Team Name:
ABS Pigeon
Team Members:
ABS Pigeon
Our project directly solves the core challenge of disjointed, hard-to-discover ABS datasets by transforming raw data into an explorable knowledge network. Using Graph Theory (to model relationships) and Knowledge Networks (to visualize connections), we eliminate the need to scroll through 100+ catalogues. Users explore topics like “Youth Employment” and instantly see linked datasets, trends, and metadata—turning chaos into clarity. This innovative, user-centric approach aligns with the challenge’s goal of improving data accessibility and usability.
Researchers and policymakers struggle with ABS data scattered across 100+ catalogues—tedious scrolling, inconsistent naming, and no clear connections. Our solution, ABS Spider, transforms this chaos into a living knowledge network. Using Graph Theory (modeling datasets, concepts, and time as interconnected nodes) and Knowledge Networks (visualizing relationships), users instantly see trends (e.g., declining unemployment) and linked datasets (e.g., Labour Force Survey → Census → Education data) without hunting through spreadsheets. This turns raw data from a chore into a clear, navigable story—no more guesswork, just actionable insights.
“ABS Spider turns data chaos into clarity
Description of Use Based on our project, we use Australian Bureau of Statistics (ABS) Labour Force Survey data (specifically Catalogue 6202.0) as the primary dataset for demonstration. This dataset tracks employment, participation, and unemployment across demographics since 1978, providing real-world examples to showcase how our tool connects related information (e.g., linking "Youth Unemployment" to education and regional data). Other ABS datasets (like Census or Education Surveys) can also be integrated, but the Labour Force Survey is our flagship example for testing the tool’s functionality.
Eligibility: Open to contestants from any State. At least one dataset from the ABS must be used.
Go to Challenge | 21 teams have entered this challenge.
