Creating spatial linked data collections for social science research using

Mr Marco Fahmi1, Associate Professor Amir Aryani2, Mr Les Kneebone3, Dr Tom Verhelst1

1Griffith University, Logan, Australia
2Swinburne University of Technology, Hawthorn, Australia
3Analysis & Policy Observatory, Hawthorn, Australia

Data CO-OP is a platform that facilitates collaboration between researchers and community groups to create collective impact through the sharing of spatial social data. To achieve this goal, Data CO-OP is creating Linked Data versions of a number of key government, non-profit and community-generated data collections using JSON-LD format.

These collections will use a shared JSON-LD Context to ensure terms and concepts are consistent to ensure data analyses are sound. As a starting point, Data CO-OP used ABS Census data (one of the most widely used collections by Australian social scientists) to map properties to (one of the most commonly used schemas for JSON-LD) and create a core Context that can be later expanded as more data collections are added to the platform.

The ABS Census data collection contains more than 15 thousand properties that need to be captured in the JSON-LD Context. Automated and semi-automated methods were used to map each property to one, two or three values in’s structure and create the core Context and convert ABS Census data into JSON-LD format.

Over sixty categories are needed to fully capture ABS Census data properties. This mapping activity used the top dozen categories to map to and create a core Context. The mapping covers 93% of the 15 thousand ABS Census data properties.

The mapping used to generate a core Context is promising. Current work is to validate Its usefulness by working with researchers on specific use cases with the intend to increase coverage to 100%.


Marco Fahmi is a digital research expert. His expertise is in technology- and data-driven research with experience in social sciences, humanities and ecological disciplines. He is currently leading efforts to create spatial linked data collections for social science research and data synthesis initiatives with state government to drive research-informed policy and decision making.

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