“Visualising well-being indicators,
enhancing our understanding”
NatStats Conference: 17 September 2010
Dr Sharleen Forbes
Providing information for public policy
Front page article – ‘The Australian’ Wednesday July 21, 2010
Julia Gillard ‘questioned whether it is time to declare that areas
such as western Sydney and southeast Queensland have reached the
limit of their capacity to grow’
Questions:
• What official statistics will help inform the ‘sustainable
population’ debate?
• How can these data best be displayed?
• City, country and world perspectives of population and wellbeing?
Why is visualising data important?
The "main goal of data visualization is to communicate information clearly and
effectively through graphical means.” Friedman (2008)
New visualisation tools make it easier to access, analyse and interrogate those
official statistics that can be used to increase understanding of population
structure and well-being.
It is hard to wrap your brain around big volumes of data.
-
So represent numbers in diagrams or pictures
Harness human visual ability to interpret patterns and structures
Use to make huge data sets understandable, summarise and compare data sets,
give fast overviews (convey memorable stories) and help experts engage with
conceptual models and analytical tools.
The Internet has massively increased the availability of visualization technologies many are open source software.
Examples given all use official statistics.
At the city level?
1. Mapping population (Census) data - Vienna City Map
Shows the distribution of different age groups across the city
2. Integrating maps and data analyses
Exciting new software available – GeoVista
But beware – visualisations can be too busy?
Ethnic
distribution
in Auckland
City:
At the city level? (continued)
3. Commuting data – Home-to-work, Work-to-home
•
•
Large and complex 2006 Census tables
By different sized areas in New Zealand:
o Territorial Authority by Territorial Authority = over 5,000 cells
o Area Unit by Area Unit = more than 3 million cells!
o Meshblock by Meshblock… forget it (2 billion cells).
Far North
District
Far North District
Whangarei District
Kaipara District
Rodney District
North Shore City
Waitakere City
Auckland City
Manukau City
Total AKld
Papakura District
Franklin District
16860
285
42
30
48
48
201
75
372
9
12
Whangarei Kaipara
District
District
396
26379
327
48
63
48
141
69
321
15
15
36
276
5931
126
24
12
39
18
93
0
3
Rodney
District
30
75
315
21183
1755
1155
738
282
3930
33
48
North
Waitakere Auckland
Shore City City
City
36
57
33
6822
58383
4332
7257
1824
71793
177
171
12
36
12
1701
1905
31794
6183
1050
40932
84
99
108
171
69
5706
28188
30957
140517
40881
240543
3894
3117
Manukau
City
Total Akld Papakura
District
42
54
27
627
2604
3288
16023
66210
88122
5079
3720
But using new mapping techniques…..
201
321
138
14856
91077
70371
169983
109962
441396
9231
7110
9
12
0
54
180
258
942
3384
4764
6567
1869
At the country level? Using Census data
Population structure and changes over time
Australian Bureau of Statistics animated population pyramids
www.abs.gov.au
Country ‘wellbeing’ indicators
Statistical Office of the Republic of Slovenia
- an Interactive Atlas
Country ‘wellbeing’ indicators (cont.)
Interrogating economic indicators- Price Kaleidoscope
•
Looking inside the Consumer’s Price Index:
www.destatis.de Federal Statistical Office of Germany
At a global level? - Cartograms
Standard Representation: The World by Land Mass Area
http://www.worldmapper.org (Gastner & Newman, 2004)
Comparing Australia and the world
Cartogram Representation: The world by Population Size
http://www.worldmapper.org
A world view of some ‘wellbeing’
indicators?
www.worldmapper.org - The world by income.
But beware – what is the quality of this data?
http://www.gapminder.org -OECD data
Summary
• New ways of presenting official statistics can enhance our
understanding of wellbeing
• Enable us to see currently available data in new ways, as ‘new’
evidence for decision making
• But are not useful
– When distinct numbers are of interest
e.g. foreign exchange rates, GDP, etc.
– When tables give a more accurate picture
e.g. to convey values of summary statistics (means, medians, etc.)
• When you are not sure of the data quality
• When a data visualisation is too busy for the reader to interpret
easily
Note:If a visual looks slick and expensive most people will assume it is correct.
Questions and comments
Contact sharleen.forbes@stats.govt.nz
Thank you.