Zr0Data

Team Name: 
Team Zr0

Description

Government agencies such as Births, Deaths and Marriages Victoria have extremely valuable but sensitive data. Our goal was to find a way to explore this data without compromising one's privacy.

We took the provided dataset and stripped out details such as people's names and exact date of birth. This dataset was then aggregated and collated with other datasets so we could see which suburbs had higher birth rates, whether there was a correlation between birth rate and socio-economic status and whether or not more hospitals were needed in certain suburbs.

Difficulties faced

  • Provided dataset was small and lacked the information we would have wanted
  • Difficulty matching data with other datasets
  • Hospitals were not uniquely identifiable due to lack of primary key

Tech Used

  • ​​​​​​​Tableau Public
Region: 
Team Prize Details: 
University of Melbourne, RMIT
Used Datasets: 
Dataset Name: 
Sample Births data from the Registry of Births, Deaths and Marriages (GovHack 2016)
Publishing Organisation/Agency: 
Department of Justice and Regulation
Jurisdiction of Data: 
Victorian Government
How did you use this data in your entry?: 
Stripped out sensitive details and visualized aggregated results
Dataset Name: 
Hospital Locations
Publishing Organisation/Agency: 
Department of Health and Human Services
Jurisdiction of Data: 
Victorian Government
How did you use this data in your entry?: 
Collated data with birth rate data to determine hospital usage statistics
Dataset Name: 
GovHack2016
Publishing Organisation/Agency: 
Australian Taxation Office
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
Extracted suburb postcodes from location sheet
Event Location: 
Melbourne