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Data / ML, Engineering

From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox

May 29, 2018 / Global
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Figure 1: The common process of creating a data visualization includes data collection, data processing, visual exploration, and then layering.
Figure 2: kepler.gl is a data agnostic, high-performance web-based application for large-scale geospatial visualizations.
Figure 3: kepler.gl can render millions of points and perform spatial aggregations on the fly.
Figure 4: The kepler.gl UX flow is composed of five layers, including hexagon, arc, and point.
Figure 5: kepler.gl’s point, arc, and heatmap layers (top)  and grid, hexbin, and polygon layers (below) provide rich geospatial data analysis.
Figure 6: kepler.gl aggregates points with a 3D hexbin layer.
Figure 7: kepler.gl enables you playback time series’ to visualize spatio-temporal data.
Figure 8: kepler.gl lets you use brushing to explore origin-destination correlations.
Figure 9: Users can render layers with subtractive blending (left) and additive blending (right).
Figure 10: kepler.gl depicts elevation contours of San Francisco and Treasure Island/Yerba Island.
Figure 11: A New York City Census tract population map depicts how populous certain area of the cities were in 2010.
Figure 12: An origin-destination map of residents in England and Wales uses 3D arcs to visualize commute patterns.
Figure 13: Diego and his team use kepler.gl to visualize open data in Brazil for their urban design research:
Figure 14: Will Geary, a data scientist at CitySwifter, uses kepler.gl’s brushing interaction to explore home to work commutes in New York City.

Posted by Shan He