One of the most common data types to collect in IoT scenarios is GPS data. At Continental we meet those data in many different Use Cases. As Data Scientist we have to handle huge masses of GPS data so we had to develope some methods to handle those data. In this talk we show show some of them. Among other it can be shown how to check for quality in GPS data, calculate Kernel densities for Truck tracks, Optimize flawed data using Kalman Filtering and presenting Spatiotemporal data for different use Cases. Most of the examples will be done using R and Shiny but Examples in Python and QGIS are also available. The audience will recieve a good overview how to work with Geospatial data even when the data size is huge.
Dubravko Dolic, Lead Architect Advanced Analytics, Continental Reifen Deutschland GmbH