# Worldwide GPS tracks with OpenStreetMaps for urban design analysis

I work as an architect, and one of the data available to us when
masterplanning and early phases of an urban design project is GPS
track activity. Knowing where people drive, where people walk, and
cycle and recreate allows us to make decisions such as where to define
architectural axes, where to place retail, and how to extend public
transport and pedestrian walkways.

One of the resources available is from a company known as Strava, who
runs a proprietary fitness social network, where fitness buffs can
track their movements via GPS devices (which can be as simple as your
phone) and compare cycling routes, distances run, and so on. Primarily
used by runners and cyclists, these GPS logs are voluntarily uploaded
to Strava, who then aggregates all the data and resells it to urban
design parties, known as their "Strava Metro" initiative.

Publicly without purchasing any data, Strava also hosts a
[global GPS heatmap](https://www.strava.com/heatmap) where you can
visually see the fire of activity by runners and cyclists. Zooming in
shows you right down to where people run down various streets. As a
high-level overview, this is a great graphic and can immediately
pinpoint activity. It also is
[a fantastic feat of engineering](https://medium.com/strava-engineering/the-global-heatmap-now-6x-hotter-23fc01d301de),
processing 5 terabytes of raw input data. That's big data!


Of course, just recently Strava decided to stop showing this public
heatmap at high zoom levels and locked it behind a paywall.
Thankfully, there are alternatives.

In
[a previous post](../openstreetmaps-open-source-maps-application/article.md),
I introduced an open-data initiative known as
[OpenStreetMaps](https://openstreetmaps.org/). Strava is largely based
on OpenStreetMaps and uses it as a base layer embedded into MapBox,
and also has a fork of the OSM iD editor called "Strava Slide", to
allow people to edit routes based off strava GPS data tracks. However,
OSM itself has many active GPS track contributors (used for various
purposes, such as mapping new routes and calibrating the map), and we
can use this open data in lieu of the proprietary product offered by
Strava. Below, we see the world's GPS tracks from the perspective of
OSM [visualised by Pascal Neis](http://neis-one.org/).


Before I get into the specifics of getting GPS data, I'd like to show
you what data is in a GPS track. Here's some GPS tracks visualised
with [JOSM](https://josm.openstreetmap.de/). We can see things like
speed, direction, and sometimes, elevation, if it is recorded. See
those red segments of the line? Those are traffic lights!


OSM has an [API](https://wiki.openstreetmap.org/wiki/API_v0.6) and
[Planet GPS extract](https://wiki.openstreetmap.org/wiki/Planet.gpx)
available to download GPS data. The Planet GPX is rather unwieldy, and
is also very outdated (from 2013). The API is not the best either, in
that it only returns 5,000 GPS points per query, and doesn't quantify
the total pages of results, so that you can't really tell with one
query how many points you need to fetch in advance. However, if you
query the API and put a page number higher than what is available, it
won't return any points. So using a binary search you can find out how
many pages to extract.

For the area of Sydney, there are roughly 750 pages of results, so
that means just under 4 million GPS points. Here's a heat map
visualisation of it I made using QGIS (but JOSM also has a heat map
visualisation feature). You won't need huge supercomputers processing
the data, either.


Here's another of Manhattan, New York.


We can couple this visualisation with other OSM data such as all
public transport nodes. In this case, railway tracks, bus routes,
ferry routes, cycling routes, train stations, and bus stops are shown.
This is also created with QGIS.


There are a few pros and cons to using this GPS data. The pro is that
it's more general purpose: it's not only used by runners and cyclists,
it's used by regular people (well, GIS geeks) doing everyday things
like shopping. Unfortunately, OSM isn't that widely used, and so the
data is relatively sparse. In remote areas perhaps no-one has walked
that route, or only a few people have. So you don't get a sense of
what they're doing. The GPS data is also not processed, so you'll have
to do your own cleaning: especially in the city where GPS data goes a
bit haywire with all the tall buildings.

Have fun and happy mapping!

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