Team Name: 
Four Planners and a Panda

SweetSpot is a data visualisation tool which combines employment, building, residential and traffic data in Melbourne to determine promising locations to start or expand a business.

It allows users to interrogate a wide range of property, landuse and demographic datasets at an individual building level, with the aim of helping users suitably locate a business.  The tool allows for the interrogation of:

Election Stitchup

Tasmanian 2013 Senate election results in the form of crochet
Team Name: 
Department of Digital Fabulists

Election Stitchup is a data visualisation project for the 2010 and 2013 Senate election results. We decided to work with election data but with a desire to represent it in a way that really stretched GovHack’s horizon - by crocheting the results.

We are using this dataset as it is available on the site, a major data portal for GovHack and indeed the data that partially determines who the Federal Government is!

Highway to the Dangerzone

Team Name: 
Four Planners and a Panda


Highway to the Danger zone is an interactive planning tool intended to help policy makers with identifying future trends for population, employment and travel demand growth and planning future development. In particularly the tool utilises Geelong City Council planning population forecasts and compares sub-region employment with labour force. Furthermore, public transport accessibility is shown for each small area. 

Find My Toilet

Team Name: 
Hackstreet Boys

An app allowing users to find their closest public amenity (toilets, shower, drinking water), provide feedback rating of the facility and inform the body responsible for the amenity if maintenance is required. By recording the location of users when they indicate they need a facility and providing access to this new dataset to councils and town planners via both an API and a heatmap, it is easy for them to visualise where new amenities are most needed.


Team Name: 
Team Avinium

IPGODMODE - patent visualization and prediction with machine learning