How I used Elastic Maps to plan my cycling trip and find unique roads
Blog post from Elastic
Philipp Kahr describes how he used Elastic Maps to enhance the planning of his cycling trip by identifying unique roads and visualizing ride data. Initially, he planned the trip using Komoot but sought a method to track route frequency and visualize the data dynamically. By converting GPX files from Komoot into GeoJSON format, he imported the data into Kibana, a part of the Elastic Stack, to create visualizations of his cycling routes. This process involved adding additional details like speed and ascent to the GeoJSON file and using Kibana's mapping and data visualization tools to display and analyze the cycling data. Kahr further utilized geotile aggregation in Kibana to determine unique and repeated road sections, allowing for a more detailed understanding of his cycling routes. He explains how these visualizations can be used to calculate the percentage of unique versus duplicated road segments, offering insights into route choices and patterns that could be expanded to analyze larger datasets, like those from Strava rides, over different seasons.
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