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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- climate
+pretty_name: IFVI Global Value Factors Database, Data Analysis Refactor, V2
+size_categories:
+- 100K<n<1M
+---
+
+# Data Overview: Data Analysis Refactoring V2
+
+[![Dataset](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/danielrosehill/IFVI-Global-Value-Factors-Dataset-V2)
+
+**Global Value Factors Database (GVFD)**
+**International Foundation for Valuing Impacts (IFVI)**
+Prepared by: *Daniel Rosehill*
+
+---
+
+## Introduction
+
+In late 2024, the International Foundation for Valuing Impacts (IFVI) released the **Global Value Factors Database (GVFD)**. This pioneering dataset provides a framework for converting non-traditional impacts into financial terms, offering a new lens for evaluating global value creation. For the full scope, methodology, and theoretical underpinnings, I encourage readers to consult the official IFVI website and accompanying documentation.
+
+This document presents an overview of my **independent refactoring effort**, undertaken to make the GVFD more accessible for data analysis, machine learning, and visualization workflows. The IFVI kindly granted consent for the republication of their data in this format. However, I take sole responsibility for any errors introduced in the refactoring process.
+
+The dataset continues to be governed by IFVI’s terms of use, as set out in the original publication (available at [ifvi.org](https://ifvi.org)). My work here is based on the **first release** of the GVFD. Should IFVI publish subsequent versions, they supersede the dataset refactored in this project.
+
+---
+
+## Refactoring Purpose and Approach
+
+The original GVFD was released as a single `.xlsm` (macro-enabled Excel) file. While suitable for exploratory work, this structure posed limitations for reproducible data workflows and programmatic use.
+
+My primary aim was to reformat the GVFD for **machine readability** and **workflow compatibility**, especially in contexts requiring:
+
+* **Data analysis** (e.g., using R or Python libraries such as pandas or tidyverse).
+* **Visualization** (e.g., dashboards or BI tools).
+* **Interoperability** (integration with geospatial or statistical workflows).
+
+To achieve this, the following steps were taken:
+
+1. Conversion of the original dataset to **CSV format**.
+2. Splitting of the data into **separate CSV files**, one for each value factor category.
+3. Creation of **structured JSON files**, reflecting the data’s original hierarchical presentation.
+4. Production of **compact Parquet versions**, auto-generated via Hugging Face for efficient querying and storage.
+
+---
+
+## Data Edits (Non-Substantive)
+
+No substantive changes were made to IFVI’s data values or methodologies. However, I applied several light-touch edits to improve usability and consistency:
+
+### 1. Country Standardization
+
+* Country names as listed in the original dataset were mapped to **ISO Alpha-2 codes**.
+* Both the **original names** and their **ISO equivalents** are retained in the refactored dataset.
+* Non-sovereign entities (e.g., certain territories, U.S. states) without ISO codes were omitted.
+
+### 2. Currency Formatting
+
+* The GVFD values are denominated in **U.S. dollars**.
+* To preserve numeric integrity, I removed dollar signs (`$`) from all values.
+* Currency denomination is now recorded in the dataset’s **metadata and documentation** rather than embedded in each cell.
+
+### 3. Structure and Accessibility
+
+* **JSON representations** preserve the original units and structure of the GVFD.
+* **Country-level JSONs** were generated to allow users to focus on all value factors within a single nation.
+* A **combined dataset** was also provided, enabling both broad and granular analysis.
+
+---
+
+## Outputs
+
+The refactored GVFD is now available in the following formats:
+
+* **CSV**: Individual files by value factor category.
+* **JSON**: Both full dataset and country-level breakdowns.
+* **Parquet**: Compact, machine-friendly versions auto-generated for efficiency.
+
+Each format is accompanied by metadata documenting units, denominations, and data lineage.
+
+The GVFD represents a milestone in the effort to measure and assign value to non-traditional impacts. My **Data Analysis Refactoring V2** initiative is intended to extend the usability of IFVI’s work by making the dataset friendlier for modern analysis pipelines.
+
+I am grateful to IFVI for granting permission to republish the data in this restructured form. Any inaccuracies or errors in the refactoring are solely my own.
