Dataset Summary

Dataset Summary: Labour Force Statistics

Let’s take a look at the Labour Force Statistics dataset from the ATO. This dataset is essentially impossible to navigate or understand for a new audience. It comprises multiple reports in different formalts, for different years and locations. The presentation does not support someone in a hurry trying to understand how to navigate and understand the key facts which are supported by this data.

The My MP data summary provides this.

The map layer shows how the primary variables affecting the labour force are represented in a voting district. Issues affecting skills demand, employment types, job security, pay, unemployment and the cost to business are presented geographically. MPs can quickly pick out areas where local employers do not match the skills of local residents, or where unemployment may be affecting an aging population.

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The key relationships map shows how the input drivers of skills demand, worker demographics and employment types interact causally to have impacts on job security and pay, unemploment and the costs to business. It call out the new economy and industry trends as important related concepts for further analysis later.

In this way, the MP is able to quickly assess the essential facts of a situation, confident that good evidence is backing the decisions they make as a result.

Dataset Summary: Air Quality Monitoring

Let’s look at an unrelated dataset. The data available here includes raw data from air quality monitoring sites. While very useful, it’s really only able to be understood by a data analyst or expert in the area. By providing a simple data summary through the My MP app, it is possible to quickly give decision-makers the ability to quickly understand the essential aspects of air quality and show them how to find the underlying data records should it be needed.

The map layer in this case could show real-time air quality, or alternatively could utilise a time slider to provide access to historical air quality information easily.

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