Bike & Walk City Maps

By Roger Fischer

The Goal

The Goal is to create a time based bike and walk map for your city. The nodes are easily identifiable places in your city, so that orientation is effortless.

Bike Durations within Paris

The Tool

We want to do this scientifically, so we need a tool that records the time as well as all GPS data points. In case of big differences between some people, we can then compare the segments between two nodes.

Download Posmo Segments for iOS or Android to record your segments.

After saving a segment on your device, go to https://posmo.datamap.io/posmo-segments to see the result. You find your start and stop times, the duration and the km traveled.

Werdinsel — Hardturm (VBZ)

The Map

Here is a first draft for the city of Zurich, Switzerland. Start with a draft and refine later, if some distances are too long.

The Data

Here is an early work in progress of bike data for Zurich (be aware that we will soon switch to the tidy format, explained below):

posmocoop/spatial_future

After some discussions at the Posmo Cooperative, we finally came to the conclusion to use the tidy data standard.

In tidy data:

Each variable forms a column.

Each observation forms a row.

Each type of observational unit forms a table.

Here is an example of how that would look like for 4 walking segments.

Walking City Map Data

data_producer: the person who recorded the data with Posmo Segments.
transport_mode: e.g. walk, bike, e-bike
transport_mode_id: 1 for Walk, 3 for Bike, 4 for E-Bike
origin: defined place, e.g. HB Zurich (main station)
origin_time: time when you leave the origin
destination: defined place, e.g. Limmatplatz
destination_time: time of arrival at the destination
duration_in_seconds

You could leave out the duration, as we can compute it. However for human readability, you might also want to keep it.
For start and end timestamps, we use ISO 8601 with the UTC offset.

The Future

In the future, when many people help to provide their data, we can also use the data to figure out which route is the fastest and which is the most secure (consensus based).
In a recent discussion on Github, @tho__hug also brought up the idea to combine the data with data from the Radmesser project to get an objective measurement of danger for a given route.
Last but not least, we could, based on the best bike routes for safety and speed, create a new bike routing app for any city.

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For additional information, see also:
https://github.com/posmocoop/spatial_future