+
+---
+
+## Source Data
+
+**Original File:** `source/IFVI_Environmental Methodology_Global Value Factor Database V2.xlsm`
+
+- Excel workbook containing multiple sheets with environmental impact data
+- Complex tabular format with countries as columns and impact categories as rows
+- Contains metadata rows for ISO codes and regional classifications
+
+## Processing Steps
+
+### Step 1: Excel to CSV Extraction
+
+**Input:** Excel workbook with multiple sheets
+**Output:** Individual CSV files per sheet in `data/individual-sheets/`
+
+**Files Generated:**
+- `air-pollution.csv`
+- `ghgs.csv` 
+- `land-use-and-conversion.csv`
+- `waste.csv`
+- `water-consumption.csv`
+- `water-pollution.csv`
+
+**Format:** Tab-separated values with:
+- Row 1: Country names (starting column 2)
+- Row 3: ISO codes (starting column 5) 
+- Row 4: Regional classifications (starting column 6)
+- Row 5+: Impact category data
+
+### Step 2: CSV Cleaning and Standardization
+
+**Script:** `clean_csv_files.py`
+**Input:** Raw individual CSV files
+**Output:** Cleaned CSV files in `data/cleaned-sheets/`
+
+**Processing Actions:**
+- Standardized column separators to tabs
+- Removed empty rows and columns
+- Normalized numeric formatting
+- Preserved original structure for downstream processing
+
+### Step 3: JSON Conversion with ISO Code Correction
+
+**Script:** `convert_to_json.py` (primary conversion)
+**Input:** Individual sheet CSV files
+**Output:** 
+- `data/json/ifvi_environmental_data.json` (hierarchical format)
+- `data/json/ifvi_environmental_data_compact.json` (flat format)
+
+
+### Step 4: Country-Level Analysis
+
+**Script:** `country_analysis.py`
+**Input:** `ifvi_environmental_data_compact.json`
+**Output:** Country aggregation files in `data/country-analysis/`
+
+**Generated Files:**
+- `countries_aggregated.json` - Complete country-level aggregations
+- `countries_summary.csv` - Summary statistics per country
+- `iso_code_mapping.csv` - Country to ISO code reference
+- `iso_code_mapping.json` - JSON format ISO mapping
+
+
+## Output Data Formats
+
+### Hierarchical JSON Structure
+```json
+{
+  "countries": {
+    "CountryName": {
+      "metadata": { "iso_code": "XXX", "region": "Region" },
+      "categories": {
+        "CategoryName": {
+          "regions": {
+            "RegionName": {
+              "datasets": {
+                "DatasetName": {
+                  "values": [...],
+                  "statistics": { "mean": X, "min": Y, "max": Z }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+```
+
+### Compact JSON Structure
+```json
+[
+  {
+    "country": "CountryName",
+    "iso_code": "XXX",
+    "region": "Region",
+    "category": "CategoryName", 
+    "subcategory": "SubcategoryName",
+    "dataset": "DatasetName",
+    "value": 123.45
+  }
+]
+```
+
+## Repository Structure
+
+```
+IFVI-GVFD-0825/
+├── source/                          # Original Excel file
+├── data/
+│   ├── individual-sheets/           # Raw CSV extracts
+│   ├── cleaned-sheets/              # Standardized CSVs
+│   ├── json/                        # Primary JSON outputs
+│   └── country-analysis/            # Country-level aggregations
+├── processing.md                    # This documentation
+├── README.md                        # Repository overview
+└── [processing scripts]             # Python conversion scripts
+```
+
+## Processing Statistics
+
+- **Total Countries:** 229
+- **Countries with ISO Codes:** 205 (89.5%)
+- **Environmental Categories:** 5 (air pollution, GHGs, land use, waste, water)
+- **Total Data Points:** ~115,000 individual measurements
+- **Regional Coverage:** 7 major world regions
+- **File Size Reduction:** ~4.5MB compressed JSON vs. original Excel format
+
+## Data Validation
+
+### ISO Code Validation
+- Cross-referenced country names with standard ISO 3166-1 alpha-3 codes
+- Manual verification of 40+ country mappings
+- 89.5% coverage with valid ISO codes
+
+### Data Completeness
+- Water consumption: Limited coverage (11 countries)
+- Other categories: Near-complete coverage (216-217 countries each)
+- No missing value imputation performed (preserves data integrity)
+
+### Regional Distribution Validation
+- Verified regional classifications against World Bank standards
+- Identified and flagged anomalous regional values for review
+
+## Usage Recommendations
+
+### For Analysis
+- Use `ifvi_environmental_data_compact.json` for data science workflows
+- Use `countries_aggregated.json` for country-level comparisons
+- Reference `iso_code_mapping.csv` for geographic joins
+
+### For Applications
+- Hierarchical JSON provides nested access patterns
+- Compact JSON enables fast loading and filtering
+- Country analysis files support dashboard and visualization needs
+
+## Future Enhancements
+
+- Automated ISO code validation against external APIs
+- Time series analysis if historical data becomes available
+- Data quality scoring and completeness metrics
+- Integration with additional environmental datasets