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About Laravel

Laravel is a web application framework with expressive, elegant syntax. We believe development must be an enjoyable and creative experience to be truly fulfilling. Laravel takes the pain out of development by easing common tasks used in many web projects, such as:

• Simple, fast routing engine.
• Powerful dependency injection container.
• Multiple back-ends for session and cache storage.
• Expressive, intuitive database ORM.
• Database agnostic schema migrations.
• Robust background job processing.
• Real-time event broadcasting.

Laravel is accessible, powerful, and provides tools required for large, robust applications.

Learning Laravel

Laravel has the most extensive and thorough documentation and video tutorial library of all modern web application frameworks, making it a breeze to get started with the framework.

You may also try the Laravel Bootcamp, where you will be guided through building a modern Laravel application from scratch.

If you don't feel like reading, Laracasts can help. Laracasts contains thousands of video tutorials on a range of topics including Laravel, modern PHP, unit testing, and JavaScript. Boost your skills by digging into our comprehensive video library.

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Premium Partners

• Vehikl
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• Kirschbaum Development Group
• 64 Robots
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• DevSquad
• Redberry
• Active Logic

Contributing

Thank you for considering contributing to the Laravel framework! The contribution guide can be found in the Laravel documentation.

Code of Conduct

In order to ensure that the Laravel community is welcoming to all, please review and abide by the Code of Conduct.

Security Vulnerabilities

If you discover a security vulnerability within Laravel, please send an e-mail to Taylor Otwell via taylor@laravel.com. All security vulnerabilities will be promptly addressed.

License

The Laravel framework is open-sourced software licensed under the MIT license.

CrimeMap Intelligence Dashboard

Description: This project is a full-stack GIS crime mapping and surveillance dashboard that integrates a frontend dashboard, a Laravel backend with MySQL database, and a Python-based ML service for classification and geolocation. The system visualizes real-time crime locations, detects crime types using ML, and displays nearby police station data for operational awareness.

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1. Technologies Used:

Frontend:

• Vue 3 with Vite
• Leaflet.js
• leaflet.markercluster
• Axios

Backend:

• Laravel (REST API)
• MySQL (Relational Database)

ML Server:

• Python 3.10
• FastAPI
• Hugging Face Transformers
• OpenStreetMap Overpass API (for police station geolocation)

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2. Data Flow:

Step 1: Data Collection

• Crime incidents are collected from RSS feeds and social media using the ML server (FastAPI).
• Each entry is classified by an ML model to determine if it is crime-related.

Step 2: Classification & Location Extraction (ML)

• The ML model uses a multilingual BERT (e.g., xlm-roberta) fine-tuned on Sinhala/Tamil crime news to classify the title.
• If classified as crime, a location extraction module (NER + geocoder) estimates coordinates.
• Output:
  • title, description, category, latitude, longitude

Step 3: Data Insertion (Laravel + MySQL)

• The Laravel API periodically pulls data from the ML server (/get-latest-crimes).
• A Laravel scheduled command (php artisan fetch:crimes) stores or updates records in the MySQL crimes table.

MySQL Table Structure (crimes):

• id (int)
• title (varchar)
• description (text)
• category (varchar)
• latitude (double)
• longitude (double)
• occurred_at (datetime)
• source_url (text)
• created_at (timestamp)
• updated_at (timestamp)

Step 4: Frontend Display (Vue + Leaflet)

• The Vue dashboard fetches live data from http://localhost:8001/api/crimes.
• The main map clusters all crimes and supports popup interactions.
• On marker click, the police response mini-map zooms to the nearest police station.
• A polyline is drawn between the crime location and the selected station.

Step 5: Police Station Data (OpenStreetMap)

• The dashboard loads real police station POIs using Overpass API.
• These are shown on the right-hand mini-map for live reference.

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3. ML Model Pipeline:

Preprocessing:

• Sinhala and Tamil crime news dataset manually labeled (crime / not crime).
• Tokenized using xlm-roberta tokenizer.

Model:

• Base model: xlm-roberta-base
• Fine-tuned using Hugging Face Trainer on classification task.

Training:

• Train/test split: 80/20
• Optimizer: AdamW
• Epochs: 4
• Accuracy: ~92% on validation

Location Extraction:

• Named Entity Recognition (NER) detects city/place names.
• OpenCage Geocoder API or rule-based bounding is used to resolve coordinates.

FastAPI Endpoint:

• /get-latest-crimes: Returns latest classified + located crime data in JSON format

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4. Setup Instructions

A. ML Server (FastAPI)

• Install Python dependencies: pip install fastapi uvicorn transformers torch feedparser requests

• Run: uvicorn main:app --reload

B. Laravel Backend

• Configure .env for MySQL
• Run migrations: php artisan migrate
• Schedule crime fetching: Add cron job for php artisan schedule:run
• API route: GET /api/crimes

C. Vue Frontend

• Install dependencies: npm install
• Start development server: npm run dev
• Access at: http://localhost:5173

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5. Future Improvements

• Add real-time WebSocket updates
• Visual analytics: heatmaps, trend charts
• User roles and secure login (admin, viewer)
• Integration with real crime datasets from local police
• Severity-based color markers
• Multi-language UI

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6. Deployment Considerations

• Frontend: Host via Netlify, Vercel, or static server
• Backend API: Host Laravel on shared VPS or Laravel Vapor
• ML Server: Deploy on a Python backend (e.g., Render, DigitalOcean, or containerized)
• DB: Use managed MySQL or provisioned RDS (if hosted)

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