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ADIEWS Quick Reference Guide

For rapid understanding of the analysis

🎯 What is ADIEWS?

Aadhaar Demographic Intelligence & Early-Warning System

A 4-layer analytical framework that transforms 10 months of Aadhaar update data (2.4M records, 1,056 districts) into actionable policy intelligence.

📊 Dataset at a Glance

Attribute Value
Records 2,375,882 district-month combinations
Time Period March 2025 - January 2026 (10 months)
Geographic Coverage 1,056 districts, 37 states/UTs
Total Updates 49.9 Million (9.07% child, 90.93% adult)
Key Variables district, state, month, child_updates, adult_updates

🔬 Methodology Summary

Phase 1: Data Preparation

  • Cleaning: Zero missing values, no duplicates
  • Feature Engineering: child_share_pct, volatility, growth_rate
  • Aggregations: District-month (2.4M), District (1K), State-month (370)

Phase 2: Exploratory Analysis

  1. Univariate: Distributions, temporal patterns (December 18× surge)
  2. Bivariate: Child-adult correlation (r=0.85), geographic inequality (Gini=0.67)
  3. Trivariate: 3D patterns, state-time heatmap, cluster analysis (5 clusters)
  4. Geographic: Spatial autocorrelation (Moran's I=0.68), hot spot analysis

Phase 3: Intelligence Layers

  1. Layer 1 (Migration Radar): Volatility-based migration detection → 274 high-volatility districts
  2. Layer 2 (Child Risk Map): Child documentation gaps → 9 high-risk districts
  3. Layer 3 (System Intelligence): DSI/ADP metrics → 873 districts in Q4 (wasted capacity)
  4. Layer 4 (Early Warning): 10-rule alert system → 417 intervention districts (39.5%)

💡 Key Findings (One-Liners)

Finding Stat Implication
Child-Adult Imbalance Adults = 10× children (19.11 vs 1.90 mean) Systemic child neglect
December Surge 10.51M updates (18× baseline) Policy deadline compliance
Geographic Inequality Top 10 states = 72.3% updates Urban concentration
Migration Hot Zones 274 high-volatility districts Population flux indicators
Child Documentation Crisis 206 districts <5% child share Welfare access barriers
Wasted Capacity 873 districts (82.7%) in Q4 Have capacity, lack child focus
Intervention Need 417 districts flagged 39.5% require active intervention

📈 Top Priority Districts

CRITICAL (10 districts)

  1. Balotra (Rajasthan) - Score: 100.0, 5 alerts
  2. Khairthal-Tijara (Rajasthan) - Score: 98.7, 5 alerts
  3. Buldana (Maharashtra) - Score: 97.4, 4 alerts

Common Issues: High migration + Child neglect + Temporal lag

HIGH (93 districts)

  • Maharashtra: 18 districts (Yavatmal, Solapur, Nanded)
  • Rajasthan: 12 districts (Jodhpur, Barmer, Pali)
  • Gujarat: 9 districts (Dahod, Panch Mahals)

Common Issues: Migration volatility + Low child share

🗂️ File Structure

ADIEWS/
├── COMPLETE_ANALYSIS_PACKAGE.md     ← Start here (full methodology)
├── README.md                         ← Project overview
├── requirements.txt                  ← Python dependencies
│
├── docs/                             ← Detailed markdown documentation (9 files)
│   ├── DATA_PREPARATION.md
│   ├── UNIVARIATE_ANALYSIS.md
│   ├── BIVARIATE_ANALYSIS.md
│   ├── TRIVARIATE_ANALYSIS.md
│   ├── GEOGRAPHIC_ANALYSIS.md
│   ├── LAYER1_MIGRATION_RADAR.md
│   ├── LAYER2_CHILD_RISK_MAP.md
│   ├── LAYER3_SYSTEM_INTELLIGENCE.md
│   └── LAYER4_EARLY_WARNING.md
│
├── reports/                          ← PDF versions (9 files, 568 KB)
│   ├── DATA_PREPARATION.pdf
│   ├── [... 7 more PDFs ...]
│   └── README.md
│
├── outputs/                          ← Analysis results (CSV/TXT)
│   ├── processed_data.csv            ← Cleaned dataset
│   ├── layer1_migration_metrics.csv
│   ├── layer2_child_risk_metrics.csv
│   ├── layer3_dsi_adp_metrics.csv
│   ├── layer4_alert_summary.csv
│   └── [... 20+ summary files ...]
│
├── Jupyter Notebooks (9 files)
│   ├── 01_data_preparation.ipynb
│   ├── 02_univariate_analysis.ipynb
│   ├── [... 7 more notebooks ...]
│   └── 09_layer4_early_warning.ipynb
│
└── website/                          ← React dashboard
    └── public/                       ← 51 PNG visualizations

