SafeSpace

Inspiration

SafeSpace is a community-driven safety app developed using the MERN stack, inspired by personal experiences in dangerous areas of Kochi.

What it does

The app aims to empower women and provide safety tools, creating a network of support in potentially risky situations. While facing challenges in implementing advanced features like shake-to-call and offline location sharing, SafeSpace successfully establishes a platform for users to connect and assist each other.

How we built it

We built it using MERN Stack (MongoDB, Express.js, React, and Node.js).

Challenges we ran into

We were unable to implement some features like shake and call, sending location to nearby phones without network, processes that required client side scripting.

Accomplishments that we're proud of

The project team is proud of their progress and looks forward to expanding the app's capabilities. We successfully created a functional platform that allows users to connect and support each other in potentially dangerous situations. Our app provides a user-friendly interface for reporting and responding to safety concerns within the community.

What we learned

We learned valuable lessons about full-stack development using the MERN stack, including handling real-time data updates and user authentication. The project also deepened our understanding of the complexities involved in creating safety-oriented applications, including the importance of user privacy and data security. We gained insights into the social aspects of community-driven safety initiatives and the potential impact of technology in addressing real-world problems.

What's next for SafeSpace

Our immediate next step is registering the domain "Safe-Space.Co" on GoDaddy. Looking ahead, we plan to:

Implement the shake-to-call feature and improve offline functionality. Expand our user base and gather feedback for further improvements. Collaborate with local law enforcement and women's safety organizations to enhance the app's effectiveness. Develop a mobile version of the app for both iOS and Android platforms. Implement machine learning algorithms to predict and prevent potential safety threats.

Built With

• mern