README.md

---
license: mit
task_categories:
- feature-extraction
- tabular-classification
language:
- en
tags:
- geography
- australia
- correspondence
- spatial-data
- abs
- government
size_categories:
- 10K<n<100K
---

# Australian Geographic Correspondence Table (ABS ASGS Edition 3)

A comprehensive correspondence table linking Australian geographic hierarchies (POA ↔ LGA ↔ SA2 ↔ Branch Catchments) built using real Australian Bureau of Statistics (ABS) data.

## Dataset Description

This dataset provides complete spatial correspondence relationships between different levels of the Australian Statistical Geography Standard (ASGS) Edition 3, enabling accurate feature aggregation and geographic analysis across Australia.

### What's Included

- **Master Correspondence Table**: 95,674 records linking POA, LGA, SA2, and HCF branch catchments
- **Branch Catchments**: 9 unique HCF service areas with 15km radius coverage
- **Spatial Weights**: Area-based intersection ratios for accurate data aggregation
- **Geographic Coverage**: Complete national coverage of Australian boundaries

## Files

| File | Records | Size | Description |
|------|---------|------|-------------|
| `master_correspondence_table.parquet` | 95,674 | 271KB | Main correspondence table (recommended) |
| `master_correspondence_table.csv` | 95,674 | 11MB | Main correspondence table (CSV format) |
| `branch_catchments.parquet` | 9 | 38KB | HCF branch service areas |
| `branch_catchments.csv` | 9 | 38KB | HCF branch service areas (CSV format) |

## Schema

### Master Correspondence Table

| Column | Type | Description |
|--------|------|-------------|
| `poa_code` | string | 4-digit Postal Area code |
| `poa_name` | string | Postal Area name |
| `lga_code` | string | 5-digit Local Government Area code |
| `lga_name` | string | Local Government Area name |
| `sa2_code` | string | 9-digit Statistical Area Level 2 code |
| `sa2_name` | string | Statistical Area Level 2 name |
| `branch_id` | string | Unique branch identifier (null if >15km from any branch) |
| `branch_name` | string | HCF branch facility name (null if >15km from any branch) |
| `branch_type` | string | "Dental" or "Eyecare" (null if >15km from any branch) |
| `branch_lat` | float | Branch latitude (null if >15km from any branch) |
| `branch_long` | float | Branch longitude (null if >15km from any branch) |
| `poa_to_lga_weight` | float | Area-weighted intersection ratio (0-1) |
| `lga_to_sa2_weight` | float | Area-weighted intersection ratio (0-1) |
| `catchment_overlap_pct` | float | % of catchment overlapping with area (0 if >15km) |
| `distance_to_branch_km` | float | Distance from area centroid to nearest branch (0 if >15km) |
| `record_type` | string | "geographic_only" or "master_correspondence" |

## Geographic Coverage

- **2,644 POA boundaries** (Postal Areas) - ABS 2021 data
- **566 LGA boundaries** (Local Government Areas) - ABS 2022 data  
- **2,473 SA2 boundaries** (Statistical Area Level 2) - ABS 2021 data
- **9 HCF branch catchments** (15km radius service areas)

## Use Cases

### Feature Aggregation
```python
import pandas as pd

# Load correspondence table
corr = pd.read_parquet('master_correspondence_table.parquet')

# Aggregate postcode-level data to LGA level
postcode_data = pd.read_csv('your_postcode_data.csv')
lga_aggregated = postcode_data.merge(corr, left_on='postcode', right_on='poa_code') \
    .groupby('lga_code').agg({
        'population': 'sum',
        'income': lambda x: np.average(x, weights=corr.loc[x.index, 'poa_to_lga_weight'])
    })
```

### Service Area Analysis
```python
# Find all areas within HCF branch catchments
branch_areas = corr[corr['branch_id'].notna()]
coverage_by_branch = branch_areas.groupby('branch_name').agg({
    'sa2_code': 'count',
    'catchment_overlap_pct': 'mean'
})
```

## Data Quality

- **Spatial Accuracy**: Built using official ABS ASGS Edition 3 boundaries
- **Coverage**: 100% coverage of Australian POA, LGA, and SA2 areas
- **Validation**: Comprehensive geometric and topological validation
- **Weights**: Area-based intersection calculations for precise aggregation

## Source Data

- **ABS ASGS Edition 3**: Australian Statistical Geography Standard digital boundaries
- **POA 2021**: Postal Area boundaries (GDA2020 coordinate system)
- **LGA 2022**: Local Government Area boundaries (GDA2020 coordinate system)
- **SA2 2021**: Statistical Area Level 2 boundaries (GDA2020 coordinate system)

## Methodology

1. **Data Download**: Automated download of official ABS shapefiles
2. **Spatial Processing**: Geometric intersection calculations using GeoPandas
3. **Weight Calculation**: Area-based ratios for accurate feature aggregation
4. **Catchment Generation**: 15km radius buffers in Australian Albers projection
5. **Validation**: Comprehensive quality checks and coverage analysis

## License

This dataset is licensed under the MIT License. The underlying ABS data is available under Creative Commons Attribution 4.0 International License.

## Citation

```bibtex
@dataset{hcf_abs_correspondence_2024,
  title={Australian Geographic Correspondence Table (ABS ASGS Edition 3)},
  author={HCF Data Science Team},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/massomo/abscorrespondance}
}
```

## Technical Details

- **Built with**: Python, GeoPandas, Shapely, PyProj
- **Coordinate Systems**: WGS84 (EPSG:4326) and Australian Albers (EPSG:3577)
- **Processing**: Spatial intersection, area calculation, distance analysis
- **Export**: Parquet and CSV formats with comprehensive metadata

## Contact

For questions about this dataset or the underlying methodology, please refer to the source repository or open an issue on the project GitHub page.

---

*Generated using the Master Correspondence Table Builder - a modern Python replacement for legacy SAS Enterprise Guide geographic workflows.*