Inspiration
This idea came to us when discussing daily issues such as simply going grocery shopping. The walk from our apartment complex to the grocery store is not long, but we all share similar concerns about our safety when going certain paths. We wondered, is there a way to automate this? Instead of just "trusting our gut," we can trust an app that uses numerical and quantitative analysis from crime data in certain areas and crowdsourced data from other users.
What it does
Our app's core functionality relies of a "safety score" that we culminated using criminal and integrating it with our routing feature. This allows for the user to access the safest path to walk to their destination. We have also included security features such as emergency contacts, and SOS button, and saved locations to enable a more seamless experience.
How we built it
We used Ionic, a web-app development software, and React.js to develop the front-end and back-end of our application. Ionic is unique, in the way that it uses HTML and CSS to create functional elements, which allows efficient development across different platforms such as a mobile device or website. For our data analysis, we used Python to corroborate different datasets and create functions that connect with our React.js server.
Challenges we ran into
One of our biggest challenges from the beginning is the new software we had to learn- Ionic. Some of our members had previous experience in React.js so we were able to acclimate well. We encountered many challenges when developing this app, including server-side issues, API implementation, and accessing the data that would provide our app's core functionality.
One issue in particular persisted throughout the project- connecting our Python functions with the React.js interface. We researched possible solutions using resources like Reddit and StackOverflow. We identified two possible approaches: using Flask, a Python-based framework or implementing a collection of Node.js packages. We proceeded with the latter and unfortunately encountered even more errors, specifically dealing with CORS policy. Bypassing this was the last step before securing the connection. Through multiple iterations, we were able successfully overcome this challenge and connect the necessary servers.
Accomplishments that we're proud of
For many of us. this was one of our first hackathons and didn't have many expectations coming into this challenge. When the hacking period started, we were naive and simply started coding separate features without much of a plan. However, as time went on, we realized we needed regular check-ins to make sure our project was progressing to plan. We are proud of how synergetic our team became over these 36 hours and evenly split the tasks so that the application developed at a surprising speed. Obviously, there were stressful times when certain aspects weren't working, but we never gave up. We collaborated and trusted each other's skills to put our best work forward.
What we learned
We learned how many intricacies lay in a project. At first, we had a big idea of what we wanted to develop but underestimated how many complexities we would have to create. There were many aspects we didn't expect to be as difficult as it was. This whole journey was a learning process that we thoroughly enjoyed.
What's next for SafeTrackr
We’re not stopping with crime data. Our vision includes integrating streetlight and traffic data to provide a fuller picture of safety. Because safety isn't just about crime; it’s also about visibility, movement, and awareness. And soon, you’ll have multiple route options to choose from, with each route having a customized safety score based on these combined factors.
Another exciting feature on the horizon is social connectivity. Imagine if your friends could receive an alert when you’re in a high-risk area, ensuring that your safety network extends beyond the app.
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