#MakeoverMonday 2020, Week 8: Australian Homelessness Services Housing Outcomes

For week 8 I chose a dataset from the Australian Institute of Health and Welfare, looking at homelessness statistics and, more particularly, the outcomes of specialist homelessness services.

The original visualization was published on AIHW's website:

https://media.data.world/wtj6rnuxQfS81iAvO0iY_week%207%20original.png

What works well:

  • The visual is grabbing my attention with the curved lines of the sankey and the colors used.
  • I like the choice of colors, as they work well together, indicate the higher risk categories (rough sleeping, couch surfing and short-term accommodation) and indicate those categories where people are not 'on their own' anymore but supported by a service.
  • In general, I like that the sankey shows the 'before and after' with the outcomes for homeless people, indicating that a lot more people are in private housing and community housing and that across all risk categories numbers have reduced over time.

What could be improved:

  • Most lay people will likely need some more context to genuinely understand what is shown in the visualization.
  • The subtitle describes what was done but does not clearly indicate how to read the chart or what 'specialist homelessness services' are.
  • The interactivity (filter) seems very laggy, which makes it harder to navigate to the different views I am interested in.
  • For me, the sankey diagram hides the magnitude of the challenge and also glosses over the individual cases. Yes, at the national level that is probably to be expected, but I find the visualization doesn't really touch me on an emotional level, and there is no call to action, which is a missed opportunity given the topic.

What I did:

  • This is not an easy dataset to work with, but it's an important topic to tackle.
  • I went through a few ideas and charts, trying to figure out what the message is I want to convey.
  • Talking about the positive outcomes was definitely tempting but after comparing the 'before' and 'after' situations and the number of people in each of the different categories, I decided that I wanted to focus on those cases where people's living situation got worse instead of improving, despite accessing specialist homelessness services.
  • AIHW probably has an answer as to why these things happen, but I figured it's the most obvious one for me to call out, so here is my viz (click for the interactive version):

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#MakeoverMonday 2020, Week 9: Sleep Hours Needed Vs. Averaged

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#MakeoverMonday 2020, Week 7: World Wealth