SmartBeacon

Inspiration

There is a great need for real-time updates about issues in the community.

Currently, emergency numbers are non-discriminatory-- there is 1 clogged pipeline for many diverse problems. This results in massive inefficiency despite the existence of various agencies tailored to handle citizen's specific problems. The untimely response from uninformed government agencies creates a disconnect between the citizens of the community and the local governments created to help them-- a problem our team aspired to solve.

What it does

The application auto-classifies citizen reports about community issues to respective local government agencies.

The program scans user inputted text for specific words relating to different local government agencies and statistics are created to inform the user and government of the issue that was reported.

How we built it

We used python to develop the user's 'response classification' algorithm. A random report generator simulates the various responses the platform will receive once integrated into communities. The program records the number of responses in the correct classification and displays that information.

Challenges we ran into

Accurately classifying user-inputted responses, receiving status reports, lack of experience

Accomplishments that we're proud of

Successful classification of user-inputted responses and output of results

What we learned

How to use python, pandas, geolocation, niche methods, object-oriented programming, GUI's

What's next for smartBeacon

Graphs to show statistics, machine learning for the agency aspect of the application, UX design to create a better user interface

Built With

• python