SafeZone

heatmap
report page
logo
homepage

Inspiration

As a minority living in America I personally noticed social injustice in the means of racism. I felt that there had to be some way to combat social injustice using technology yet I couldn't find an app that did this. When I joined this hackathon my mind immediately went back to this problem and soon after SafeZone was born. Born out of necessity and determination to create change.

What it does

SafeZone is an app that addresses a critical need in our communities. Each year, countless people face social injustice without a means to share their experiences. Our platform provides them with a voice. Through SafeZone, users can report these incidents and foster a safer community. SafeZone generates detailed geographic heat maps from user reports using a clustering algorithm. The user reports are analyzed by an AI Language model to categorize them based on the type of event and find the severity of the event. Community members can see different heatmaps, the actual reports submitted by the user, and the AI based analysis. This sheds light on issues within local communities relating to racism, sexism, assault, etc. SafeZone’s goal as an app is to make communities safer by creating awareness about issues that are often never talked about. Each report leads to a safer community, a SafeZone community.

How we built it

We used react native and node.js to code the app. For the app styling we utilized css. The login/sign up page is made using firebase authentication. All of the report data gets stored in the firestore database. We also used a large language AI model to find patterns in this data. To make the google maps heat zone visual to raise awareness we use the google maps api and javascript.

Challenges we ran into

One of the largest technical difficulties we faced was creating our HeatMap system. In order to get the app working to its fullest extent, we had to store our reports in a database, feed them into our AI model, and place them into a map with a clustering algorithm using the Google Maps API. Our main challenge was taking all of these different technologies and combining them into 1 cohesive app. Another challenge came with properly rendering the heatmaps, where we had to make a clustering algorithm, which combined heat zones in proximity to each other. To overcome these challenges, we spent weeks coding, experimenting and debugging. It only goes to show that nothing is impossible with determination.

Accomplishments that we're proud of

We are proud of getting the AI to work and classify/find trends in report data. This was my first time ever interacting with AI so getting it to work is something I am proud of. The heat map and clustering system also work really smoothly which adds a lot to the user experience. I am also happy with how the app ended up looking, css is not a strong suit of mine but I think SafeZone turned out pretty nice. All in all I am proud of how I incorporated so many different technologies to make 1 cohesive app that helps local communities by combating social injustice.

What we learned

We learned the ins and outs of different apis, for instance the google maps api. We also learned how to set up our own database with firebase/firestore. Furthermore we learned a lot about how Large Language AI models work and how you incorporate them into javascript code. All in all we learned a lot, but the most important thing we learned is troubleshooting. I can't even talk about the amount of times we hit roadblocks where we got errors. But by persevering, watching tutorials, and by reading documentation we got a finished app that worked. As corny as it sounds we learned a programmers most important tool is not giving up.

What's next for SafeZone

One thing to make this app more useful for users would be to add data from police and crime databases to our heatmap. Currently, we only have user report but by adding crime reports to SafeZone 2.0 we can get one step closer to creating a safer community. We would also create a profile system in SafeZone 2.0, which would help make reports more credible. Through this profile system, we could also limit the number of daily reports an account can send to prevent bots from spamming fake reports. To make this user profile system we would combine components of other user profile systems from apps like Uber and Instagram.