To Employ or Not To Employ

Project Info

Team Name


Potato = Not Potato


Team Members


3 members with unpublished profiles.

Project Description


Using MyVic and data.gov.au data to find how governments can best make efficient decisions to increase local employment per suburb (ie. transport, infrastructure, investment, industries).


Data Story


We used the MyVic mapping API (https://www.data.vic.gov.au/data/dataset/my-victoria-mapping-api-govhack-2018), with suburb specific datasets for various impactors, to find the impact of multiple features (eg infrastructure, cultural diversity, income) on employment per suburb (also a data set in the MyVic mapping API). The Public Internet Locations dataset (https://data.gov.au/dataset/public-internet-locations-vic) was also used as a feature in the model (to determine its impact on employment). This data was ultimately used to suggest efficient improvements in features in order to maximise employment


Evidence of Work

Video

Project Image

Team DataSets

MyVic Mapping API

Description of Use Suburb specific data from multiple data sets in the MyVic (including income, infrastructure and cultural diversity) was used as features in neural network to find impact on employment, another data set in this API, and generate suggestions based on these data sets

Data Set

Public Internet Locations (VIC)

Description of Use This dataset was used to determine the amount of public internet locations per suburb, used as a feature to determine its impact on employment

Data Set

Challenge Entries

More than apps and maps: help government decide with data

How can we combine data to help government make their big and small decisions? Government makes decisions every day—with long term consequences such as the location of a school, or on a small scale such as the rostering of helpdesk staff.

Go to Challenge | 58 teams have entered this challenge.

My Victoria: Supporting small business making big decisions

Where are the best places for certain business types to be based?

Go to Challenge | 12 teams have entered this challenge.