SheSafe : A Women Helpline AI Assistant
A women's safety solution using crime data for risk analysis and emergency support, powered by Llama AI, Leaflet maps, and a CrimeSafetyAnalyzer for proactive safety insights.
Created on 26th October 2024
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SheSafe : A Women Helpline AI Assistant
A women's safety solution using crime data for risk analysis and emergency support, powered by Llama AI, Leaflet maps, and a CrimeSafetyAnalyzer for proactive safety insights.
What’s your problem statement?
An AI-powered system can significantly improve public safety by analyzing crime data to predict high-risk areas, offering real-time alerts, and providing emergency assistance through chatbots. By mapping crime patterns and integrating with location tracking, AI can help users navigate safely and alert authorities during emergencies. Additionally, AI can use crowdsourced and social media data to update safety insights dynamically, creating a responsive safety network. With data-driven guidance for policymakers, this approach not only empowers individuals but also strengthens community and law enforcement responses to reduce crimes against women.
The problem SheSafe : A Women Helpline AI Assistant solves
This AI-powered system addresses the critical need for safer public spaces, especially for women, by offering real-time safety insights and preventive support. Users can access location-based safety scores, receive alerts when entering high-risk zones, and get guidance on safe routes, making navigation safer and informed. Emergency chatbots provide instant assistance, from sending distress alerts to connecting with nearby emergency contacts. Additionally, law enforcement and city planners benefit from data-backed insights on crime trends, enabling targeted safety measures. This system simplifies staying safe in public spaces, providing users with confidence and authorities with actionable insights
Challenges we ran into
During the development of the project, one significant challenge I faced was integrating real-time data with the crime analysis features. Initially, the system struggled to process and display updated crime statistics efficiently, leading to delays in user alerts. To overcome this, I implemented a more robust data handling mechanism using asynchronous programming with Promises in JavaScript, which allowed for smoother data fetching and updates. Additionally, I optimized the database queries for faster access to crime records. This not only improved the system's responsiveness but also enhanced the overall user experience by ensuring timely and accurate safety insights.
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