DATA2x01 Greater Sydney Analysis Assignment

Introduction

Australia is formally defined by more than 2000 "Statistical Area Level 2" (SA2) distinct geographical regions, designed to represent communities of between 3000-25000 people "that interact together socially and economically". In this assignment, we'll focus on the 350+ SA2s within the Greater Sydney area, and you will be tasked with spatially integrating several datasets of various formats to calculate e score for how "well-resourced each region is.

Preparation

Form a group of 2-3 students (within your enrolled tutorial where possible, or with your tutor's permission otherwise).

A shapefile of the SA2 digital boundaries can be accessed on the ABS website here. Use these, alongside the data sources on Canvas, so complete the tasks below.

• Initial data loading and cleaning should be completed in Python, then SQL should be used to merge datasets and produce scores. This code should be collated in a neat, concise Jupyter notebook file.
• This unit's Week 9 tutorial covers instructions for managing spatial data and the Installation of PostGIS (the spatial extension of PostgreSQL) on your local database server.

TASKS

Task 1

Import all datasets (deas if required) into your PostgreSQL server, using a well-defined data scheme. These sources include.

• SA2 Regions: Statistical Area Level 2 (SA2) digital boundaries (feel free to fiber this down to the "Greater Sydney" GCC).
• Businesses Number of businesses by industry and SA2 region, reported by turnover size ranges
• Stops: Locations of all public transport stops (troin and bus) in General Transit Feed Specification (GTFS) format. • Polla: Locations (and other premises details) of polling places for the 2019 Federal election
• Schools Geographical regions in which students must live to attend primary, secondary and future Government schools.
• Populations Estimates of the number of people living in each SA2 by age range [for "per capita" calculations)
• Income: Total earnings statistics by SA2 (for later correlation analysis).

Task 2

Compute a score for how "well-resourced" each individual neighbourhood is eccording to the following formula, where S is the sigmoid function, is the normalised z-score, and 'young people' are defined as anyone aged 0-19. Feel free to only calculate scores for SA2 regions with a population of at least 100, and you are welcome to extend the scoring function however you deem necessary, so long es refional exploration is provided (eg. other mothemofcol standardisation techniques, mitigating the impact of outliers, calculofing some metrics per-copito or per-sqkm, etc)