Negative Gearing InSight - Are you one?

Team Name: 
Legion

In the last year, the topic of "negative gearing" has become a hot topic in both the media, political arena as well as daily conversations. It is a controversial debate where opinions vary on the impacts of whether it's a policy that should be changed. 

Legion has taken the govhack opportunity to crunch the tax data provided by the ATO with a focus on negative gearing. By using data visualisation, predictive analytics and causal inference; we will show some hidden insights via our webapp which visualises the population geographically and demographically and socialgraphically.

 

Used Datasets: 
Dataset Name: 
Taxation statistics - Aggregated individual tax return sample files
Publishing Organisation/Agency: 
Australian Taxation Office
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
We combined the data from ATO with the SA4 region data where we converted the SA4 region names to SA4 region codes. We then converted this to csv and json for consumption by our webapp. We also created a bayesian network using the R package BNLearn and used this as the basis for our causal inference analytics.
Dataset Name: 
Statistical Area Level 4 (SA4) ASGS Ed 2011 Digital Boundaries in ESRI Shapefile Format
Publishing Organisation/Agency: 
Australian Bureau of Statistics
Jurisdiction of Data: 
Australian Government
How did you use this data in your entry?: 
We converted this shape file into a GeoJSON file for rendering by D3js in the browser. We simplified the shapes so we have a 1.4Mb GeoJSON file instead of a 90Mb GeoJSON file.
Event Location: 
Sydney Official