Uber's kepler.gl, an open source toolbox for GeoSpatial Analysis

Geography Visualization, also called as Geovisualization plays a pivotal role in areas like cartography, geographic information systems, remote sensing and global positioning systems. Uber, a peer-to-peer transportation network company headquartered at California believes in data-driven decision making and hence keeps developing smart frameworks like deck.gl for exploring and visualizing advanced geospatial data at scale. Uber strives to make the data web-based and shareable in real-time across their teams and customers.

Early this month, Uber surprised the geospatial market with its newly open-source toolbox, kepler.gl, a geoanalytics tool to gain quick insights from geospatial data with amazing and intuitive visualizations.

What’s exactly Kepler.gl is?

kepler.gl is a visualization-rich web platform, developed on top of deck.gl, a WebGL-powered data visualization library providing real-time visual analytics of millions of geolocation points. The platform provides visual exploration of geographical data sets along with spatial aggregation of all data points collected. The platform is said to be data-agnostic with a single interface to convert your data into insightful visualizations.

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The platform is very user-friendly where one can just drag the CSV or the GeoJSON files and drop them into the browser to visualize the dataset more intuitively. The platform is supported with different map layers, filtering option, aggregation feature through which you can get the final visualization in an animated format or like a video. The usability of features is so high that you can apply all the metrics available to your data points without much of a hassle. The web platform exhibits high performance where you can get insights from your spatial data in less than 10 minutes and that too in a single window. Another advantage of this framework is it does not involve any sort of coding and hence non-technical users can also reap the benefits by churn valuable insights from the data points.

The platform is also equipped with some advanced, complex features such as 2D cartographic plane,a separate dimension for altitude, visibility of height of hexagon and grids.

The users seem happy with the new height feature which helps them detect abnormalities and illicit traits in an aggregated map. With the filtering menu, the analysts and engineers can compare their data and have a granular look at their data points. This option also helps in reading the histogram well and one can easily detect outliers and make their dataset more reliable. It  has a feature to add playback to time series data points which makes getting useful information of real time location systems easy.

The team at Uber looks at this toolbox with a long-term vision where they are planning to keep adding new features and enhancements to make it highly functional and a single-click visualization dashboard. The team has already announced that they would be powering it up with two major enhancements to the current functionality in next couple of months. They would add support on,

• More robust exploration: There will be interlinkage between charts and maps, and support for custom charts, maps and widgets like the renowned BI tool Tableau through which it will facilitate analytics teams to unveil deeper insights.
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• Addition of newer geo-analytical capabilities: To support massive datasets, there will be added features on data operations such as polygon aggregation, union of data points, operations like joining and buffering.

Companies across different verticals such as Airbnb, Atkins Global, Cityswifter, Mapbox have found great value in kepler.gl offerings and are looking towards engineering their products to leverage this framework. The visualization specialists at these companies have already praised Uber for building such a simple yet fast platform with remarkable capabilities.

To get started with kepler.gl, read the documentation available at Github and start creating visualizations and enhance your geospatial data analysis.

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