Little Rubbish

Project Info

Team Name

Data Junkies

Team Members

1 member with an unpublished profile.

Project Description

Given the enormous problem of fast food packaging being littered all over Queensland, in our parks, streets, forests, waterways, beaches and oceans: this is causing enormous damage to our environment and health of our communities, as well as costing us millions of dollars in clean-up costs.

Our plan is to develop a practical solution that involves using a challenging yet entertaining gamification approach to foster awareness of the importance of preventing and/or cleaning up litter among the queensland communities. Our plan is to maximise the use of data sets such as the Litter auditing data from South West Queensland Litter Prevention Pilot Project, SoE2015: Main material types littered, SoE2015: Number of litter items in Queensland, joined with local government area datasets to bring factual data into the app that represents the true extent of the problem and brings a fun gaming approach to solving the issue that is aimed at changing people’s mindset and changing the culture of littering being everybody’s problem and everybody’s responsibility. We hope this gaming solution will go a long way in educating the younger generation about the responsibility of waste removal in schools.

Data Story

Using a number of data sets we were able to identify Queensland’s most problematic litter areas. We downloaded a number of data files, e.g. the litter audit CSV file, loaded it into a database and counts for Fast food (takeaway) were tallied together. Bin counts were also totalled together. The data was then loaded into a mysql database where it could be queried directly. Next we created a REST api to return the litter audit data in JSON format. We then analysed this data and found highly travelled areas were the ones which had the most litter. We matched these areas to the regional councils these specific areas falls under. We identified datasets that linked state and non-state schools as well as fast food outlets that could be plotted to each specific area.

Data Feeds Developed:

Evidence of Work


Project Image

Team DataSets

Age and sex indicators by local government area (LGA), Queensland, 30 June 2016pr

Description of Use We used the data set to cross reference against population data for the identified high density litter areas, and identify potential main source contributors to littering on the assumption of the age & sex data for that region.

Data Set

Estimated resident population by local government area (LGA), Queensland, 1991 to 2017r

Description of Use We compared the population data of identified high density litter areas to see if there was a correlation or any additional resident data to be interpreted.

Data Set

Litter auditing data from South West Queensland Litter Prevention Pilot Project

Description of Use We used the Collection Number, Geo-Coords, Site Info, Location Size, Traffic, Bins & Rubbish Counts from the South West Litter Prevention Pilot Project to populate live rubbish locations on a map.

Data Set

Challenge Entries

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.

Bounty: Decision Support

How can we make it easy to use weather and ocean data to our advantage? (e.g. when should you lay concrete, or go out yachtting or picnicking?)

Go to Challenge | 24 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.