Civic Connect is a modern mobile application designed to streamline the process of reporting civic issues and providing suggestions to local authorities. By leveraging machine learning and intelligent categorization, the app makes it easier for citizens to accurately report problems while reducing the workload for government agencies through better classification.
- AI-Powered Categorization: Automatically suggests relevant categories based on issue descriptions
- Location-Aware Recommendations: Adjusts category suggestions based on user's location (e.g., near parks or public transport)
- Learning System: Improves recommendations based on community usage patterns
- Related Categories: Suggests alternative categories that might be relevant
- Visual Category Selection: Clear icons and minimal text for intuitive navigation
- Step-by-Step Guidance: Logical progression through the reporting process
- Category Examples: Shows real examples of what belongs in each category
- Visual Cues: Highlights recommended categories to speed up selection
- Multi-step Process: Category → Location → Details → Review
- Image Attachments: Include up to 3 photos with each report
- Location Detection: Automatic location detection with manual adjustment option
- Offline Support: Create and save submissions when offline
- Anonymous or Account-based: Submit anonymously or create an account for tracking
- Submission History: View past reports and their status
- Feedback System: Rate municipal responses and provide additional information
- Frontend: React Native / Expo
- State Management: Zustand
- Machine Learning: TensorFlow.js
- UI Components: Custom components with accessibility support
- Icons: Lucide React Native
- Location Services: Expo Location
- Node.js (v14 or later)
- npm or yarn
- Expo CLI
- iOS/Android device or emulator
- Clone the repository:
git clone https://github.com/your-username/civic-connect.git
cd civic-connect- Install dependencies:
npm install
# or
yarn install- Start the development server:
npx expo start- Run on your device:
- Scan the QR code with Expo Go (Android) or Camera app (iOS)
- Or press 'a' for Android emulator or 'i' for iOS simulator
-
Category Selection:
- Type your issue in the search field for smart suggestions
- Browse categories, with recommended options highlighted
- Select the most appropriate category for your issue
-
Location Information:
- Use current location or enter an address manually
- Adjust the pin on the map for precise location
-
Issue Details:
- Add a concise title and detailed description
- Attach up to 3 relevant photos
- Use voice-to-text for hands-free input (where supported)
-
Review & Submit:
- Review all provided information
- Submit the report to the relevant authority
- View all your submitted reports
- Check status updates and responses
- Add additional information when requested
- Provide feedback on resolution
The application uses a modular architecture with the following components:
- UI Components: Reusable, accessible interface elements
- Store: State management with Zustand for reactive updates
- ML Service: Machine learning classification service
- Navigation: Expo Router for screen management
- Mocks: Sample data for development and testing
The ML classification system uses a combination of techniques:
- Text-based classification using keyword analysis
- Location-aware suggestions using geospatial context
- User history tracking to improve recommendations
- Multi-factor weighting to provide the most relevant suggestions
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Image recognition for automatic category suggestion
- Real-time duplicate detection of nearby reports
- Integration with municipal 311 systems
- Enhanced offline capabilities with background sync
- Gamification elements to encourage civic participation
This project is licensed under the MIT License - see the LICENSE file for details.
- Local government partners for providing category structure
- Community beta testers for valuable feedback
- Open source libraries and frameworks that made this project possible
Made with ❤️ for better communities