Visualizing Summer Travels

projected-shapefile-gps-coordinatesThis is a series of posts about visualizing spatial data. I spent a couple of months traveling in Europe this summer and collected GPS location data throughout the trip with the OpenPaths app. I explored different web mapping technologies such as CartoDB, Leaflet, Mapbox, and Tilemill to plot my travels. I also used Python and matplotlib to run some descriptive statistics and visualize other aspects of my trip.

Here is the series of posts:

My Python code is available in this GitHub repo. I also did some more involved work under the hood to prep the data and support these visualizations. For example, in the following posts I reverse-geocoded the spatial data set and reduced its size with clustering algorithms and the Douglas-Peucker algorithm:

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Visualizing Summer Travels Part 3: Leaflet

This post is part of a series on visualizing data from my summer travels.

I’ve previously discussed my goals in visualizing GPS data from my summer travels and explored visualizing the data set with CartoDB. The full OpenPaths location data from my summer travels is available here and I discussed how I reverse-geocoded it here.

Lastly, I reduced the size of this spatial data set so Leaflet can render it more quickly on low-power mobile devices. I discussed why this is important and how to do it with the DBSCAN clustering algorithm and also with the Douglas-Peucker algorithm. The final data set I’ll be working with is available here.

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Reducing Spatial Data Set Size with Douglas-Peucker

In a previous post I discussed how to reduce the size of a spatial data set by clustering. Too many data points in a visualization can overwhelm the user and bog down on-the-fly client-side map rendering (for example, with a javascript tool like Leaflet). So, I used the DBSCAN clustering algorithm to reduce my data set from 1,759 rows to 158 spatially-representative points. This series of posts discusses this data set in depth.

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