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

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

Speeding was the major contributing factor in 66% of accidents in NSW for 2015 and 42% involving at least one fatality with the rest seriously injured (Transport for New South Wales 2016). This equated to 384 people being killed on NSW roads last year (Transport for New South Wales 2017).

Financial Impact of Speeding

The police are tasked with reducing this road toll through implementing speed reduction programs focused on speeding cameras and patrol cars. The hope is that by imposing penalties driver behaviour will change. However, are the methods used being effective in driving this change?

Last fiscal year $75 million dollars was raised in speeding fines and a further $175 million was raised from speed cameras. That is whopping $250 million dollars in a single year!

 

Looking at which regions that are being fined the most per head of population shows that the strategies currently employed are not working. The open data portal (Department of Finance, Services and Innovation 2017) give two years’ worth of fines that were issued by NSW Police and speeding cameras by the suburb of the driver’s residence (Department of Finance, Services and Innovation 2017). Blending this with the Socio-Economic Indexes for Areas (SEIFA) and population estimates from the 2011 census (Australian Bureau of Statistics 2011) show that financial penalties don’t seem to be working on the people who can afford it the least.

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

Looking at the data, the four out of the five areas that are the poorest in Sydney get the most speeding tickets with Macarthur Region averaging 40 tickets issued per 100 people. Compared to the Inner West which gets less than 13 tickets per 100.

Fine Comparison – Best & Worse

We need to urgently look for different strategies to reduce speeding and consequently the road toll in the poorer areas of Sydney because the current practices are not working.

References

Australian Bureau of Statistics 2011, ‘SEIFA by State Suburb Code’, Australian Bureau of Statistics (ABS), viewed 15 May 2017, <http://stat.data.abs.gov.au/Index.aspx?DataSetCode=SEIFA_SSC>.

Department of Finance, Services and Innovation 2017, ‘Data on the speeding fines issued by the speeding cameras and NSW Police’, NSW Open Data, viewed 15 May 2017, <https://data.nsw.gov.au/data/dataset/data-on-the-speeding-fines-issued-by-the-speeding-cameras-and-nsw-police>.

Transport for New South Wales 2016, Crash and casualty statistics – NSW Centre for Road Safety, viewed 15 May 2017, <http://roadsafety.transport.nsw.gov.au/statistics/interactivecrashstats/nsw.html>.

Transport for New South Wales 2017, ‘Statistics – NSW Centre for Road Safety’, Transport for New South Wales, viewed 15 May 2017, <http://roadsafety.transport.nsw.gov.au/statistics/>.

Image Credit: Ludo

DVN Data Story 02 – Is NSW Becoming More Tolerant

pride

I was curious to see if Australia was becoming a more tolerant society and found that the Anti-Discrimination Board NSW publish their statistics online via the NSW Open Data Portal (NSW Open Data Portal – Anti-Discrimination Board NSW n.d.).

The Board administers the anti-discrimination laws and handles complaints under the Anti-Discrimination Act 1977 (NSW) (NSW Open Data Portal – Anti-Discrimination Board NSW n.d., p.1), and as such maintains records of complaints that have been lodged by individuals in NSW.

Looking at the reported rates of discrimination in NSW for the period between 2000 and 2014 it is clear to see that the trend is very clearly downward, with a reduction of around 88%. This is a great result particularly given the rise of xenophobic rhetoric that has entered our political landscape with the rise of One Nation and other minor parties (Stein 2015; Burnside 2009; Hassan 2005). However, in 2016 the United Nations special rapporteur claimed that “Australian politicians have given permission for people to act in xenophobic ways” (Davidson 2016, p.1) and as the data is not yet released for those years we cannot see if there has been a recent rise in discrimination.

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

The one disturbing trend is that even though discrimination against Homosexual and Transgender people has also fallen in line with the other forms of discrimination, it is over-representative of the population size. According to Smith et al. (2003), he estimates that only 2% or the population are Homosexual and yet discrimination against them accounts for 5% of the reported cases that are categorised. What the figures show is that that discrimination against people with a disability is not falling at the same rate as other forms, in fact it is now the highest proportion of categorised cases. The message is getting through about equal opportunities for women, but unfortunately not for the disabled.

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

The good news is that the fight is being won, particularly on gender equality, but work still needs to be done to make it a fairer world for everybody… able-bodied or otherwise.

References

Burnside, J. 2009, ‘Australians are xenophobic’, Sydney Morning Herald, 5 November, viewed 14 May 2017, <http://www.smh.com.au/federal-politics/political-opinion/australians-are-xenophobic-20091105-hzix.html>.

Davidson, H. 2016, ‘Australia’s politicians have promoted xenophobia: UN expert’, The Guardian, 18 November, viewed 14 May 2017, <https://www.theguardian.com/australia-news/2016/nov/18/australias-immigration-policies-have-promoted-xenophobia-un-expert>.

Hassan, G. 2005, ‘Rising Tide of Xenophobia: Australia’s Shallow Multiculturalism’, Global Research – Centre for Research on Globalization, viewed 14 May 2017, <http://www.globalresearch.ca/rising-tide-of-xenophobia-australia-s-shallow-multiculturalism/1011>.

NSW Open Data Portal – Anti-Discrimination Board NSW n.d., viewed 14 May 2017, <https://data.nsw.gov.au/data/dataset?organization=anti-discrimination-board>.

