jayesh-rn / homelessness-prevention

Our project aims to create a data-driven platform for preventing homelessness. By analyzing economic factors, health determinants, and eviction trends, we'll predict individuals at risk.

Hacksparrow - A SOLUTION TO TRACK AND HELP HOMELESS PEOPLE

PROJECT DESCRIPTION

APPROACH

Slums are generally characterized by extremely high population densities, irregular arrangement of small buildings, and poor living conditions. These areas often harbor homeless populations. Our approach involves:

1. Identification of Slum Areas: We identify areas with extensive slum presence, such as Dharavi, one of the largest slums. This is done through surveys and user inputs.

2. Location Mapping: Collected data is marked on maps to track slum areas.

3. Machine Learning Integration: Using satellite imagery (Sentinel) and machine learning, we train models to predict probable slum locations based on collected data.

4. Validation and Tracking: Probable locations are validated against user inputs and surveys. This helps in tracking homeless populations effectively.

5. Additional Features: We have incorporated features such as:

   • User-input location storage in a database.
   • Contacting nearby NGOs.
   • Donation options via QR codes.
   • Reporting suspicious activities like kidnapping, smuggling, or rape cases.
   • Organizing and attending events like free health checkups, treatments, food distribution, and educational sessions.

FILE DESCRIPTION

• Mumbai_details.html: Details of slums or homeless populations displayed using various maps (Google map, Satellite map, and Machine Learning classified map).
• Support-us.html: Allows users to donate for homeless people using QR codes.
• contactauthorities.html: Allows users to contact managing authorities.
• contactNGO.html: Allows users to contact nearby NGOs.
• index.html: Home page.
• contribute.html: Allows users to contribute the location of homeless people stored in a database.
• file_report.html: Allows users to report any deformities or suspicious activities related to homeless people shown on the map.
• index-logout.html: Allows users to logout from the website.
• Login.css: Stylesheet for the login page.
• login.html: Login page.
• mission.html: Describes the mission of the project.
• signup2.css: Stylesheet for the signup page.
• signup2.html: Signup page.
• package.json: Stores important metadata about the project.
• feature_collection.js: Dataset for ML classified map to be imported in Google Earth Engine.
• ml_map_gee.js: Machine learning code used in Google Earth Engine editor to predict probable slum areas.
• github_repo_turing_crypt.txt: GitHub repository link.

SETTING UP THE PROJECT

Requirements

For development, you will only need Node.js and a node global package, Yarn, installed in your environement.

Node

• Node installation on Windows

  Just go on official Node.js website and download the installer. Also, be sure to have git available in your PATH, npm might need it (You can find git here).

• Node installation on Ubuntu

  You can install node.js and npm easily with apt install, just run the following commands.

  $ sudo apt install nodejs
  $ sudo apt install npm
  

• Other Operating Systems

  You can find more information about the installation on the official Node.js website and the official NPM website.

If the installation was successful, you should be able to run the following command.

$ node --version
v8.11.3

$ npm --version
6.1.0

If you need to update npm, you can make it using npm! Cool right? After running the following command, just open again the command line and be happy.

$ npm install npm -g

Authors

• @Jayesh Pandey
• @Arpitshivam Pandey
• @Abhay Maurya

Contributing

Contributions are always welcome!

Tech Stack

Client: HTML, CSS, JAVASCRIPT

Server: NodeJS, MongoDB

MACHINE LEARNING: Pre-processing, Multiclass Classification using Random Forest, NLTK, PyTorch, ReLU

Dashboard: PowerBI