Best practice visualisation Brendan Lim of Little Sketches worked with Blue Environment in reviewing the methods for presenting data in the NWR 2018. Little Sketches’ brief was to review the visualisation approach and individual visualisation applied in the NWR 2016 and propose options for improvement. In proposing options, Little Sketches was asked to consider: best practice in data visualisation Blue Environment’s need for automated totals and sub-totals to improve quality control the Department’s requirement, stated in the project brief, for to respond to periodic “capacity state and territory data releases” such as providing for published (possibly via Power BI) “results into a limited series of key charts etc. displayed on the Department’s website”. In including the last two points within the brief, we linked visualisation, quality control and reporting flexibility. These are linked through the medium of the program software to be used for the bulk of the data presentations. This section discusses principles for best practice visualisation, then assesses the NWR 2016 from that perspective, and finally discusses options for visualisation and data presentation software. Principles for best practice visualisation Principles for best practice visualisation are tabulated below. They are collated by Little Sketches from several sources. Table Principles for best practice visualisation Principle Comments Maximise the proportion of the used for The implication is that junk’ should be ‘ink’ ‘chart displaying information minimised. Advocated by Edward Tuft. Use multiple small dense charts for comparisons Also popularised by Edward Tuft. Help the reader understand the key points of the Annotate the things that matter visualisation. Put simply, this is about choosing the type of chart (‘visual encoding’) that humans best understand. Choose chart types that best match the data types (i.e. optimised’) Based on research of the Hierarchy of Perceptual ‘perceptually tasks by William S. Cleveland and Robert MacGill Keep charts simple. Minimise the messages per chart. NWR 2016 Assessment of the from the perspective of best practice visualisation Data visualisations (i.e. charts) from the NWR 2016 were reviewed. Suggestions for improvements are separated into three tabulated sections (following). These are: Comments on sample charts from the NWR 2016 15): the NWR 2016 approach to data visualisation utilises a series of densely packed charts often combining multiple data series. –
Best practice visualisation Brendan Lim of Little Sketches worked with Blue Environment in reviewing the methods for presenting data in the NWR 2018. Little Sketches’ brief was to review the visualisation approach and individual visualisation applied in the NWR 2016 and propose options for improvement. In proposing options, Little Sketches was asked to consider: best practice in data visualisation Blue Environment’s need for automated totals and sub-totals to improve quality control the Department’s requirement, stated in the project brief, for “capacity to respond to periodic state and territory data releases” such as providing for “results published (possibly via Power BI) into a limited series of key charts etc. displayed on the Department’s website”. In including the last two points within the brief, we linked visualisation, quality control and reporting flexibility. These are linked through the medium of the program software to be used for the bulk of the data presentations. This section discusses principles for best practice visualisation, then assesses the NWR 2016 from that perspective, and finally discusses options for visualisation and data presentation software. Principles for best practice visualisation Principles for best practice visualisation are tabulated below. They are collated by Little Sketches from several sources. Table Principles for best practice visualisation Principle Comments Maximise the proportion of the ‘ink’ used for displaying information The implication is that ‘chart junk’ should be minimised. Advocated by Edward Tuft. Use multiple small dense charts for comparisons Also popularised by Edward Tuft. Annotate the things that matter Help the reader understand the key points of the visualisation. Choose chart types that best match the data types (i.e. ‘perceptually optimised’) Put simply, this is about choosing the type of chart (‘visual encoding’) that humans best understand. Based on research of the Hierarchy of Perceptual tasks by William S. Cleveland and Robert MacGill Keep charts simple. Minimise the messages per chart. Assessment of the NWR 2016 from the perspective of best practice visualisation Data visualisations (i.e. charts) from the NWR 2016 were reviewed. Suggestions for improvements are separated into three tabulated sections (following). These are: Comments on sample charts from the NWR 2016 15): the NWR 2016 approach to data visualisation utilises a series of densely packed charts – often combining multiple data series.