“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.