Your Life on a Map: Private Geospatial Analytics with Kotlin DataFrame and Kandy | Aleksei Zinovev

Recording brought to you by American Express. https://americanexpress.io/kotlin-jobs In the age of fitness trackers, smartwatches, cycling computers, and travel apps, each of us generates a huge amount of personal geographical data - daily steps, running routes, bike rides, hikes, city walks, and road trips. But how do we turn this raw stream of GPX, GeoJSON, KML, or TCX files into meaningful insights, clean datasets, and reliable analytics? These datasets are rich and deeply personal - and not everyone wants to upload them to cloud platforms or rely on whatever limited dashboards a mobile app provides. Sometimes the analytics you want simply aren’t there, and sometimes privacy matters more than convenience. In this talk, I’ll show how Kotlin DataFrame and Kandy can help you process and analyze personal GPS data entirely on your own machine. We’ll parse and clean tracks collected by everyday mobile apps, handle common anomalies and sensor glitches, enrich the data with useful metrics, and create just enough visualization to understand mobility patterns - without promising a full-blown GIS system. This session demonstrates a practical Kotlin-native workflow for anyone who wants deeper insights into their own movement data while keeping full control and privacy.