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Vermont Livability Data Visualization App

A Streamlit web app for exploring, visualizing, and interpreting Vermont data. Users can upload one or more datasets and view tables, data summary reports, and custom plots through an interactive interface.


Installation & Setup

  1. Clone this repository:

    git clone https://github.com/your-username/vermont-livability.git
    cd vermont-livability
  2. Install the package dependencies:

    pip install -r requirements.txt
  3. Run the Streamlit app from the terminal with this command:

    streamlit run Home.py

How to Use

  1. Launch the app using this command in the terminal:

    streamlit run Home.py
  2. Upload your data files via the sidebar. Many file formats are supported.

  3. Navigate through the pages to preview, summarize, and visualize your data.


Some Features

  • Upload and preview CSV, XLSX, XLS, JSON, GEOJSON, or SAV datasets

  • ✅ Interactive data table display

  • ✅ Layered mapping with geojson files

  • ✅ Automated data profiling and summary statistics

  • Visualization for single variables and variable pairs

  • ✅ An About Page to learn more about this project


Project File Structure

/vermont-livability-app
│── Home.py               # Main Streamlit app entry point
│── utils.py              # Helper functions for file uploads
│
├── pages/    # A "pages" folder to organize the different app pages
│   ├── 1_Table Preview.py    # Interactive data grid display
│   ├── 2_Data Summary.py     # Automated data profiling and summaries
│   ├── 3_Visualize.py        # Single and dual variable plotting
│   └── 4_About.py            # App information and credits
│
│── requirements.txt      # Python dependencies
│── README.md             # Project documentation

Planned Improvements

  • Add data comparison support features (for VT jurisdictions)
  • Include geospatial analytics
  • Include data filtering options
  • Improved UX and overall app design

License

This project is open-source under the MIT License.


Credits

  • Created by Ian Sargent
  • Developed under the Open Research Community Accelerator (ORCA)
  • Built using Streamlit and Python

Enjoy & Have Fun Exploring!

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