Project 4680

Project Info

Project Description


Community solution that reduces litter, creates healthier communities and increases regional development.


Data Story


How might we help prevent littering especially fast food litter and how can we help promote healthier and stronger communities around Queensland? 

We reviewed the data collected and analysed to find the most appropriate course of action. Some of the information interpreted from the data were; that littering was largely going down in Australia, Queensland was the only state that had a regression in "Plastics", Although the premise largely focussed on "Fast Food" litter, we cannot find enough data to help formulate a definitive plan to address the problem.

Some of the analysis we did focused on related possible reasons for the apparent deficit in improvement in Queensland. We compared the population growth, tourism rates, industry growth against the littering rates data.  Although some areas suggested a correlation, causation was not apparent.     

Talking with local community caretaker from his observations when cleaning up was that cigarette butts and glassware was still a major problem, this matched the data from our analysis.

We also wanted to review the data for healthier communities, when it comes to mental and social health in the community most of the data appeared representative of the most severe forms of health problems. Identifying/preventing social isolation would be a good start to address these issues before they become severe.

The paramount issue we encountered was a lack of data resolution to help make clear decisions moving forward but believe this solution will be a sustainable solution.

Interactive map: https://sleeples.love/govhack
App prototype: https://marvelapp.com/9fdb1j7/screen/47640001


Evidence of Work

Video

Homepage

Team DataSets

Projected population (medium series), by five-year age group and sex, by local government area, Queensland, 2011 to 2036

Description of Use: Used to analyse the correlation between population growth and littering.

Data Set

Regional employment projections, 2010–11 to 2040–41

Description of Use: Used as to analyse the correlation between industry growth and litter.

Data Set

SoE2015: Number of litter items for different site types

Description of Use: Used to create time series charts to identify where litter is being generated.

Data Set

SoE2015: Main material types littered

Description of Use: Used to create time series charts to identify types of litter.

Data Set

Gladstone region: Non–resident population projections, 2016 to 2022

Data Set

International visitors by Queensland Tourism region

Description of Use: Used to cross reference if any similarities between littering rates and increased human presence.

Data Set

Domestic tourism: Day visitors by Queensland tourism region visited, 1998–99 to 2016–17

Description of Use: Used to cross reference if any similarities between littering rates and increased human presence.

Data Set

Litter auditing data from South West Queensland Litter Prevention Pilot Project

Description of Use: Used to identify types of litter by locality.

Data Set

Challenges

Science Data Challenge

How might we make discovering and understanding scientific data for a location possible?

Go to Challenge | 9 teams have entered this challenge.

Tourism Jobs Challenge

How might we determine the future tourism job needs for Queensland?

Go to Challenge | 7 teams have entered this challenge.

Healthy Communities Challenge

How might we assist councils to build healthier and stronger communities?

Go to Challenge | 49 teams have entered this challenge.

Out of the Box - New take on data for regional development

Use an existing data set outside its normal context to both display and encourage innovate solutions to regional problems and promote and foster regional economic development.

Go to Challenge | 11 teams have entered this challenge.

Litter Challenge

How might we prevent littering of fast food packaging?

Go to Challenge | 16 teams have entered this challenge.

Save Lives With Data

How can we use data and technology to better the health of the Australian population, and what could be the economic impacts?

Go to Challenge | 35 teams have entered this challenge.

Protecting our Carers

How can we motivate young carers to search out information on and engage with supports and services early in their caring journey, rather than when they’re in a crisis situation?

Go to Challenge | 9 teams have entered this challenge.

Bounty: Industry meets Academia

How can we overcome the cultural differences between business and researchers to encourage innovation and collaboration?

Go to Challenge | 12 teams have entered this challenge.

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