Showcasing our regions
How might we promote South Australian regions to boost regional development?
Eligibility: Use any open dataset to support your entry.
Go to Challenge | 16 teams have entered this challenge.
Chamonix IT Solutions
Did you know that the Adelade metropolitan housing market can cost up to 56% more than regional areas (https://www.sa.gov.au)?
Using government provided open datasets in conjunction with some freely available datasets, ‘Chamonix IT Solutions’ have identified the potential for showcasing regional South Australia, which over time will help drive government investment prioritisation for the regions. With a broad target audience not limited to those already looking to migrate, MySALife captures your specific interests and requirements for residency and matches this to regional areas that have similar strengths, growth potential, or an emerging industry to entice you to make the move.
With national datasets available, MySALife can compare the proposed regional location with others to make the location feel familiar. Additional potential exists to tie your interest in with ATO data, allowing MySALife to fine tune your results based on current/past employment industries and income, and overlay your known standing with the regional statistics to help you see if it’s a good fit for you.
Over time, BI reports on usage statistics will show government agencies what areas of interest people are looking for when considering migration, enabling smarter prioritisation of marketing and investment.
Sitting behind the scenes of MySALife is a collection of select datasets from sources such as ABS, ATO, Data.sa, ACARA (MySchool.edu), with emphasis on key factors that drive residency decisions. Some of these factors include:
• Employment and business/industries
o Growth of positions/roles by area, small to large sized business growth etc
o Primary industry of region, growth of specific industries etc
o Distribution of part-time/full-time employment
o Unemployment rate
• Education
o Distribution of public/private schools, number of pre, primary, and secondary schools, enrolment statistics per school, NAPLAN results available upon application
• Healthcare
o Facilities available, coverage, local hospital(s) etc
• Cost of living and housing
o Average mortgage repayments, median house prices etc
• Quality of life/Environment
o Air/water quality, crime rate statistics,
o Average commute times etc
• And many more
Using tools such as BI, this information is displayed to the user in a meaningful way and help them through a discovery journey to help them make a decision on where to migrate.
Description of Use Used to add information regarding internet speeds for residential and commercial areas under the NBN. Can be significant for those wishing to work from home
Description of Use Used to capture property price in regional areas in contrast to metro areas as well as other significant data points such as income, employment, industry growth, average and media commute times by region etc
Description of Use Summary of median private rent in South Australia by: suburb, postcode, State Government regions and Local Government Areas. Used to compare against other state information
Description of Use Geo location of fruit fly pest free areas, a unique piece of information for SA
Description of Use Key information for education statistics by region
Description of Use Used to identify industry worth and growth potentials from trends
Description of Use Used to compare/contrast income potential
Description of Use Used to access water quality information to help qualify Quality of Life sections
Description of Use ATO specific information for statistics such as income by region
Description of Use Used to collate various SA regional statistics, such as crime rates
Description of Use Used to enhance the "education" criteria for matching and comparison
Eligibility: Use any open dataset to support your entry.
Go to Challenge | 16 teams have entered this challenge.
Eligibility: The participants need to use ATO Taxation Statistics.
Go to Challenge | 27 teams have entered this challenge.
Eligibility: Use any open dataset to support your entry.
Go to Challenge | 10 teams have entered this challenge.