As students enrolled in the AP US Government class, we are committed to not only getting involved in our government, but making a difference within our communities. Often we see that our neighborhoods are dissatisfied with their current role in the government, feeling as if they don't have control over the decisions of their legislators. Often citizens, legislators, and experts have difficulties cooperating as they lack a platform to easily and clearly understand one another's values. We wanted to take this opportunity to use our skill and passions to make a difference in a real issue that affects our communities.

What it Does

Our application collects user information (opinions on policies, demographics, opinion on social issues). Users can form discussions over certain political issues through the Communities tab, grow closer with one another, and get more involved with their government. We then visualize this data in Tableau using GIS to make the data easier to understand. This would allow changes to happen quicker, legislators could better understand public opinions and needs, and better collaborate with professionals to make informed decisions.

How We Made It

The desktop app was built in Visual Studio using the universal windows platform (UWP). This was used to display user data, community data, and allowed users to communicate in regards to certain political issues. There was also a database behind the app which contained the user and message data. Sample data was generated using Python, which was then fed into the decision tree machine learning model, also written in Python. The trained model reads the data, and Gives a rating of the user's political ideology. The user's data and rating is then piped into our Tableau dashboards, which help us visualize and analyze the results and data. By mapping our results, we find not only patterns among users, but allow legislators to more clearly understand the values of their constituents.

Challenges (╯°□°)╯︵ ┻━┻

One of the challenges we faced was in connecting the database from SQL Server to Visual Studio, as it had an issue with the connection string. However, we later found out that the issue lied in the properties of the database where we had to enable dns for it. Also, creating the UWP app was challenging because it was slightly different than WPF in navigation, which we were more familiar with. However, in the end, we figured out the correct syntax. Feeding the data through the machine learning algorithm was challenging at first, but after more research we were able to do so successfully.

Accomplishments ( ´ ▽ ` )b

During this hackathon, we created a desktop application that connected to a database to connect users with each other to share their views on political topics and see which areas politically tilt which way. We also utilized machine learning and data visualization, something we both have a great interest in. Moreover, we were able to not only create an application, but a system for legislators and constituents to use.

What We Learned

We learned more about technologies we had a great passion in, but weren't very familiar with such as machine learning, tableau, and gis. We're glad that we chose something new, as we truly learned more in these 24 hours than we would have in a slower-paced environment.

*What's Next for Localicy?

We plan to create an Android Application, most likely in Xamarin, and create better looking UI. We'll also add functionalities such as groups, a follow system, and create systems that make it more inviting for people to meet face-to-face at events (e.g. a featured news system).

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