Inspiration
Time differences of even just a few seconds on the scene of a fire can make the difference between life and death.
What it does
Google Maps API is queried with a user provided address and the resulting coordinates are compared with our dataset to find a handful of the closest hydrants.
How we built it
Python backend interfaced with through Flask. This backend does all interfacing with the Google's APIs. Webpage with JQuery and JS to communicate with the Flask server.
Challenges we ran into
- Defining an easily mutable and traversable data structure.
- First experience as a team with Flask and JQuery/Asynch JS calls.
Accomplishments that we're proud of
- Accurately calculating closest hydrant
- Styling of the Webpage
- Presentation of results of calculation
What we learned
- Python can provide a very powerful and robust backend.
- How to accurately determine distances on a spherical surface.
- Reinforced appreciation for CSS precompilers.
What's next for HydraView
- Further expansion of independantly acquired data points to provide better coverage.
- Aid in prevention of lost pressure at larger scale fires.
- Keeping a more accurate record of the condition of hydrants.
- Acquire a domain
HydraView
A PhillyCodeFest2018 Project
Built With
- Python
- Flask
- SASS
- HTML5
- Google Maps Geo-Coding API
- Amazon Web Services
Contributors:
- John Zlotek
- Dakota Wessel
- Joseph Hines
Elevator Pitch
In the event of an emergency, HydraView provides first responders with the location of the nearest fire hydrant.
Challenge
Get the most accurate information to responders. Create an application that asks a user for their current location. Use this current location to find the nearest fire hydrant that is in service, display any critical notes that may be available.
Existing systems
American Water provided us with a JSON file that contained information about various groups of fire hydrants within New Jersey. The file gave us the latitude and longitude, wether the hydrant was operational or not, and if the hydrant needs to be upgraded.
Business Case
In this competition, we were provided a json file containing all the data points; each data point has the respective categories:
- Lat
- Lng
- OutOfService
- Critical
- CriticalNotes
With this data set we want to provide first responders with an application that will direct them to the most optimal fire hydrant for the location that they are heading towards potentially recovering additional live saving seconds.
Our Approach
Our web application currently takes the given data set which persists of ~2500 fire hydrants in the NJ area. The user is asked for the address of the fire when an address is inputted into our API, we make HTTP GET calls to our Flask server with the address of the fire as a query parameter. We return the address's decimal degree coordinates and a list of the closest, n fire hydrants to that address (another query parameter).
An example query: http://ec2-52-207-219-213.compute-1.amazonaws.com/getClosestHydrants?address=Address&num=X Address is the address, X is the number of hydrants to find
A map will then be displayed for the user with fire hydrant pins that user can select.
Future improvements include implementing a quad tree as a way of storing data. This would allow the application to scale and still have quick lookups regardless of the data size.
Conclusion
In conclusion, we believe that by utilizing the provided data we can effectively provide an unique solution for first responders and water departments to view fire hydrants and maintain their conditions. By doing so, they can view critical notes, see any currently in service, or view them on the fly for emergencies. Overall, we believe our solution is what should be adopted to increase the effectiveness of our first responders.
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