USAGE_GUIDE.md

Australian Health and Geographic Data (AHGD) - Usage Guide

Quick Start

Loading the Dataset

Using Pandas (CSV)

import pandas as pd

# Load the CSV version
df = pd.read_csv('ahgd_data.csv')
print(f"Dataset shape: {df.shape}")

Using PyArrow (Parquet)

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)

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

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

# 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

# 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)

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