Infographic
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""Where does our money go?" The average weekly household expenditure in NZ: A quick overview of data from year ending June 2010.", by Natalie Morrell
Every week we spend our income on the important and maybe not-so important items – where does our money really go in a week? This bright eye-catching graphic lays out the some of the information collected by Statistics New Zealand, communicating our weekly spend in an easy to follow design. Some of the results are surprising and really make you think about where all your money really goes!
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"A Time To Live", by Courtney Stewart
My infographic was designed to be a visual feast. I have carefully selected the most interesting and relevant pieces of information out of the Statistics NZ data provided, and wrapped it in the lovely luscious design and colour that such a medium deserves. I have attempted to achieve this by singling out the theme of time and expanding upon the symbolism associated with it in the illustrations I have created for the infographic. In saying that, I have also been very cautious with not just the aesthetic delivery but the usefulness of the included data and its representation. Included in the design are a wide variety of graphs and facts in ways that are easy to understand and absorb. Central to the piece is a lengthy graph providing the audience a snapshot of the overall change alluded to in the original data and reinterpreted. Complementing this are infobites of interest that further expand on how this change is substantiated in real life. It is a fairly complex mashup, and as such not intended to be absorbed quickly. Rather I have created a work to be lingered over and enjoyed at the same time that it educates: enlightenment by design.
Selected judges' comments
"Good design, easy to read..."
"Absolutely gorgeous...The bar chart that forms the central axis of the graphic is a useful presentation of the dataset."
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"CPI-DaliPie", by Keith Ng
CPI-DaliPie won the Infographic category sponsored by Statistics New Zealand
What is driving inflation? Over the past few years, there's been a lot of media coverage of rising prices. This is an attempt to put CPI changes into context by intuitively showing the relationship between the CPI weighting basket and the changes of each group. This visualisation allows users to zoom in to every part of the CPI, to see how individual product groups have changed over time, and how they affect the higher-level groups. The "Dalipie" is a variation of the Nightingale chart. Unlike the Nightingale chart, however, both variables are dependent variables. The second variable maps to the area of each slice (if the second variable increase by 50%, the area of that slice increases by 50% - this is not the same as the radius increasing by 50%!). It's called DaliPie because it's salient effect is disproportionality. The fact that the slices do not line up jar with user expectations of what a pie graph looks like, and this jarring immediately draws attention to the graph's main feature. An element's total area and its disproportionality are immediately apparent: e.g. "This element is big/small, but it has grown disproportionately." The disproportionality is paired with animated disaggregation. You can see how each slice breaks down into more volatile slices (or conversely, when volatile slices are joined together, they average out). A lot of time has been spent on the animation because that implicity explains, purely visually, how a CPI actually works - a weighted average of changes across a broad group of items. It was a very conscious choice to use a pie-like graph. When you zoom in to a small category, even though it's filling up half the screen, the curve of it's shape tells you how big that group is in relation to the whole circle. Top-level context is retained in a way that is not possible with any other shape. -
"Know where - the earthquakes are (in 3D)", by Miles Denton (Critchlow)
This study shows earthquake density, population density and dwelling insurance cost per meshblock for New Zealand in 3D. Earthquakes are a hot topic currently around NZ, especially due to the Christchurch Earthquakes which devastated the Canterbury region and the people of NZ. However did you know that in 2009 an earthquake of 7.8 hit Fiordland at a depth 30km. The February Christchurch earthquake was a magnitude 6.3, 10 km south-east of Christchurch at a depth of 5 km. Imagine if there was a large population density or city in Fiordland during the 2009 quake, the consequences could have been far worse than Christchurch. This is why the location of populations/ cities are important to avoid events like Christchurch. Obviously proximity to fault lines (especially unknown ones) are also important, along with soil type. What I hope this 3D visualisation does, is to get people to think about Location Intelligence and how this industry can help shape our future and that of New Zealand cities. I think the 3D element provides the audience with new insights on eathquakes and location, this is why it's original. This is why I use Critchlow's catch phrase of - know where. Location is very important and it can not only save lives but also your pocket. This is why the cost of dwelling insurance is "mashed up"/ included, which adds value and something different. The most useful part of the study I discovered was that Wellington pays a high premium on dwelling insurance even though the frequency of earthquakes are low. The most appealing aspect would be the seven "mash up" comparisons between the three data sets in 3D. The most fascinating and gosh darn awesome part would definitely be Christchurch due to its high levels across all visualisation models and Fiordlands frequency of earthquakes! Please refer to the following links to access and view all 7 pdfs (Google couldn't share them all on one page/link, as they are individual pdfs, the offical link pasted further into the entry form is all three datasets mixed and mashed together): EQ=Earthquake, IN=Insurance & POP=Population EQ EQ_IN EQ_POP EQ_POP_IN IN POP POP_IN
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"Live to Work to Live", by Sean Lipidis
Using data sourced from Statistics New Zealand's 'Household economic survey', this dynamic infographic reveals how New Zealanders spend their money. It's designed to be swift, punchy, and informative. A high pace brings energy and engagement to the data; the use of a minimal palette and stylised silhouettes keeps the facts at the forefront.
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"New Zealand Forestry Infographic", by Jessica Scheurich
This entry won a Lead Judges' special award sponsored by Department of Conservation
This amazing infographic allows people to get a general understanding/summary of the forestry industry, which is more so than most currently know. The infographic is broken down into the various benefits for NZ, what's growing in the plantations, where they are growing, what happens to the trees and where it's going to. I have distilled all this information to make it easily digestible and visually rich for all readers. -
"The Super-Modern Thompsontron 1952!", by Keith Ng
How do men and women spend their time? And how do these gender differences change among different groups? The name of this visualisation comes from Alasdair Thompson, the former head of the Employers and Manufacturers Association, who resigned after saying that women tend to spend less time at work.. because of "monthly sick problem". The debate he sparked inspired me to create this. This is an exercise in simplicity. While there is a lot of data in StatsNZ's Time Use Survey, this visualisation focuses exclusively on male/female comparisons. This tight focus means the design could be stripped down to its bare minimum - change groups on top, change activities with the donut. I know the visualisation community hates on pie and donut graphs, but it was important to show each activity in the context of the whole 24 hours. It is clear, at a glance, how significant an activity is. A stacked column graph could have done the same, but element-to-element comparisons between stacks is hard because they don't have the same base. Normally, a element-to-element comparison between donuts/pies is even harder, but by spinning them around to the same level, I've allowed height comparison to be done on donut graphs, greatly increasing the ease and accuracy of the comparison.