Group items tagged

At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes - and other people may be able to learn from them, too. After a deep dive into our archive, I found several instructive examples. I grouped our crimes against data visualisation into three categories: charts that are (1) misleading, (2) confusing and (3) failing to make a point. For each, I suggest an improved version that requires a similar amount of space - an important consideration when drawing charts to be published in print.

The Guardian datablog has a set of visualisations https://www.theguardian.com/technology/data-visualisation and a set of stories focused on the Australian context; useful for exploring how data analysis and visusalisation are used to tell a story.

How do we measure illegal activity? How do we estimate the size of the 'black economy'? Some nice visualisations in this report. The Australian government has announced a taskforce to "crack down on the black economy", with a panel reportedly to consider measures such as removing the $100 note from circulation and limiting cash transactions above a certain limit. One estimate of the underground economy from 1999, which only considered cash transactions and excluded illegal activities, put the size of the underground economy at around 15% of gross domestic product. However, a more recent estimate by the Australian Bureau of Statistics (ABS) in 2013, which encompassed proceeds from illegal activities as well as other areas, estimated the size to be far smaller, at only 2.1% of GDP.

This is a really great example of using a visualisation to communicate a quantitative fact check. This claim is a good case for doing a basic plausibility check, and thinking about what numeric information you'd need to know to understand the claim (e.g., how many people are deported now (what's the baseline), and what are the estimates for the maximum number of unauthorized immigrants in the country?).

Different ways to use visualisation to gain insight onto the same issue (the US midterm elections)

The "What's going on in this graph' series is from the NYT Learning Network, and is about interpreting graphs that represent real data to tell a story. It's aimed at high school students but that just means the examples and explanations are a really great introduction to visualising and interpreting data!

Mona Chalabi (@MonaChalabi) is the Data Editor of The Guardian US and a columnist at New York Magazine. Mona uses hand-drawn visualisations and data stories to make the numbers more relatable. Before getting into journalism, Mona worked in the nonprofit sector, first at the Bank of England, then Transparency International and the International Organization for Migration. 30 minute podcast interview, with some great examples of why data matters, and how to tell stories with data.

Very cool visualisation, showing the power of thinking in manageable numbers. If Australia were just 100 people, what would it look like? New census data gives us an opportunity to find out, and provides some surprising insights into the state of the nation.

Is income inequality growing (in America)? This article discusses the issue, and uses a chart from the Financial Times (and Pew research data) to demonstrate the change since 1971. Great visualisation and great discussion of what it shows.

A great visualisation of the income distribution (using a frequency distribution) to illustrate the change over time.

Some fantastic visualisations in this piece from the Guardian, including a scatterplot and some different kinds of histograms! Well worth exploring. "The figures reveal men are paid more than women in 7,795 out of 10,016 companies and public bodies in Britain, based on the median hourly pay. Across the companies and organisations that had filed by 8am on Thursday, eight out of 10 had a gender pay gap. While the figures do not reflect equal pay for equal work, they do raise questions about structural inequalities in the workforce and may hold the answer to closing the gap."

Ourworldindata is a great website discussing lots of different datasets about global issues. This example data-story discusses the issue of global health, giving an overview (and lots of great visualisations), and discussing how we actually measure 'health' (life expectancy, quality of life measures, etc.).

'Killer facts' are those punchy, memorable, headline-grabbing statistics that make reports special. They cut through the technicalities to fire people up about changing the world. They are picked up and repeated endlessly by the media and politicians. They are known as 'killer' facts because if they are really effective, they 'kill off' the opposition's arguments. The right killer fact can have more impact than the whole of a well-researched report.

Any great story means visualization and detail. It takes the small additions of those details to build a picture in someone's mind to truly make the story complete. The same goes for analytics and data. Data is just a collection of numbers until you turn it into a story. Showing reports and dashboards can be overwhelming without adding a narrative to the data. Any great insight explains what happened, why it is important and how you can use it to turn it into something actionable. Data visualization is using data and statistics in creative ways to show patterns and draw conclusions about a hypothesis, or prove theories, that can help drive decisions in the organization.

For the 11 million Australians with private hospital cover, premium rises are nothing new. The 3.95% average increase on April 1, 2018 will be the seventeenth consecutive year in which insurance premiums have been hiked up. Health insurance premiums have increased by an average of 5.35% per year since 2000, which is significantly more than wage growth, meaning that households are spending a larger share of their income on health care.

How should statisticians, journalists and designers reveal uncertainty in graphics for public consumption? Great short (12 page) essay with loads of examples and figures illustrating how we deal with uncertainty, and some of the problems people face when interpreting uncertainty - for example, when you see a map showing the path a hurricane is expected to take, how do you interpret the line? What does it mean?

Nice little interactive piece from the NYTimes - they've charted sets of data from the Bush presidency, can you accurately extend their line charts to show the change over the Obama years?

The histogram is one of my favorite basic chart types, because it lets you quickly see the shape and distribution of a dataset. However, a lot of people don't know what a histogram shows or how the chart works.

An excellent visual data story describing the gender pay gap (in America) and debunking the claim that there is no real difference in the amount men and women get paid. The pay gap varies depending on the occupation, working hours, education attainment, experience, and geography. That explains part of the difference in pay between men and women, but not all of it. And even though most economists agree that after adjusting for age, education, experience and other variables there's still an unexplained gap, there are voices who argue that the gender pay gap is a myth. Pay gap deniers purport that women's choices, rather than discrimination, cause the pay gap between women and men. But those choices are actually consequences of the social forces at play.

Wrapping your head around the scale of a global pandemic is not easy, and the volume of stats and data can be bewildering. What, for instance, are we to make of the fact Australia recorded just 109 new cases in its daily count for April 6? Given this figure peaked at around 400 new cases per day, does this mean the rate of infection is now tapering off? And what, apart from sadness, are we to make of more gruesome statistics, such as the 969 COVID-19 deaths reported in a single day in Italy on March 27? To help interpret and understand the mountains of COVID-19 data, we'll look at five commonly used methods, and explain the pros and cons of each.