the CCVI score (“ccvi_score”). Table 2 shows the results of the correlation tests. In both the scatter plots and correlation coefficients highlighted pink, there is reason for a compelling, negative relationship between the number of ride-shares demanded and higher vulnerability scores.

Figure 5. Correlation scatter matrix

Table 2. Correlation coefficient (Pearson)

Conclusion

Though the measures are not infallible, the study of the spatial relationship between ride-share demand and vulnerability show a negative correlation in times of the most inclement weather in Chicago this year. The higher demand in areas of lower vulnerability validates my initial hypothesis, though more research is needed to provide further nuance. For example, knowing that socio-economic factors contribute to public transit ridership, it would be an interesting analysis to bring in data from the Chicago Transportation Authority on daily ridership trends and spatial accessibility to public transportation into addressing the initial research question.

Implications of such spatial inequality of the use of private and often-pricier modes of transport as being spatially exclusive to areas of lower vulnerability are clear. The pandemic emphasized time and again that those most disadvantaged are often the ones who cannot afford to stay home in times of distress, and the spatial distribution of ride-share demand and vulnerability show that the already disadvantaged are excluded from the benefits of TNC services in Chicago.

The published story was completed as an assignment for PLA6619: Introduction to Urban Data and Informatics, Fall 2021, taught by Professor Boyeong Hong at Columbia University GSAPP. Lizzie Lee is a candidate for the Masters of Science in Urban Planning and pledges to continue posting on Medium.