Smith, A.M.A., Rissel, C.E., Richters, J., Grulich, A.E. & de Visser, R.O. 2003, ‘Sex in Australia: sexual identity, sexual attraction and sexual experience among a representative sample of adults’, Australian and New Zealand Journal of Public Health, vol. 27, no. 2, pp. 138–45.

Stein, G. 2015, ‘Australia accused of being nationalistic, xenophobic ahead of regional people smuggling talks’, ABC News, viewed 14 May 2017, <http://www.abc.net.au/news/2015-05-28/australia-accused-of-being-xenophobic-in-migrant-crisis-response/6503844>.

Photo Credit: Jeremiah John McBride

DVN Data Story 01 – Why Education Matters

Education and its funding is in the news a lot at the moment thanks to the budget it fills newspaper columns (Chapman et al. 2017; Harris 2017; Goss 2017; Doyle 2017) and talkback radio slots (Varishetti 2017; The Curious Case of School Funding in Australia 2017, Education, Environment and Equality 2016). Everyone has an opinion, and there are lots of vested interests from the education sector. So much so they have formed their own lobby groups such as the ‘Independent Schools Council of Australia’ (n.d.), ‘Independent Schools Council of Australia’ (n.d.), and Save Our Schools Australia (n.d.). These groups are created due to there being a fixed amount of money for funding education and everyone wants their share.

These discussions are essentially about funding and who is going to get what. The question I wanted to answer is ‘Why does it actually matter?’ Why do we even care about how much money schools get and more importantly, which schools get it? It matters because a good education means the poverty cycle can be broken.

“Children who come from low socioeconomic backgrounds are more likely to have low educational attainment. This has multiple implications, including health, criminality, economic participation, literacy and numeracy. Issues of functional illiteracy are closely linked to significant social impacts.” (Riddle 2014, p.1)

The chart shows that poor kids tend to do poorly at school. Attending a school with a below average Socio-Education score (ICSEA) correlates to poor performance on the NAPLAN tests which measure literacy and numeracy skills (‘NAPLAN – FAQ’ n.d.). There is also a strong correlation between achievement and attendance with students who do not do well at school attending school less. This is shown by the colour cast with the higher attendance rates (grey) predominately above the ICSEA average.

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

The Gonski funding model attempts to correct this imbalance through providing more funding to the kids that need it most because it recognises the fundamental link between a good education and lifelong accomplishment and endeavours to allow students “to achieve their very best regardless of their background or circumstances” (Gonski & Department of Education 2012, p.xxix).

You can make a difference to kids in need by making sure that you vote for fairer funding for all.

 

References

Chapman, B., Croucher, G., Clarke, K. & Watson, L. 2017, ‘Federal Budget 2017: what’s changing in education?’, The Conversation, viewed 12 May 2017, <http://theconversation.com/federal-budget-2017-whats-changing-in-education-77177>.

Doyle, J. 2017, ‘Government secures Hinch vote for school funding changes’, ABC News, Current, viewed 12 May 2017, <http://www.abc.net.au/news/story-streams/federal-budget-2017/2017-05-11/government-secures-hinch-vote-for-school-funding-changes/8515464>.

Education, Environment and Equality 2016, Q&A | ABC TV, ABC, Her Majesty’s Theatre in Adelaide, 26 September, viewed 12 May 2017, <http://www.abc.net.au/tv/qanda/txt/s4521340.htm>.

Gonski, D.M. & Department of Education, E., and Workplace Relations 2012, Review of funding for schooling: final report, Dept. of Education, Employment and Workplace Relations, Canberra.

Goss, P. 2017, ‘Gonski 2.0: Is this the school funding plan we have been looking for? Finally, yes’, The Conversation, viewed 12 May 2017, <http://theconversation.com/gonski-2-0-is-this-the-school-funding-plan-we-have-been-looking-for-finally-yes-77081>.

Harris, R. 2017, ‘Catholic schools to gain funding’, HeraldSun, viewed 12 May 2017, <http://www.heraldsun.com.au/news/national/federal-budget/federal-budget-2017-catholic-primary-schools-to-gain-funding/news-story/2c4b4e34fc501787f46d6341e468f82b>.

‘Independent Schools Council of Australia’ n.d., Independent Schools Council of Australia, viewed 12 May 2017, <http://isca.edu.au/>.

‘NAPLAN – FAQ’ n.d., NAPLAN, viewed 12 May 2017, <https://www.nap.edu.au/information/faqs/naplan–general>.

Riddle, S. 2014, ‘Why poor kids continue to do poorly in the education game’, The Conversation, viewed 12 April 2017, <http://theconversation.com/why-poor-kids-continue-to-do-poorly-in-the-education-game-23500>.

Save Our Schools Australia n.d., viewed 12 May 2017, <http://www.saveourschools.com.au/>.

The Curious Case of School Funding in Australia 2017, Radio National, 13 April, viewed 12 May 2017, <http://www.abc.net.au/radionational/programs/themoney/the-curious-case-of-school-funding/8433936>.

Varishetti, B. 2017, PM on school funding increase, Drive with Belinda Varishetti, Perth, 1 May, viewed 12 May 2017, <http://www.abc.net.au/radio/perth/programs/drive/pm-on-education/8491154>.

Image Credit: U.S. Army