Inspiration
Longer ambulance response times and trips lead to more preventable deaths. Positioning ambulances in a better more efficient way can reduce the average response time of ambulances in any given city saving more lives.
What it does
Uses a mixture of population density, past emergency call history, and current traffic data to calculate the most optimal placement of x amount of ambulances to minimize average response time.
How we built it
Using Gemini API to analyze the data and return the results, we used mock data to simulate emergency call history and population density while using Google Maps API to track real time traffic.
Challenges we ran into
Connecting all the APIs and code portions together since we initially used multiple languages and file types which did not work together easily.
Accomplishments that we're proud of
Having a working API that displays the optimal positions to place an ambulance given data. A heatmap which displays a simulation of call history density and locations.
What we learned
How to connect various APIs, how to combine multiple file types to work together, best practices for git
What's next for RapidReach
Improving the mock data, with real world data. Enhancing AI analysis and adding more scalability and things to consider while placing ambulances.
Built With
- css
- flask
- gemini
- geminiapi
- html
- javascript
- maps-javascript-api
- python
- react
- routesapi
- typescript
- vite
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