GwinNETTwork: Week 3

It’s been another fun week for us. The first couple of days we got some insight into the fields of data science and machine learning research at the Machine Learning in Science and Engineering conference, where we got to go to many talks from professors and students at universities around the country. In addition, we were also able to mingle and network with some graduate students, professors, and even industry professionals during poster sessions in the afternoon.

Even while attending the MLSE conference, we were still able to have a productive week. We met with Dr. Guin again, where we went over several things including:

  • With the webpage with the Leaflet.js heatmap getting closer to completion, we went over how to use the Windows Remote Desktops that could be used to SSH/SCP into the Linux box that would be used to host the heatmap
  • Improved the filtering by getting rid of firetruck location based off of the speed. The threshold of getting rid of the firetruck location was if the firetruck was moving at a speed less than 7 minutes per hour for more than a period of 4 minutes. We understand that the threshold might change as new methods of filtering get introduced over time.

This is a sample of the heat map we have completed:

As a result of the filtering, this is the image we were able to come up with in QGIS 3:

From the above mapping, the blue and red dots represent firetruck locations before the filtered was applied, but only the blue dots remained after the filtering script was applied.

We are nearing completion of producing initial visualizations of one fire station’s set of data. Next week, we will start rolling this process out to the other 15+ sensors among 6 fire stations along the Peachtree Industrial Boulevard corridor and upload this heat map onto the website! Afterwards, we will hopefully start collaborating with other researchers on this project and connect the fire truck data with the traffic light/sensor data in order to eventually optimize routes for emergency vehicles in the future.

A challenge that we continue to face is QGIS being slow, unreliable, or crashing at random times, causing unnecessary frustration. As far as the data points themselves go, we are still working on edge cases for filtering out the irrelevant points. Additionally, we are also wondering if and how it might be possible to improve upon the current Leaflet.js heat map that we have.