Strategic Aadhaar Centre Placement with Night Lights and Census Data

Project Overview

This project aims to develop a data-driven approach to identify optimal locations for new Aadhaar centers in India. The project utilizes a combination of nighttime lights (NTL) data, population density data, census data, and existing Aadhaar center data to identify areas with high demand but limited accessibility.

Problem Statement

Aadhaar is a 12-digit unique identity number issued to all Indian residents. However, many residents, particularly in rural areas, face difficulties in accessing Aadhaar centers to enroll or update their information. This project seeks to address this issue by identifying optimal locations for new Aadhaar centers.

Methodology

Data Collection

• Collect NTL data, population density data, census data, and existing Aadhaar center data.

Data Preprocessing

• Preprocess the data by cleaning, transforming, and merging it into a single dataset.

Rural-Urban Settlement Map

• Create a rural-urban settlement map of India using census data.

Aadhaar Center Density Map

• Create an Aadhaar center density map of India using existing Aadhaar center data.

Algorithm Development

• Develop algorithms to identify locations for new Aadhaar centers in urban and rural areas.

Result Visualization

• Visualize the results using maps and graphs.

Algorithms

Urban Area Algorithm

• This algorithm uses NTL data, population density data, and Aadhaar center density data to identify locations for new Aadhaar centers in urban areas.

Rural Area Algorithm

• This algorithm uses the distance between a village and existing nearest Aadhaar centers to identify locations for new Aadhaar centers in rural areas.

Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, GeoPandas
• Geospatial Data Platforms: ArcGIS, Google Earth Engine, QGIS
• Data Visualization Tools: Tableau, Matplotlib, Seaborn
• Statistical Analysis Tools: R, Python's StatsModels

Dataset

• NTL Data: Nighttime lights data from NASA's Earth Observations
• Population Density Data: Population density data from the Indian Census
• Census Data: Census data from the Indian Census
• Existing Aadhaar Center Data: Existing Aadhaar center data from the Unique Identification Authority of India (UIDAI)

Getting Started

1. Clone the repository using git clone https://github.com/your-username/strategic-aadhaar-centre-placement.git
2. Install the required libraries using pip install -r requirements.txt
3. Run the Jupyter notebook using jupyter notebook
4. Open the Strategic_Aadhaar_Centre_Placement.ipynb notebook and run the cells

Contributing

Contributions are welcome! If you'd like to contribute to the project, please fork the repository and submit a pull request.

License

The project is licensed under the MIT License.

Acknowledgments

The project uses data from the following sources:

• NASA's Earth Observations
• Indian Census
• Unique Identification Authority of India (UIDAI)

The project also uses libraries and tools from the following sources:

• Pandas
• NumPy
• GeoPandas
• ArcGIS
• Google Earth Engine
• QGIS
• Tableau
• Matplotlib
• Seaborn
• Python's StatsModels