commit

app.py

@@ -6,7 +6,6 @@ from datasets import load_dataset
 import numpy as np
 
 # Load the dataset
-@gr.cache_examples
 def load_data():
     """Load the GVFD dataset from Hugging Face"""
     try:
@@ -334,6 +333,124 @@ with gr.Blocks(title="GVFD Navigator", theme=gr.themes.Soft()) as demo:
                 interactive=False
             )
 
+        with gr.Tab("About"):
+            gr.Markdown("""
+            # About GVFD Navigator
+
+            ## Purpose of This Tool
+
+            The **GVFD Navigator** is an interactive visualization tool designed to help researchers, analysts, policymakers,
+            and sustainability professionals explore the Global Value Factor Database (GVFD). This navigator enables you to:
+
+            - **Filter and explore** environmental and social impact value factors by country and category
+            - **Visualize patterns** in how different countries value environmental impacts
+            - **Compare regions** to identify global trends and outliers
+            - **Export and analyze** filtered data for your own research or reporting needs
+            - **Understand monetary valuations** of environmental impacts across 229 countries
+
+            This tool transforms the raw GVFD dataset into accessible, interactive visualizations that make it easier to
+            understand how environmental and social impacts translate into economic terms across different regions.
+
+            ---
+
+            ## About the Global Value Factor Database (GVFD)
+
+            ### What is the GVFD?
+
+            The **Global Value Factor Database** is a pioneering dataset developed by the [International Foundation for
+            Valuing Impacts (IFVI)](https://www.ifvi.org/) that converts non-financial environmental and social impacts
+            into standardized monetary values (US Dollars).
+
+            The database represents a groundbreaking framework for evaluating global value creation by translating
+            companies' environmental and social impacts into financial equivalents, enabling a more holistic assessment
+            of corporate and organizational performance.
+
+            ### Methodology
+
+            The GVFD uses a rigorous methodology to:
+
+            - Convert non-financial environmental and social impacts into standardized monetary values
+            - Provide value factors as multipliers to calculate monetary equivalents of impacts
+            - Standardize impact accounting across different domains and geographies
+            - Enable currency conversion for non-USD jurisdictions
+            - Support integration into financial reporting and impact accounting systems
+
+            ### Coverage
+
+            - **229 countries and territories** worldwide
+            - **205 countries with ISO codes** (89.5% coverage)
+            - **~115,000 individual measurements** across all categories
+            - **7 major world regions** represented
+            - **50 US states** included for detailed US analysis
+
+            ### Impact Categories
+
+            The GVFD covers five major environmental impact categories:
+
+            1. **Air Pollution** - Value factors for atmospheric emissions and air quality impacts
+            2. **Land Use and Conservation** - Monetary values for land use changes and conservation impacts
+            3. **Waste Generation** - Economic valuations of waste production and management
+            4. **Water Consumption** - Value factors for water use and depletion
+            5. **Water Pollution** - Monetary values for water quality degradation and contamination
+
+            ### Unique Features
+
+            - **Standardized monetary conversion** enables comparison across impact types and geographies
+            - **Comprehensive global coverage** includes nearly all countries and territories
+            - **Detailed methodological documentation** ensures transparency and reproducibility
+            - **Currency flexibility** allows conversion to local currencies for regional analysis
+            - **Integration-ready** format supports incorporation into existing impact accounting systems
+
+            ### Use Cases
+
+            The GVFD and this navigator can support:
+
+            - **Corporate sustainability reporting** - Quantify environmental impacts in financial terms
+            - **ESG analysis** - Evaluate environmental performance with monetary metrics
+            - **Policy modeling** - Assess economic costs of environmental impacts for policy decisions
+            - **Impact investing** - Evaluate and compare environmental impact of investments
+            - **AI and machine learning** - Train models on environmental impact valuations
+            - **Academic research** - Study relationships between environmental impacts and economic values
+            - **Correlation analysis** - Identify patterns in how different countries value environmental impacts
+
+            ---
+
+            ## Data Source and Attribution
+
+            **Original Data**: [IFVI Global Value Factor Database V2](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2)
+
+            **Dataset Developer**: International Foundation for Valuing Impacts (IFVI)
+
+            **Official Website**: [https://www.ifvi.org/](https://www.ifvi.org/)
+
+            **Navigator Tool**: This is an **unofficial visualization tool** created to make the GVFD more accessible.
+            For official data, methodologies, and authoritative guidance, please consult IFVI's official resources.
+
+            ---
+
+            ## Disclaimer
+
+            This navigator is an independent visualization tool and is not officially endorsed by IFVI. While every effort
+            has been made to accurately represent the data, users should refer to the original GVFD dataset and IFVI's
+            official documentation for authoritative information and methodology details.
+
+            The monetary values provided represent economic valuations of environmental impacts based on IFVI's methodology
+            and should be interpreted within the context of their methodological framework.
+
+            ---
+
+            ## Technical Details
+
+            - **Built with**: Gradio, Plotly, Pandas, Hugging Face Datasets
+            - **Data Format**: Parquet files loaded from Hugging Face Hub
+            - **Visualizations**: Interactive charts using Plotly for exploration and analysis
+            - **Filtering**: Dynamic filtering by country, category, and value ranges
+
+            For questions, feedback, or issues with this navigator tool, please visit the
+            [GitHub repository](https://huggingface.co/spaces/danielrosehill/GVFD-Navigator) or contact the tool maintainer.
+            """)
+
+
     gr.Markdown("""
     ---
     ### About the Data