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Sensor mapping

Sensors are everywhere nowadays, but how do you cope with this massive stream of information? Usually a (big)data analytics approach is used, which is great. Location however, is often forgotten. That’s a shame! Using a location based approach helps putting the sensor data into context and opens the possibility to combine different (sensor)data streams to end up with data that is useful and can be applied in research, business intelligence and other projects. Let us give two examples:

Incas3 sensor mapping

Together with leading Sensor company Incas3 we map sensors at the Zernike Campus in Groningen. Temperature and humidity are measured and presented on a map which gives immediate insight in the data. By combining sensordata with their spatial context the relation between urban vegetation, buildings, roads and the microclimate is established experimentally. This is valuable input for the improvement of the living environment.

App for fieldwork

Fieldwork requires on the spot information and the ability to process information. We use collector apps to do this job. On a tablet or smartphone there is always access to data and maps and new data is directly synchronising with our database. You can even work offline and synchronise your entries later. The app is customizable for specific needs.

Real time rental bikes mapping

The NS (Dutch Railways) rents out a lot of bikes (ov-fietsen) at their stations. In their app you can see how many bikes are available for every station, but that doesn't give you a quick insight in the available bikes per city for example. Putting the stations on a map, with the amount of available bikes doesn't only look nice, but also helps with that insight. The amount of bikes for every 5 minutes is stored in a database with a timestamp creates an interesting dataset for future analysis.

Last modified:22 May 2017 4.09 p.m.
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