SHELTER MAP

Overview

SHELTER MAP is a web application designed to provide real-time assistance during natural disasters. It integrates data from multiple sources, including the OCHA database and satellite imagery, to offer accurate and timely information during emergencies.

Problem Statement

Natural disasters are unpredictable and often catch people unprepared, leaving them disconnected and vulnerable. During such crises, accessing real-time information, safe evacuation routes, and emergency services is critical for survival. The common challenges include:

• Lack of real-time disaster information from reliable sources.
• Limited access to safe routes and navigation during emergencies.
• Difficulty in reaching emergency services like police, fire, and medical support.
• Inability to report incidents anonymously, which can delay critical response.

How It Works

1. Registration & Profile Setup:

   • New users are directed to a sign-up page.
   • After successful registration, users are redirected to their profile section, which gathers basic health information to create a health card (e.g., medical conditions, allergies, emergency contacts).

2. Home Page & Panic Button:

   • Users are directed to the home page upon completing their profile.
   • The home page features a Panic Button for real-time assistance during disasters, integrating data from multiple sources like the OCHA database.
   • User's exact location (latitude and longitude coordinates) is automatically fetched and displayed on a map.

3. Safe Navigation & Incident Reporting:

   • The system uses trained ML models (GNNs, A*, and U-Net algorithms) for image processing and pathfinding to navigate users to the safest path.
   • Users can report incidents, such as accidents, fires, or hazards, anonymously on every page.
   • Real-time updates about disaster incidents and suspicious activities are available.

4. Emergency Services:

   • Quick access to Police, Fire, and Ambulance services.
   • Resource information about emergency services (e.g., contact details, nearest stations).
   • Nearby hospitals' directions and contacts are accessible through maps.

Market Impact

• Improved Public Safety: Provides real-time disaster updates, safe route navigation, and quick access to emergency services.
• Global Applicability: Usable in disaster-prone regions worldwide, adaptable to various languages and local needs.
• Government & NGO Support: Assists in coordinating faster relief efforts and efficient resource management.
• Reduced Economic Losses: Minimizes property damage and speeds up evacuations, reducing strain on emergency services.
• Scalable Solution: As climate-related disasters increase, the app can grow with more features and integrations.

Future Plans

• Predictive Disaster Alerts: Integrate AI-based models to predict disasters and send early warnings to users.
• Expanded Data Sources: Incorporate additional real-time data from local governments, NGOs, and private sector organizations.
• Multilingual Support: Enable the app to support more languages for global accessibility.
• Offline Functionality: Develop offline capabilities for safe route navigation and emergency information when internet access is unavailable.
• Community Reporting: Introduce a feature for users to report real-time hazards, traffic blockages, and safe zones during disasters.
• Integration with Wearables: Sync with smartwatches and other wearable devices for immediate alerts and health monitoring in emergencies.

Tech Stack

• Frontend: [ React, Vue.js]
• Backend: [ Django]
• Machine Learning Models: GNNs, A*, U-Net for image processing and pathfinding
• Database: [ PostgreSQL]
• APIs: OCHA database integration, real-time satellite imagery processing , NASA Satellite Imagery.

Contributing

Contributions are welcome! Please read our CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

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Developed by
Hounds of Justice

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Below are the setup instructions for running both a Django backend and a React frontend for the project.

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Prerequisites

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Python (3.8 or higher) Node.js (with npm) PostgreSQL (or any other database you're using)

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Backend: Setting Up Django Server

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Prerequisites

Before starting, ensure you have the following installed:

• Python (version 3.x)
• pip (Python package installer)
• virtualenv

Installation Steps

Step 1: Clone the Repository

Clone the Django application repository to your local machine using the following command:

git clone https://github.com/Marketscope-Dev/Resume-Parser-Data-Entry

Step 2: Navigate to the Project Directory

Change into the directory of the cloned repository:

cd Resume-Parser-Data-Entry

Step 3: Create a Virtual Environment

Create a virtual environment using virtualenv:

python -m venv my_env

pip install virtualenv

This will create a virtual environment named my_env.

Step 4: Activate the Virtual Environment

Activate the virtual environment using the following command:

• On Windows:

  my_env\Scripts\activate

• On macOS and Linux:

  source venv/bin/activate

Step 5: Install Dependencies

With the virtual environment activated, install the required dependencies using pip:

pip install -r requirement.txt

This will install all the dependencies listed in the full-requirements.txt file.

Step 6: Run Migrations

Apply the database migrations:

python manage.py migrate

Step 7: Start the Development Server

Run the development server to start the application:

python manage.py runserver

Access the application at http://127.0.0.1:8000/ in your web browser.

Deactivating the Virtual Environment

To deactivate the virtual environment, simply run:

deactivate

Additional Notes

• Ensure you have the latest version of pip by running pip install --upgrade pip.
• If you encounter any issues during the installation process, check the error messages for guidance and ensure all prerequisites are met.

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Frontend: Setting Up React Server

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Step 1: Navigate to the frontend directory

cd frontend-react

Step 2: Install required packages

npm install

Step 3: Start the React development server

npm start

The React server will be running at http://localhost:3000/

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