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Learn how to use Kepler.gl for data visualization through our tutorial, where we show how easy it is to load multiple datasets into Kepler.gl to visualize traffic safety in Manhattan. Urban traffic can be dangerous, a point known all too well by city dwellers and drivers. Discovering the most dangerous street locations in a city can help urban planners take steps to enhance safety through strategies such as lower speed limits or traffic rerouting.

Data visualization is a key step to understand the dataset and draw inferences from it. While one can always closely inspect the data row by row, cell by cell, it’s often a tedious task and does not highlight the big picture. Visuals on the other hand, define data in a form that is easy to understand with just a glance and keeps the audience engaged. Matplotlib is a basic library that provides options for various plots along with extensive customizations in the form of labels, title, font size etc. I watched numerous videos and read articles online for visualization. To understand Matplotlib better, I took the population density dataset from Kaggle and started creating my own visualizations.