Bounty: Unmasking the State / Territory employment data.
How can we unmask the hidden data behind the labour market in our states and territories?
Go to Challenge | 8 teams have entered this challenge.
Team Name:
DataCake
Team Members:
Solving Insolvencies
Project Info
Team Name
DataCake
Team Members
4 members with unpublished profiles.
Project Description
We want to help Australian to be in a better financial situation. Our goal is to avoid another GFC or any other financial crisis. This is the reason why we wanted to work on the challenge related to insolvencies.
Our approach for this hackathon was to integrate large volume of data across multiple governmental agencies and analyse the main contributing factors impacting insolvencies in Australia.
https://public.tableau.com/profile/william6478#!/vizhome/GOVHACK/Main?publish=yes
Data Story
What we did:
- We integrated more than 20 different datasets from different governmental agencies
- We built a dataset with 200 different variables
- We used a combination of Machine Learning and Advanced Analytics to derive additional information
- We focused on interpreting our models outputs in order to derive meaningful insights. We are against black-box solution
- We built an interactive dashboard to explore all these informations
Our solution helps to:
- Explore different datasets we integrated
- Compare different SA3 areas on the different accessible variables
- Understand the factors that are impacting insolvencies ratio
Team DataSets
regional personal insolvency statistics
CENSUS 2016 - LFSP Labour Force Status
CENSUS 2016 - Highest Education Achieved (HEAP)
CENSUS 2016 - Total family income in weekly amount
CENSUS 2016 - Family composition
CENSUS 2016 - Count of All Children
CENSUS 2016 - Hours worked in ranges
Mode of travel to work
Disability (ASSNP)
CENSUS 2016 - Year of arrival in Australia
CENSUS 2016 - Language spoken at home
CENSUS 2016 - Proficiency in spoken English (ENGP)
CENSUS 2016 - Industry of employment
CENSUS 2016 - Occupation
CENSUS 2016 - Sex
Social Marital Status
CENSUS 2016 - Age
Department of Social Services FOI Disclosure Log
Australian Institute of Health and Welfare FOI Disclosure Log
Specialist attendances and Medicare benefits expenditure on specialist attendances, per person, by Statistical Area Level 3 (SA3)
Life Expectancy and Potentially Avoidable Deaths
Census of Population and Housing: Estimating homelessness
GovHackATO
Higher Education Attrition Rates
NSW childhood immunisation coverage data by SA3
MBS Mental Health Data by SA3
Job Seekers Receiving Newstart Allowance and Youth Allowance (Other) By Statistical Area Level 3
B40 Non-School Qualification: Level of Education by Age by Sex
B17 Total Personal Income (weekly) by Age by Sex
DSS Young Carers Demographics March 2018
Census 2016 Datapack
Non Compliance Personal Insolvencies
Debtors Regional Statistics
DSS Payment Demographic Data
Challenge Entries
What do you want from government data challenge?
How should NSW government best provide data to the developer community? Show how our data can be made more usable for developers. What quality or format or standardisation issues does government need to fix or to consider? What developer community needs does the government need to support better?
Go to Challenge | 13 teams have entered this challenge.
SEED - Open Data with a Purpose
We are seeking to challenge the status quo. Moving from open data as a bi-product of government business, to active management of open data to better support reuse and innovation – hence open data with a purpose. To achieve this we want to trigger a conversation between developers and the data custodians.
Eligibility: Use at least one dataset from SEED as central element, plus feedback on potential improvements to it.
Go to Challenge | 11 teams have entered this challenge.
Bounty: Is seeing truely believing?
How can we tell a story with visualisations, that speaks the truest representation of our data?
Go to Challenge | 28 teams have entered this challenge.
Bounty: Integrating AIHW
How can we integrate AIHW and other data sources in interesting ways?
Go to Challenge | 28 teams have entered this challenge.
Bounty: Mix and Mashup
How can we combine the uncombinable?
Go to Challenge | 61 teams have entered this challenge.
Show Us The Numbers
How can we use open finance data to turn numbers into stories?
Go to Challenge | 13 teams have entered this challenge.
Data4Good
How can open data be used to make a social impact, contributing to the betterment of society? How can we improve prospects for children, and education, using open data? What sort of impact can be made on homelessness, mental health outcomes, or the environment, using open data?
Go to Challenge | 19 teams have entered this challenge.
Spatial data challenge
How can spatial data be leveraged to provide the best community outcome? How can this mapping data be used to deliver value to the people of NSW?
Eligibility: This challenge will award a prize to the best pitch that combines two or more NSW spatial datasets in a proposal that delivers value to the people of NSW.
Go to Challenge | 14 teams have entered this challenge.