The City of Syracuse is one of the snowiest cities on record within the United States. Given this fact, it was deemed important that the residents of Syracuse should be able to see when their streets were plowed. During snow storms, the City of Syracuse sees an increased number of calls from residents telling us a street was not plowed or inquiring when their street was last plowed. By having a tool like the snow plow map we can be more transparent and open about our operations, giving residents insight into our answers. Also, a snow plow map allows our departments to see where we have and have not yet plowed, so we could go plow a street if it were missed for one reason or another.
Earlier in 2018, the Office of Accountability Performance and Innovation held a Civic Hackathon in partnership with Syracuse University’s iSchool to find insights on plow data. This project was a continuation of the mission of that hackathon to tackle our snow problem. The City of Syracuse’s snow plow mapping application is an in-house project, created by me (Edward Deaver, IV), a computer science intern with the City.
Snow plow mapping applications have been implemented by other municipalities within the region, and throughout the country:
Rochester, New York: https://gis.cityofrochester.gov/plowtrax/
New York City, New York: http://maps.nyc.gov/snow/#
Before using a potentially expensive off-the-shelf commercial solution, we wanted to explore the potential of building a product ourselves. Going this route allowed experimentation and familiarization with cloud computing services that have become an integral part of technology infrastructure in the 21st century. If in the future a commercial product were to arise that had economic and feature benefits it would be considered.
Over the course of the latter half of the 2018 I proposed a system to tackle this challenge. It would talk to tracking devices on our snow plows, understand where they are, and relay that information to a website so anyone could see when their street was plowed. The idea was simple, but the execution of it was complex.
Initial challenges that I faced were spotty documentation to get the snow plow location data, how reasonably fast can this information be updated, and how do numbers (GPS coordinates) translate to street names. For a more in-depth look at these challenges, please see the technical version of this blog post.
The documentation issue was solved through discussions with our vehicle tracking vendor, but the others were not so easy.
The speed at which the information updated posed a couple of issues:
If the information updated really fast, my program would need to obtain information and pass it around a lot faster and more efficiently. Also, all of the services my program depended on would be placed at an increased strain.
If the program updated slowly, this may not reduce Cityline calls because the website would say your street wasn’t plowed when it was.
Translating where our snow plows were to street names posed a larger issue (this is discussed in depth in the technical version of this blog post). The system needed four parts to work together and each of them needed to run in a specific order for the system to work.
This system that I proposed had a lot of moving parts - the more moving parts the higher the complexity - and this reached a point that I was no longer able to complete tying together these parts. This ended iteration 1 of the project. For iteration 2, I presented my work to Sam Edelstein, the City’s Chief Data Officer, and, using my work and lessons learned from it, he was able to create a simpler solution to the problem.
The snow plow map can be found at: http://www.syracuse.ny.us/snowplowmap.html