Uber Movement

The goal of Uber Movement is to provide data and tools for cities to more deeply understand and address urban transportation challenges. At its core, our team believes that working with cities to improve infrastructure side-by-side is the future.

 

 

Upon joining the team, my goals were to:

  • Understand our core customers and their journeys more deeply, 
  • Expand and improve the product offerings under the Uber Movement umbrella and
  • Discover and increase impact we have in cities

 

 

 

New Mobility Dashboard

Our first MVP of the new mobility dashboard was a pivotal step towards helping regulators, planners, and city operators understand the activity of bikes and scooters in their city via key metric and geospatial data. Having launched a very simple MVP in a matter of weeks, we were surprised by the positive feedback we received from our users. The new mobility dashboard was a uncharted waters, but in the end it turned out to be a good bet and spun off a lot of internal cross-org collaboration and external city relationship building.   

 

 

Eventually we found the dual geospatial maps, which represented the two data points (pick-up and drop-off, aka PuDo), to be a lesser experience. I advocated for merging them into one map – the tricky part was figuring out the interaction pattern of how a user drills down into the hexagon to get both parts of the data. Later we released a merged map and and improved drill down experience, as well as a new modular framework for these dashboards.

 

 

 

Launching Movement Speeds

For the first time, we were able to map the speed data to individual street segments (which is, right now, the only universal measure of roads), achieving the highest granularity yet. For more info about this dataset, you can view our methodology paper or read about it here.

Movement Speeds gif

The goal of the speeds data set was to empower planners to pinpoint choke points, identify unsafe streets for pedestrians or bikers, or understand congestion patterns as a whole. Our experimental goal was to unlock other use-cases, alongside our other data products, that could present a brand new perspective to solving for congestion or safety in a city (or perhaps another use-case we didn’t know about yet). A big part of this process was soliciting continual feedback from several city partners and showing them prototypes and designs. From the feedback, we found that users often are looking for extremes and anomalies in their analysis; On the legend, you can see two black circular handles – these handles allow the user to focus on viewing speeds they’re interested in and eliminating noise.

 

 

We also experimented with street/corridor analysis, allowing further deep dives into the data. The data panel that pops up on the right (User can toggle this at any time) is a consistent experience we wanted to bring to all of our data tools to allow deep analysis when the time was right. It was important to not take up too much space with UI elements that weren’t needed at the time. Creating a more modular UI allowed for more flexible discoverability of data and analysis.

 

 

Product entry rehaul

In order to get the best out of our public launch of the speeds dataset, we also wanted to reassess how effective our home page was. The previous home page experience was your typical cookie-cutter landing page with a very un-engaging hero video, a couple of CTA buttons and some basic information below the fold. In collaboration with Charlie Waite, the product entry flow and first-time experience became more focused, engaging, and targeted specific customers. We centered the experience all around cities.

 

 

 

 

 

Product Parity • Consistent experiences across products

Another big goal of mine was to unify the experiences the user has across the products – because before there was dissonance, inconsistent UI Patterns and interactions, and different features that might cause confusion.

 

For example, Travel Times allowed the user to download the data after they’ve filtered it, and Speeds didn’t at first. The filtering offered different capabilities, as well, for no particular reason – I saw many opportunities like these to create parity where it made sense and to allow for custom functionality where it made sense… As long as the user has similar expectations about what they can do or get from these products.

 

 

Date

December 16, 2019

Category

data vis, interaction, product, research, user experience

Tags
city data, data vis, geospatial visualization, product, research