Disaster Management Platform

A real-time disaster relief coordination platform designed to streamline aid distribution using crowdsourced data, geolocation tracking, and AI-driven demand prediction.

🌐 Live Demo

Frontend: https://disaster-manager-frontend.onrender.com

Backend API: https://disaster-manager.onrender.com

Note: Free tier hosting - initial load may take up to 60 seconds as the server spins up from sleep mode.

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Quick Start - Run Locally

Prerequisites

• Node.js and npm installed
• Python 3.x installed
• MongoDB Atlas account

Setup & Run

1. Clone and install dependencies

   git clone https://github.com/Gotnochill/Disaster-Manager.git
   cd Disaster-Manager
   npm install
   cd frontend
   npm install
   cd ..

2. Configure environment variables

   • Create a .env file in the root directory
   • Add your MongoDB connection string:

     MONGODB_URI=your_mongodb_connection_string_here
     PORT=3000
     

3. Install Python dependencies

   pip install pymongo pandas python-dotenv dash plotly

4. Start the backend server

   npm run dev

   This initializes faker inputs to simulate crowdsourced disaster data.

5. Run the Python data importer (optional)

   python importer.py

   This starts a real-time dashboard for data analytics.

6. Start the React frontend

   cd frontend
   npm start

   If prompted to change the server port, press 'y'.

7. Access the application

   • Frontend: http://localhost:3001
   • Backend API: http://localhost:3000

The React application provides three main features:

• Dashboard: Real-time data analytics
• Live Map: Map integration with emergency service details
• Latest Alerts: Most recent emergency service inquiries

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Overview

This project is a real-time disaster relief coordination platform that enables efficient response to disasters through crowdsourced data collection and AI-driven insights.

Features

• Live Disaster Reporting: Real-time submission of disaster reports
• Geolocation Tracking: Visualization of affected areas using Leaflet.js
• AI Demand Prediction: Predicts where aid is needed most
• Volunteer & NGO Coordination: Enables efficient communication between responders
• Data Analytics: Insights from stored disaster data using Python-based Tableau integration

Tech Stack

• Frontend: React, HTML, CSS, JavaScript, Leaflet.js
• Backend: Node.js, Express.js, Faker
• Database: MongoDB (Atlas)
• Real-time Communication: Socket.io
• Data Analysis: Python, Pandas, Dash, Plotly, Tableau

API Endpoints

Get All Disaster Reports

GET /api/disasters

Retrieve all disaster reports from the database.

Submit New Disaster Report

POST /api/disasters

Request Body:

{
  "name": "John Doe",
  "type": "Disaster",
  "location": { 
    "type": "Point", 
    "coordinates": [81.6000, 21.2100] 
  },
  "severity": 3,
  "description": "Severe flooding reported in the area",
  "affectedAreas": ["Naya Raipur", "Pandri"]
}

Get Specific Disaster Report

GET /api/disasters/:id

Get details of a specific disaster report by ID.

Python-Based Tableau Integration

Extract data from MongoDB for visualization:

from pymongo import MongoClient
import pandas as pd
import os
from dotenv import load_dotenv

load_dotenv()

client = MongoClient(os.getenv('MONGODB_URI'))
db = client["test"]
collection = db["disasters"]

data = pd.DataFrame(list(collection.find()))
data.to_csv("disaster_data.csv", index=False)

Load the exported CSV into Tableau for advanced visualization and analytics.

Contributing

Contributions are welcome! Feel free to submit issues and pull requests.

License

MIT

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Code-Phoenix-25 - Built for the "Code of the Phoenix" 2025 Hackathon