Movement takes the many Uber trips happening everyday and translates them into historical zone-to-zone travel time information. The granular travel time insights mean cities no longer have to commission resource intensive travel time studies to understand what’s happening on their streets. The data can be used for numerous transportation planning and policy projects, like identifying congestion trends, and measuring the impact of road closures and infrastructure improvements.
The positive reception Movement received across the world is a testament to its potential to help solve challenging mobility issues many cities are facing. However, as we bring Movement to more cities around the world, we’ve been thinking hard about other ways this data may come in handy. We know transportation impacts different facets of our lives, so: can we use this dataset to understand and address issues outside the realm of transportation?
Today, Uber is excited to announce the Cincinnati Mobility Lab, a three year partnership with the Cincinnati region’s local government and transportation providers. As part of this effort, we have engaged with OKI, our local partner, on new ways this data can be be used for research and analysis. Our conversations with the OKI team have led to interesting ideas and we are excited to share our early explorations around using Movement data to understand access to important services and infrastructure.
Understanding Accessibility Issues using Uber Movement
Accessibility refers to a person’s ability to reach a service, activity or place. The zone-to-zone nature of Movement data makes it possible to measure access to a zone in terms of travel time. If the zonal location of a public resource or essential amenity can be identified, Movement can measure access to the resource or amenity from other places in the region. Put differently, it can estimate how long it takes to get to a specific location from different parts of the city. Given how access to certain key amenities can drastically impact quality of life, this approach can be a useful aid in a city’s decision making process.
To coincide with bringing Uber’s Meals on Wheels partnership to the Cincinnati region as part of the Mobility Lab, we looked at access to healthy food in the Cincinnati region for this exploratory analysis. By measuring travel times from supermarkets — an important indicator when measuring access to healthy food — we hope to contribute to conversations around identifying food deserts, or low access to healthy meal options. Healthy produce is undoubtedly available from other sources throughout the city, but we only included supermarkets in this initial analysis.
Looking at Movement travel times data from supermarket locations enables the straightforward measurement of travel times from established sources of healthy food. In Figure 1, we’ve visualized travel times from a supermarket in the featured zone. White zones have near-immediate supermarket access based on travel time, while darker colors show travel times from the supermarket in a 5 and 10 minute timeframe.
Taking this a step farther, in Figure 2 we’ve applied the same methodology to the entire Cincinnati region. For each zone, the travel time from the nearest supermarket (zone) is calculated, providing a region-wide map of access to healthy food. This information, in turn, has the potential to inform future planning and decision making to minimize coverage and access gaps.
As you can see highlighted in Figure 3, the dataset provides a straightforward mechanism for visually identifying potential food deserts — areas with inadequate access to healthy food options. Additional datasets, particularly around population density and land use, can help determine whether or not increased travel times to a given zone are problematic; this approach provides a straightforward way to highlight those areas for further investigation. In this example, we’ve zoomed into an area of western Cincinnati with visible valley of increased travel times between supermarkets. Travel times are over 8 minutes, which is significantly longer than other areas in the city center.
It’s important to emphasize that there are limitations to this approach. For one, it’s only looking at road travel times. When understanding access, it’s vital to include travel times on other modes such as walking, biking or transit. Second, the travel times are averaged across the entire zone, and therefore only provide an approximate travel time to and from the location of interest. Finally, travel times only explain one part of accessibility. Other important elements such as cost of access aren’t included. Our aim is not to provide the complete picture, but rather an effective and flexible dataset that can contribute to addressing these types of questions, in combination with other publicly available data resources.
While we have focused on travel times from known healthy food sources in this post, we’re intrigued by the possibilities to build on the early work we’ve begun here in collaboration with our partners in the Cincinnati region. A retooling of this approach juxtaposed with transit, survey and census data can be helpful when optimizing the placement of healthy food options. Alternatively, the universal nature of this approach means it can be applied to build access or coverage maps for any given set of facilities or services. This could include public schools, hospitals, emergency services, voting booths, or major job centers. We look forward to working with our partners in the Cincinnati region to explore these possibilities in more detail.
Explore and download travel times data at movement.uber.com.