Seeing Like A Bike: Week 8

Over the last week we have made a large amount of progress! Last Tuesday we did 8 test runs around piedmont park and got full coverage from one of our sensors on every run and coverage on 5 out of 8 runs with the other sensor. From there, the sensors have only gotten more reliable, and we have stopped getting empty readings at points throughout the data. With the sensors acting more predictably and the GRIMM back we have been focusing this week on collecting and analyzing data. In addition, we have added a second route, this time around Georgia Tech so as to increase the speed at which we can complete runs. The wealth of data has allowed us to begin seeing trends in how the sensors relate to each other, and given us lots of ideas for test runs to make over the coming week.

The above graph shows one of our runs at piedmont park where we had one sensor placed near the GRIMM, represented by the red line, at the front of the steel bike and one sensor on the front of the pink bike, which is represented by the blue line.  The GRIMM readings are the grey line. We were able to confirm that the spikes in the GRIMM readings are real data, and not mistakes caused by the turbulence of a bike ride. While neither sensor shows such large spikes, they are close to the GRIMM’s readings in general.

Next week will take us even closer to the end of the program and we intend to completely finish data collection before Monday so we can devote the week to data analysis and preparation for presentations!

Seeing Like A Bike Week 2

This week we have been hard at work getting our new air quality sensor up and running. We are using a PurpleAir sensor, which provides real-time particulate readings. The PurpleAir sensor uploads its data through wifi so we have it connected to a raspberry pi by an ad-hoc network so we can get mobile readings.

We ran into a few obstacles in getting the network up and running, but now that everything is connected our goal is to get the pi reading and recording the data supplied by the sensor as soon as possible. Once the sensor is up and running we will compare it’s readings to our other sensors to confirm it’s accuracy and then start running tests. Our goal is to be able to start testing as soon as possible to maximize our data set.

While the sensor has been our primary focus, we have also worked to get caught up with past research on air pollution measurement and eye tracking. We were lucky enough find several useful research papers covering topics including mobile air pollution measurement using bikes and stress measurement using eye tracking.