Posts filed under Graphics (360)

May 26, 2016

Budget visualisations

This will likely be updated as I find them

  1. From Keith Ng. Budget now and over time. This gets special mention for being inflation-adjusted (it’s in 2014 dollars). Doesn’t work on my phone, but works well on a small laptop screen
  2. NZ Herald. Works (though hard to read) on a mobile. Still hard to read on a small laptop screen, but attractive on a large screen. I still have reservations about the bubbles.
  3. Stuff has a set of charts. The surplus/deficit one is nicely clear, though there’s nothing about the financial crisis/recession as an explanation for a lot of it.
  4. The government has interactive charts of Core Crown Revenue, Core Crown Expenditure, and breakdown for a taxpayer. On the last one, they lose points for displaying just income tax, when the Treasury are about the only people who could easily do better.
April 29, 2016

Bar chart of the week

From the IMF, using OECD data, (via Sam Warburton)


Bar charts should start at zero (and probably shouldn’t  have distracting house/arrow/tree reflections in the background), but this graph would look even worse if the y-axis went down to zero. The problem is that ‘zero’ isn’t 0 for this sort of measurement.  The index is the price:income ratio now, divided by the price:income ratio in 2010, multiplied by 100.  The “no change” value is 100, which suggests using that for the floor of the bars.  Making the bars wider relative to the spaces gives easier comparisons and makes the graph less busy.  The colour scheme isn’t ideal for dichromats, but it only reinforces the information, it’s not needed to interpret anything.



The next step, as Sam suggested on Twitter, would be to give up on the ‘index’, which is really economist jargon, and just describe the change in %.  He also suggesting putting the two labels in colour (which required some fiddling: for the text colour to look like the bar colour it has to actually be darker).


One might also go back to the full names of the countries, but I quite like the abbreviations.


April 28, 2016


Most of Auckland is within walking distance of a school: there are over 500 schools in the 560 km2 of Auckland’s urban area. That’s usually regarded as a Good Thing, and Healthy. Auckland Transport’s “walking school bus” program takes advantage of it to get kids more active and to get cars off the roads. The coverage is pretty impressive: in this map by Stephen Davis, the circles show a 800m (half-mile, 2 km2 ) area around each school:


However, as a story at Stuff notes,  if most everywhere in Auckland is close to a school, the schools are going to be close to other establishments.  With a school on most square kilometres of urban land, there will be shops in the square kilometre around most schools selling fast food, or junk food.

That’s going to be even more true in denser, more walkable cities elsewhere, from Amsterdam to New AmsterdamYork.  “Near schools” isn’t a thing in cities. To reduce the number of these shops near schools, you have to reduce them everywhere.

This isn’t to say that all restrictions on fast-food sales are unreasonable, but having lots of things in a relatively small area is hard to avoid in cities. It’s how cities work.

April 20, 2016

Housing affordability graphics

Another nice Herald interactive, this time of housing affordability.


Affordability comes in two parts: down payment and monthly mortgage costs. The affordability index from Massey University looks at monthly payments; this one looks at the 20% down payment.

The difference between Auckland and the rest of the country is pretty dramatic, but there are other things to see. Above, the centre of Auckland is much less expensive than the rest of the city: 75% of properties are valued at under $500,000 by CoreLogic.  That’s the apartments, but they mostly aren’t the sort of apartments people are planning to stay in long-term.

Another interesting feature for Auckland is that the neighbourhoods really are ordered in price — you don’t see the spatial trends changing as you move the slider, so there aren’t areas where the low-end houses are especially cheap and the high-end houses especially expensive.

You can also see the difficulty of relating valuations to prices. In Point Chev, the valuations say 70% of homes are valued at over $1 million. On the other hand, the median sale price is $990,00, so less than half the homes that changed hands went for over a million.


Both those numbers are correct. Well, ok,  I assume they are both correct; they are both what they are supposed to be.  It’s just that home sales aren’t a random sample of all homes.  But if the median sale price is $990k and the median valuation for all homes is $1.2m, you can see that interpreting these numbers is harder than it looks.

April 9, 2016

Movie stars broken down by age and sex

The folks at Polygraph have a lovely set of interactive graphics of number of speaking lines in 2000 movie screenplays, with IMDB look-ups of actor age and gender.  If you haven’t been living in a cave on Mars, the basic conclusion won’t be surprising, but the extent of the differences might. Frozen, for example, gave more than half the lines to male characters.

They’ve also made a lot of data available on Github for other people to use. Here’s a graph combining the age and gender data in a different way than they did: total number of speaking lines by age and gender


Men and women have similar number of speaking lines up to about age 30, but after that there’s a huge separation and much less opportunity for female actors.  We can all think of exceptions: Judi “M” Dench, Maggie “Minerva” Smith, Joanna “Absolutely no relation” Lumley, but they are exceptions.

Compared to what?

Two maps via Twitter:

From the Sydney Morning Herald, via @mlle_elle and @rpy


The differences in population density swamp anything else. For the map to be useful we’d need a comparison between ‘creative professionals’ and ‘non-creative unprofessionals’.  There’s an XKCD about this.

Peter Ellis has another visualisation of the last election that emphasises comparisons. Here’s a comparison of Green and Labour votes (by polling place) across Auckland.


There’s a clear division between the areas where Labour and Green polled about the same, and those where Labour did much better


March 24, 2016

Graphics: what are they good for?

From Lucas Estevem, an interactive text-sentiment visualiser (click to embiggen, as usual)


Andrew Gelman, whose class this was a project for, asks what the visualiser is useful for?

An interactive display is particularly valuable because we can try out different texts, or even alter the existing document word by word, in order to reverse-engineer the sentiment analyzer and see how it works. The sentiment analyzer is far from perfect, and being able to look inside in this way can give us insight into where it will be useful, where it might mislead, and how it might be improved.

Visualization. It’s not just about showing off. It’s a tool for discovering and learning about anomalies.

March 18, 2016

What they aren’t telling you is a beautiful visualisation of what news topics are less covered in your country (or any selected country) than on average for the world:


For a lot of these topics it will be obvious why they’re just not that relevant, but not always.

(via Harkanwal Singh)

March 11, 2016

Getting to see opinion poll uncertainty

Rock’n Poll has a lovely guide to sampling uncertainty in election polls, guiding you step by step to see how approximate the results would be in the best of all possible worlds. Highly recommended.

Of course, we’re not in the best of all possible worlds, and in addition to pure sampling uncertainty we have ‘house effects’ due to different methodology between polling firms and ‘design effects’ due to the way the surveys compensate for non-response.  And on top of that there are problems with the hypothetical question ‘if an election were held tomorrow’, and probably issues with people not wanting to be honest.

Even so, the basic sampling uncertainty gives a good guide to the error in opinion polls, and anything that makes it easier to understand is worth having.


(via Harkanwal Singh)

March 7, 2016

Crime reports in NZ

The Herald Insights section has a multi-day exploration of police burglary reports, starting with a map at the Census meshblock level.


When you have counts of things on a map there’s always an issue of denominators and areas.  There’s the “one cow, one vote” phenomenon where rural areas dominate the map, and also the question of whether to show the raw count, the fraction of the population, or something else.  Burglaries are especially tricky in this context, because the crime location need not be a household, and the perpetrator need not live nearby, so the meshblock population really isn’t the right denominator.  The Herald hasn’t standardised, which I think is a reasonable default.

It’s also an opportunity to link again to Graeme Edgeler’s discussions of  why ‘burglary’ is a wider category than most people realise.