DVN Assignment 2- Feedback in Action

Reflections on an Alien World

I wanted to capture the difference in my visualisations, showing the pre- and then post- feedback in one place. This was to allow me to more easily see the differences and help me reflect on my learnings.

The format below is that for each of the three data stories I will show the original visualisation, then the feedback and finally the adjusted chart incorporating the suggestions.

At a glance I can see the improvement those suggestions have made in comprehension and legibility. The biggest improvement has been in bigger font sizes and changing the vertical orientation to horizontal. I thought it would take up too much screen real-estate, but as you will see, it really doesn’t.

DVN Data Story 01 – Why Education Matters

Chart that contains dot points, one for each school. X Axis is the Average NAPLAN results for that school and the Y axis is the Index of Community Socio-Educational Advantage. The dots are sized, with the largest ones showing poor NAPLAN performance and coloured from Red to Green with red indicating poor attendance. The chart shows a clear correlation between low ICSEA and low NAPLAN scores.
ICSEA – NAPLAN – Correlation

Similar to the quick feedback as a result of your presentation. Some possible way to improve the chart:

-Un-rotate the title of the vertical axis.
-All fonts larger…some quite tiny.
-Did you try making the background white? I wonder if it gets clearer…maybe white wouldn’t be the colour for the middle values anymore…

-What is the horizontal line? average? could you clearly indicate that?

-Maybe you can add some text in the graph or change the title indicating what is the message of this graph (see comment in your third post for an example).

Great topic and easy to follow since the beginning. Like Roberto says maybe changing the background colour for the chart could improve the contrast between the data points. Also It will be good idea if you present specific sections form the chart to focus the reader’s attention and connect with your argument.

Chart that contains dot points, one for each school. X Axis is the Average NAPLAN results for that school and the Y axis is the Index of Community Socio-Educational Advantage. The dots are sized, with the largest ones showing poor NAPLAN performance and coloured from Red to Green with red indicating poor attendance. The chart shows a clear correlation between low ICSEA and low NAPLAN scores.
ICSEA – NAPLAN – Correlation

Some more feedback –

Did you see this story in the news the other day. http://www.abc.net.au/news/2017-05-17/spelling-mistake-spotted-in-adelaide-road-sign/8534078

I thought the story a powerful one. Id love to see a legend on your graph just so I can understand and verify it immediately in one glance that red is bad. Is the larger the dot the more students in schools? I read it referenced in your article but an annotation or simple legend would help. Ta.

Chart that contains dot points, one for each school. X Axis is the Average NAPLAN results for that school and the Y axis is the Index of Community Socio-Educational Advantage. The dots are sized, with the largest ones showing poor NAPLAN performance and coloured from Red to Green with red indicating poor attendance. The chart shows a clear correlation between low ICSEA and low NAPLAN scores.
ICSEA – NAPLAN – Correlation between poverty and performance

I think it makes it better

 

DVN Data Story 02 – Is NSW Becoming More Tolerant

Discrimination Trends 2000 – 2014
Discrimination Trends – Percentage Comparison 2000 – 2014

Nice story Rory, can you make the text larger? There is a legend added in the first figure that I couldn’t read.

The text in the x axis is nice and simple. Are the data-points really grouped in pairs of years? that’s a bit confusing.

Titles could be more prescriptive too rather than descriptive (see comment in your third post (align them to the left).

Another thing for this case it would be good if you add the source of the data underneath each graph because in one disability discrimination is going down and in the second is going slightly up. This is now understood from the text but not from the graphs alone.

Line chart from year 99-00 to 13-14. The starting amount for Homosexual and Transgender discrimination is 552 and the last figure is 63. The other line that is picked out is Race and it starts with 1745 and finishes with 230. All of the other lines for Sex, Disability, Care's Responsibility and Victimisation trend down sharply and have reduced by about 88% over the period
Discrimination Trends 2000 – 2014
Chart shows the % of complaints in relation to each other. over the period year 99-00 to 13-14. Homosexual and Transgender starts at 6% and ends at 6%. The one that has risen is Disability which started at 22% and ends at 30%. It seems to have made most of the gain from Sex which has fallen from 29% to 18%.
Discrimination Trends – Percentage Comparison 2000 – 2014

DVN Data Story 03 – Do Speeding Tickets Work The Same For Everyone

All Speeding Fines as % of Population – FY 2013 & FY 2014 – Showing SEIFA

-Better put the title above the chart. It can also be rewritten so it is more descriptive of the insight…maybe something like “he poorest in Sydney get the most speeding tickets”
-What is the meaning of the red colour? the poorest areas in sydney? Why two different tones of red? this needs to be made more explicit.

Hi Rory, great post and good story. Maybe for the next analysis you can check for data sets in other parts of Australia to enrich the narrative with more context. Great use of charts and color contrast.

Speeding Fines as % of Population – Police and Camera Combined – FY 2013 & 2014

I also created another graphic to show this better. (I really like this one)

Fine Comparison – Best & Worse

 

Image Credit: Andrew Bartram

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Rory

I am a full-time student in my second year of the MDSI program. I previously worked using Oracle Business Intelligence connected to a Siebel database. I am a Microsoft guy through and through and in a previous life was a MCSE qualified network engineer.

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