The unfortunate reality of domestic terrorism affects hundreds of Americans and their families every year. Oftentimes these crimes are perpetrated using firearms. We believe there is a way to alert the community of active threats with higher accuracy and lower latency, with the goal of keeping students safe when tragedies occur.

What it does

DASH works by detecting gunshots using witness mobile devices, which are then used to provide timestamped geolocation data to build a rough profile of an active shooter's location. This data is then displayed to users of the Android app via an embedded map.

How we built it

The mobile application was created in Android Studio using Kotlin and the Google Maps API. A serverless API was created with Azure Functions to handle the backend logic. We built it using Kotlin / Android, and JavaScript on Azure.

Challenges we ran into

Initial location accuracy with GPS caused a lot of issues due to compound error. Audio recognition of gunshots proved difficult due to a lack of previous experience with machine learning.

Accomplishments that we're proud of

A novel solution for determining location within a building using WiFi access points.

What we learned

Team members got to learn Kotlin / Android, the Google Maps API, as well as Azure. Brainstorming about active shooter situations broadened our perspectives regarding potential defense mechanisms and triangulation techniques.

What's next for DASH - Defensive Active Shooter Heatmap

A more sophisticated algorithm could be developed to take into account additional statistics which in turn would increase the accuracy of the location model.

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