🚀 Quick Start

For Reading:

  1. COMPLETE_ANALYSIS_PACKAGE.md - Full methodology (20 min read)
  2. docs/ folder - Specific layer details (5-10 min each)
  3. reports/ PDFs - Printable versions

For Code Execution:

# 1. Clone repository
git clone https://github.com/AtharvaKatiyar/ADIEWS.git
cd ADIEWS

# 2. Setup environment
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# 3. Run notebooks (in order)
jupyter notebook
# Execute: 01 → 02 → 03 → ... → 09

# 4. Check outputs
ls outputs/

For Visualization:

# Option 1: View PNG files
cd website/public/
ls *.png  # 51 visualizations

# Option 2: Run web dashboard
cd website/
npm install
npm run dev
# Open: http://localhost:5173

📊 Statistical Techniques Used

Technique Application Result
Pearson Correlation Child-adult relationship r = 0.8507
Linear Regression Child = f(Adult) R² = 0.724
Multivariate Regression Child = f(Adult, Volatility, Time) R² = 0.781
Logistic Regression High risk prediction AUC = 0.89
Three-Way ANOVA Age × Geography × Time η² = 0.678 (age)
K-Means Clustering District segmentation 5 clusters
Spatial Autocorrelation Geographic clustering Moran's I = 0.68
Gini Coefficient Inequality measurement 0.67

🎨 Visualizations

51 PNG files organized by analysis type:

  • Univariate (8): Histograms, boxplots, time series
  • Bivariate (5): Scatter plots, correlation matrix, Lorenz curve
  • Trivariate (5): 3D plots, heatmaps, cluster analysis
  • Geographic (5): Choropleth maps, hot spot analysis
  • Layer 1 (12): Volatility distribution, migration patterns
  • Layer 2 (4): Child share, risk scores, lag analysis
  • Layer 3 (4): DSI/ADP distribution, quadrant plots
  • Layer 4 (4): Alert distribution, priority heatmap

📝 Documentation Types

Type Count Location Purpose
Markdown 9 docs/ Detailed methodology
PDF 9 reports/ Printable versions
Jupyter 9 Root Executable code
CSV 20+ outputs/ Analysis results
PNG 51 website/public/ Visualizations

🔍 How to Find Specific Information

Want to know about...

Data cleaning? → COMPLETE_ANALYSIS_PACKAGE.md (Phase 1) → docs/DATA_PREPARATION.md → 01_data_preparation.ipynb

Child documentation gaps? → docs/LAYER2_CHILD_RISK_MAP.md → reports/LAYER2_CHILD_RISK_MAP.pdf → 07_layer2_child_risk.ipynb

Migration patterns? → docs/LAYER1_MIGRATION_RADAR.md → outputs/layer1_migration_metrics.csv → 06_layer1_migration_radar.ipynb

Alert system? → docs/LAYER4_EARLY_WARNING.md → outputs/layer4_alert_summary.csv → 09_layer4_early_warning.ipynb

Specific district data? → outputs/layer*_metrics.csv (search by district name) → Check Layer 4 priority rankings

Visualizations? → website/public/*.png (51 files) → Each documentation file references relevant PNGs

💻 Python Libraries Required

pandas==2.1.4          # Data manipulation
numpy==1.26.2          # Numerical computing
matplotlib==3.8.2      # Plotting
seaborn==0.13.1        # Statistical visualization
scipy==1.11.4          # Statistical tests
scikit-learn==1.3.2    # Machine learning
plotly==5.18.0         # Interactive plots
geopandas==0.14.1      # Geographic data
pysal==23.11           # Spatial analysis
jupyter==1.0.0         # Notebook environment

📞 Support

Repository: github.com/AtharvaKatiyar/ADIEWS
Documentation: See COMPLETE_ANALYSIS_PACKAGE.md
Issues: GitHub Issues tab

⏱️ Time Estimates

Task Duration
Read COMPLETE_ANALYSIS_PACKAGE.md 20-30 min
Read all docs/ markdown files 1.5-2 hours
Execute all notebooks (fresh run) 1-1.5 hours
Review all visualizations 30-45 min
Full project understanding 3-4 hours

Last Updated: January 18, 2026
Version: 1.0
Status: Complete & Ready to Share