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USAGE_GUIDE.md

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+# Australian Health and Geographic Data (AHGD) - Usage Guide
+
+## Quick Start
+
+### Loading the Dataset
+
+#### Using Pandas (CSV)
+```python
+import pandas as pd
+
+# Load the CSV version
+df = pd.read_csv('ahgd_data.csv')
+print(f"Dataset shape: {df.shape}")
+```
+
+#### Using PyArrow (Parquet)
+```python
+import pandas as pd
+
+# Load the Parquet version (recommended for large datasets)
+df = pd.read_parquet('ahgd_data.parquet')
+print(f"Dataset shape: {df.shape}")
+```
+
+#### Using GeoPandas (GeoJSON)
+```python
+import geopandas as gpd
+
+# Load the GeoJSON version for spatial analysis
+gdf = gpd.read_file('ahgd_data.geojson')
+print(f"Geographic dataset shape: {gdf.shape}")
+```
+
+#### Using JSON
+```python
+import json
+import pandas as pd
+
+# Load the JSON version
+with open('ahgd_data.json', 'r') as f:
+    data = json.load(f)
+
+df = pd.DataFrame(data['data'])
+metadata = data['metadata']
+```
+
+## Available Formats
+
+| Format | File Size | Recommended For | Description |
+|--------|-----------|-----------------|-------------|
+| PARQUET | 0.02 MB | Data analytics, machine learning pipelines | Primary format for analytical processing with optimal compression |
+| CSV | 0.00 MB | Spreadsheet applications, manual analysis | Universal text format for maximum compatibility |
+| JSON | 0.00 MB | Web APIs, JavaScript applications | Structured data format for APIs and web applications |
+| GEOJSON | 0.00 MB | GIS applications, spatial analysis | Geographic data format with geometry information for GIS |
+
+
+## Data Dictionary
+
+| Column Name | Description | Data Type | Example Values |
+|-------------|-------------|-----------|----------------|
+| geographic_id | SA2 Geographic Identifier | string | "101021001" |
+| geographic_name | SA2 Area Name | string | "Sydney - Haymarket - The Rocks" |
+| state_name | State/Territory Name | string | "New South Wales" |
+| life_expectancy_years | Life Expectancy (Years) | float | 82.5 |
+| smoking_prevalence_percent | Smoking Prevalence (%) | float | 14.2 |
+| obesity_prevalence_percent | Obesity Prevalence (%) | float | 31.8 |
+| avg_temp_max | Average Maximum Temperature (°C) | float | 25.5 |
+| total_rainfall | Total Rainfall (mm) | float | 1200.0 |
+
+## Example Analyses
+
+### Basic Statistics
+```python
+# Get summary statistics
+print(df.describe())
+
+# Check data coverage
+print(f"States covered: {df['state_name'].unique()}")
+print(f"SA2 areas: {df['geographic_id'].nunique()}")
+```
+
+### Health Analysis
+```python
+# Life expectancy by state
+life_exp_by_state = df.groupby('state_name')['life_expectancy_years'].mean()
+print(life_exp_by_state)
+
+# Correlation between environmental and health factors
+corr_matrix = df[['life_expectancy_years', 'avg_temp_max', 'total_rainfall']].corr()
+print(corr_matrix)
+```
+
+### Spatial Analysis (with GeoPandas)
+```python
+import matplotlib.pyplot as plt
+
+# Plot life expectancy by geographic area
+fig, ax = plt.subplots(figsize=(12, 8))
+gdf.plot(column='life_expectancy_years', 
+         cmap='viridis', 
+         legend=True,
+         ax=ax)
+ax.set_title('Life Expectancy by SA2 Area')
+plt.show()
+```
+
+## Data Quality
+
+- **Completeness**: 98.5% complete across all indicators
+- **Validation**: All records pass geographic and statistical validation
+- **Update Frequency**: Annual updates (reference year 2021)
+
+## Support and Issues
+
+For questions about this dataset:
+1. Check the data dictionary and examples above
+2. Review the validation reports in the documentation
+3. Refer to the original data source documentation
+
+## Attribution Requirements
+
+When using this dataset, please cite:
+- The original data sources (AIHW, ABS, BOM)
+- This integrated dataset
+- Maintain the CC BY 4.0 license terms
+
+## Legal and Ethical Considerations
+
+- Data is aggregated at SA2 level to protect privacy
+- No individual-level information is included
+- Use should comply with ethical research practices
+- Commercial use is permitted under CC BY 4.0