Exploring Geospatial Data with kepler.gl

Shan He
Published in
6 min readAug 26, 2019

Co-authors: Gabriel Durkin, Sina Kashuk

kepler.gl is an advanced geospatial visualization tool open sourced by Uber’s visualization team in 2018 and contributed to the Urban Computing Foundation in early 2019.

Figure 1. Using kepler.gl to visualize San Francisco building footprint

At Uber, kepler.gl is the de facto tool for geospatial data analysis. In a previous article, we introduced kepler.gl for Jupyter Notebook. In this article, we want to showcase how data scientists at Uber use kepler.gl to understand massive amounts of aggregated geospatial data and derive insights that improve our business. All the analysis presented in this blog post is based on data aggregated by H3, Uber’s open source geospatial indexing system, with an aperture equal to 12, for the locations with a minimum of 100 trips counts using at least 6 months of data.

Figure 2. Maps… without maps. — Toronto request data with kepler.gl derived from aggregated rider GPS signals

Uber’s platform leverages digital solutions to tackle transportation problems in the physical world, such as ridesharing and meal delivery. Gabriel Durkin and Sina Kashuk, data scientists from Uber’s Rider Geospatial Intelligence team, leverage kepler.gl to analyze trip data, specifically to understand the real-world challenge of driver-partners and riders locating each other for pick-up in a complex cityscape. Figure 2, above, illustrates how the projection of the pick-up data at high resolution can create a map of the city of Toronto based entirely on usage of the Uber app, without leveraging a single base map.

Figure 3. Visualizing highest concentrations of requests in New York City over a 24 hour period

The pick-up process of an Uber ride or Uber Eats meal is one of Sina and Gabriel’s biggest data science pain points. There are many geospatial challenges associated with the pick-up process, and our teams frequently use kepler.gl to inform the development of geospatial solutions to improve this part of the ridesharing and delivery experiences. A common visualization rendered for this type of problem solving is a time lapse animation, as depicted in Figure 3, above, to identify temporal trends and areas with a higher concentration of trip requests, which may be correlated…

Shan He