📍 Location Estimation App

This is a Python application built with Streamlit to analyze and visualize mobile location data based on cell tower triangulation. It estimates the most probable state (e.g., NY or CT) a person was in during specific time intervals and calculates a confidence level for each estimate.

🔧 Features

• Upload a CSV file containing geolocation data or API URL json format
• Generate time-interval-based reports estimating the most likely state
• Display confidence percentages for each estimate
• Interactive map visualization
• Filter report by selected states
• Export results to CSV
• Download charts (line, histogram, bar) as PNG images

📊 Sample Visuals

• Time-based line charts of confidence levels
• Histograms of confidence distribution
• State frequency bar charts
• Interactive maps with estimated positions

🧪 Project Structure

├── app.py               # Main application
├── requirements.txt     # Python dependencies
├── .gitignore           # Git ignored files
├── dataset.csv          # CSV Dataset
├── dataset.json         # JSON Dataset for external API test
└── README.md            # Documentation

📝 License

• This project is licensed under the MIT License.

📦 Dependencies

• Python (3.13)
• Streamlit (>=1.32.0)
• pandas (>=2.2.0)
• matplotlib (>=3.8.0)
• altair (>=5.1.0)

📁 Input File Format

The uploaded CSV file should contain at least the following columns:

• UTCDateTime (timestamp in UTC)
• Latitude
• Longitude
• State

If you want to use API URL instead of csv upload, copy and past the URL bellow to simulate an API: https://raw.githubusercontent.com/saulostopa/location-estimation-app/refs/heads/main/dataset.json

Example rows:

UTCDateTime,Latitude,Longitude,State
2021-01-05T10:15:00Z,41.123,-73.456,NY
2021-01-05T10:30:00Z,41.124,-73.457,CT

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/saulostopa/location-estimation-app.git
cd location-estimation-app

2. Install Requirements

We recommend using a virtual environment:

python3.13 -m venv venv

source venv/bin/activate

On Windows:

venv\\Scripts\\activate

Install requirements

pip install -r requirements.txt

3. Run the App

streamlit run app.py

4. Open in Browser

• Streamlit will automatically open the application in your default browser at http://localhost:8501

5. Deploy on Streamlit Cloud

• Go to https://streamlit.io/cloud
• Log in with your GitHub account
• Click “New app”
• Select the repository and the main branch
• Set app.py as the main file
• Click “Deploy”

6. ToDo

• Periodically extract data from a PostgreSQL database.
• Transform and save the data in Redis (as JSON, lists, hashes or strings).
• Redis will serve as a temporary read source, accessible via URL or public API.