DATA2001 Group Assignment – SA4 Well-Resourced Area Scoring

This project analyzes the well-resourced status of SA2 regions within a given SA4 area in Greater Sydney. It uses various datasets to compute composite scores based on public amenities, economic indicators, and services.

📁 Project Structure

• scripts/
  • main.py – Orchestrates the pipeline execution
  • data_loader.py – Loads CSV and shapefile data into PostgreSQL/PostGIS
  • analyzer.py – Contains the analyze_sa4 function that processes an SA4
  • poi_fetcher.py – Fetches and processes POI data using a mock API
• data/ – Contains input datasets such as Population.csv, Income.csv, Businesses.csv, and shapefiles
• output/ – Stores generated score maps, correlation plots, and CSV outputs
• notebooks/code.ipynb – Jupyter notebook version of the analysis for interactive development
• README.md – Project documentation

🧰 Technologies Used

• Python Libraries: pandas, geopandas, sqlalchemy, matplotlib, shapely, requests
• Database: PostgreSQL with PostGIS for spatial data support

🚀 Execution Flow

1. Data Loading

   • Run data_loader.py to import all CSV and shapefiles into the PostgreSQL database.
   • This creates and populates tables like population, businesses, income, stops, school_catchments.

2. SA4 Analysis

   • Call analyze_sa4(sa4_name) from main.py or the notebook.

   • For each SA2 within the SA4, compute the following:

     • Business density per 1000 people
     • Public transport stop count
     • School catchment intersection count
     • Point of Interest count (mock data)

   • Z-scores are computed and normalized using the sigmoid function:

     Final Score = sigmoid(
         z(business per 1000 people) +
         z(stop count) +
         z(school count) +
         z(poi count)
     )

3. Output

   • A CSV of scores is saved in output/
   • A PNG map of SA2 scores with top-3 annotated is generated
   • A PNG scatterplot of score vs income with correlation coefficient is also saved
   • All scores are written to a PostgreSQL table for reuse

📌 Example

python scripts/main.py