🚨 AI-Powered Crime Hotspot Prediction & Reporting Platform

📌 Overview

This project is an AI-driven platform that allows users to anonymously report harassment cases while utilizing Machine Learning to predict potential crime hotspots. By combining crowdsourced data and AI-based analysis, it helps authorities and the public identify high-risk areas and take preventive measures.

🔥 Key Features

✅ 1. Anonymous & Secure Reporting

• Users can report harassment cases without revealing their identity.
• The reports include location, type of harassment, and description.

🌍 2. Real-Time Crime Mapping

• A dynamic map displays reported harassment cases as red blurry markers.
• More reports = Higher intensity, helping visualize crime-prone areas.

🤖 3. AI-Powered Hotspot Prediction

• Uses Kernel Density Estimation (KDE) to predict the next potential crime hotspot.
• The predicted hotspot is marked in green, with intensity decreasing from the most severe to least severe.

📈 4. Data-Driven Insights

• A curved graph dynamically updates to show crime trends across different locations.
• The system highlights the area with the most reports in real-time.

🔍 5. Crowdsourced Data Collection

• Community-driven data collection improves predictive accuracy.
• Allows law enforcement and citizens to stay informed about crime patterns.

⚡ 6. Scalability & Future Expansion

• Designed to handle large-scale data entries.
• Initially, a CSV dataset with 10,000 reports will populate the database for AI model training.
• After reaching this milestone, reports will be manually submitted by users.

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🛠 Tech Stack

Backend 🖥️

• Django (Python) for server-side processing.
• SQLite3 for managing reports and predictions.

Frontend 🐥

• HTML, JavaScript, Bulma CSS for an intuitive user experience.
• Leaflet.js & OpenStreetMap API for map visualization.

Machine Learning & AI 🤖

• Kernel Density Estimation (KDE) for hotspot prediction.
• Pandas & NumPy for data processing.

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🔄 How It Works

1️⃣ Report Submission

• Users enter location, type of harassment, and description.
• The system automatically fetches latitude & longitude based on the location name.
• The report is stored securely in the database.

2️⃣ Real-Time Visualization

• A map dynamically updates to reflect new reports.
• Crime hotspots are predicted using AI, ensuring proactive safety measures.

3️⃣ Prediction Mechanism

• The AI model analyzes historical crime data.
• KDE determines the most likely future crime locations.
• The top 20 hotspots are displayed with decreasing intensity (green border with blurred green inside).

4️⃣ Prediction Mechanism

• Community commenting feature to discuss about the crimes.

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📂 Setting Up the Project

🔧 1. Clone the Repository

    git clone https://github.com/yourusername/Report-Connect.git
    cd Report-Connect

📦 2. Install Dependencies

    pip install -r requirements.txt

🔥 3. Run Migrations & Start Server

    python manage.py migrate
    python manage.py runserver

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🚀 Future Enhancements

• Advanced AI Models: Improve prediction accuracy with Deep Learning.
• User Alerts: Send notifications when users are in high-risk zones.
• Integration with Law Enforcement: Provide verified data to help authorities take action.
• Multilingual Support: Expand accessibility to a wider audience.

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🏆 Impact & Goals

• Empower communities to report incidents without fear.
• Assist law enforcement in focusing efforts on high-risk areas.
• Utilize AI & Big Data for proactive crime prevention.

🫂 Together, we can make cities safer! 🔥