abdullahsafi / Sydney_Cyclability_Analysis

Analysing data collected from the Australian Bureau of Statistics (ABS), to calculate a "cyclability" score for different neighbourhoods in the Greater Sydney area.

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🚴‍♂️ Sydney Cyclability Analysis 🚴‍♂️

DATA2001 PRACTICAL ASSIGNMENT

Aim 🔎

To calculate a cyclability score for different neighbourhoods in Sydney.

What is a Cyclability score?

Its a score that we define to assess how well a neighbourhood caters to cyclists.

To calculate cyclability per neighbourhood, a formula was used that took all data sources with relevance to cyclability into consideration. The formula is as follows:

cyclability = z(population density)+ z(dwelling density)+ z(service balance)+ z(bikepod density)+ z(BikeParking)

Where z is z-score or "standard score" of a measure, which can be calculated using z(measure, x) = x − avg measure / stddev measure

Table of contents 📋

  1. Analysis Code
  2. Final Report
  3. Raw Data
  4. Getting Started
  5. Data Origin and Summary
  6. Schema Overview
  7. Important Variables

Getting Started 📁

Short list of intructions for new collaborators to get up and running with the project.

List of commands:

  • $ git clone https://github.com/abdullahsafi/Sydney_Cyclability_Analysis.git
  • $ cd Sydney_Cyclability_Analysis
  • Here you can access the Source Code and Report in Jupter Notebooks.
  • The Product Notebook can be opened through Google Colab:
    • Ensure you upload your copy of the repo onto your google drive
    • There is a path variable for you to link the data
  • The Product Notebook can be opened through Jupyter Notebooks (recommened):
    • Ensure that modules and dependancies are downloaded

Data Origin and Summary 💾

Origin of data

Several CSV files with Statistical Area 2 (SA2) data from the Australian Bureau of Statistics (ABS), as well as some car-sharing data from Sydney.

populationdensitypopulation divided by neighbourhood’s land areaNeighbourhoods.csvdwellingdensitynumber of dwellings divided by neighbourhood land areaNeighbourhoods.csvservicebalancebalanceof selected business types in neighbourhoodBusinessStats.csvtransportdensitynumber of car-sharing pods per suburb divided by areaCarSharingPods.csv

Schema

schema.PNG

Key Variables

Description of variables that are important throughout the Notebooks.

Key Variables Description
populationdensity population divided by neighbourhood’s land area
dwellingdensity number of dwellings divided by neighbourhood land area
servicebalance balance of selected business types in neighbourhood
transportdensity number of car-sharing pods per suburb divided by area

About

Analysing data collected from the Australian Bureau of Statistics (ABS), to calculate a "cyclability" score for different neighbourhoods in the Greater Sydney area.


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