UTS-AEI

What is the best way of reporting on data related to the environment? Where do you find the data in the first place? How do you make it relatable to the public and which challenges do you face along the way? Last October, seven experts got together on the Data Journalism Awards Slack team on to tackle these questions.

There is a lot of discussion recently about proposed changes to NSW Selective High Schools. This contentious issue may be something to consider for AT4. For example, see the articles in the Sydney Morning Herald such as this one:

On the importance of having data, in order to understand and tackle an issue... The most recent U.S. estimate...concluded approximately 513,000 women and girls at risk of genital mutilation...But that number should be taken with a big grain of salt...the data doesn't account for immigrants from countries where female genital cutting isn't studied or widely practiced...."You also can't assume that people who come to the U.S. are a representative sample of their country of origin," Clark said. That's especially problematic for estimating rates of female genital cutting, since it's not practiced uniformly within countries. It's also possible, he said, that some immigrants abandon the procedure as they assimilate....some advocates point out that although the estimates focus on immigrants, ...female genital cutting isn't new to the U.S. Female circumcision was performed as a treatment for masturbation by American physicians as recently as the mid-20th century...

or those of us who can't stand the thought of creepy crawlies or who jump at the sight of a shadow in the ocean, summer in Australia can be a challenging time. Deadly snakes, sharks and spiders - you name it, we've got it. But just how deadly are these creatures and how disproportionate is our fear of them? To find out we compiled a list of deaths commonly associated with summer.

A complex look at an important issue and what data can tell us about it, and how missing data complicates the picture.

An opinion piece in last week's Annals of Internal Medicine argues that just how aggressively we screen for some cancers can actually distort our understanding of the risk factors for a particular cancer, as well as how common we perceive it to be.

Excellent analysis from the FT (you'll need to login to view via the link) that uses knowledge of the Mean and Median to show that some companies have reported incorrect (fabricated?) pay-gap information! One in 20 UK companies that have submitted gender pay gap data to the government have reported numbers that are statistically improbable and therefore almost certainly inaccurate, a Financial Times analysis has found. Sixteen companies, each with more than 250 employees, reported that they paid their male and female staff exactly the same, that is they had a zero average gender pay gap measured by both the mean and median. Experts on pay said that it was highly anomalous for companies of that size to have median and mean pay gaps that were identical because the two statistics measure different things. The mean gap measures the difference between the average male and female salary while the median gap is calculated using the midpoint salary for each gender.

When we launched Beyond the blade earlier this year, we wanted to know how many young people and children were being killed by knives in the UK. Who are these young people being killed?, Where are they dying? Is the scale of the issue changing, and if so how? We spoke to experts about the number of children and teenagers affected in Britain and Northern Ireland. We checked with the Office for National Statistics, the Home Office, politicians, academics and thinktanks. But the answer to how many young people are dying every year, it seemed, was that nobody knows. So we started trying to find out. Until now, there has been no publicly available information about the demographic profiles of those who have died from knife attacks in the UK

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.

How excited would you be about a medication that lowered your risk of cardiovascular death, heart attack or stroke by 1.5%? Excited enough to spend a few thousand dollars a year on the drug? I expect not. What if, instead, the drug reduced those same terrible outcomes by 20%? That's probably enough benefit to interest some in the drug. Well, those statistics come from the same clinical trial, evaluating the same drug. In fact, they present the exact same results, but they simply do it in different ways.

some examples of how the 3 types of machine learning - supervised, unsupervised, and reinforcement - have already been used for journalistic purposes, and using those to explain what those are along the way. Examples include: supervised learning to investigate doctors and sex abuse; unsurprivsed learning to identify motifs in Wes Anderson films; reinforcement learning to create a rock-paper-scissors that can beat you...

The reasons for the disparity in pay vary from company to company but the broader source of the gap can be seen in the distribution of high earners by gender. Of all the companies that have reported to date the top pay quartile, the highest paid 25% of employees, is male-dominated. Almost two-thirds of the top quartile is made up of men, while conversely 57% of the lowest-paid employees are women.

When it comes to Sydney's perennial east vs west rivalry, there are plenty of opinions and stereotypes about the spray-tanned, salmon-wearing 'silver tails' and ugg boot-bound bogan 'westies'. And then there are facts. Here, we've used data to explore the actual differences between life in a selection of Sydney's eastern and western suburbs. Some of it's serious, some of it's fun. Nothing here is meant to be perjorative. Because there's no 'superior' Sydney - just different ones.

For AT2 we're asking you to analyse one of the datasets https://tinyurl.com/AEIDatasets . This piece provides some nice examples of how people are using data to do deep exploration of a topic (bike sharing) and its impact for particular stakeholders. If you were doing something on road safety/bikes, then these kinds of resources are good examples of other research you could draw on to support your own analysis of the data and to tell your data story.

The census has been used for hundreds of years to determine how many U.S. House members each state will have, and it currently helps determine how hundreds of billions of dollars in federal spending is divvied up. "The risk that really troubles me is that there's a big undercount and then there's a big lack of representation," said John Thompson, who was director of the U.S. Census Bureau until he resigned last year (the bureau is still without a director).

When it comes to inequality, a growing body of evidence shows that people across countries underestimate the size of the gap between the rich and poor, including their wages. This can undermine support for policies to tackle inequality and even lead to apathy that consolidates the gap. But how exactly are existing perceptions of inequality measured by social scientists?

Public sector employers with more than 250 staff are legally obliged to publish their gender pay gap by Friday, while private firms and charities have until Wednesday 4 April. About 7,000 of a estimated total of 9,000 organisations had filed results by Thursday.companies have filed mathematically impossible figures - at least 17 have reported a bonus gap of more than 100%. One company reported an hourly mean gender pay gap of 106.4%, implying that for every £100 earned by a man a woman would "pay" £6.40. A spokesperson at the company declined to comment.

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.

Interesting links to different sources discussing data on teacher pay, and what it shows in relation to different things that stakeholders might care about. educator pay varies significantly across states, from an average of $44,921 in Oklahoma to $77,957 in New York. Why should school administrators and government leaders care about teacher pay - beyond wanting their employees to be able to afford their living expenses? Below, we present research that examines this issue. What scholars have found is that teacher salaries are linked to employee retention and that better pay seems to draw smarter people to the field and into the classroom. It's not clear, however, whether higher salaries result in higher student achievement.

It's not just disadvantaged and vulnerable groups that struggle with financial decision-making. People who are highly educated in finance also make poor decisions - for instance, by focusing too much on growing their assets and ignoring risks. But studies show that when regulation is effective and the financial system can be trusted, even consumers with limited financial knowledge and information-processing capabilities have the potential to deal with complex financial decisions. For example, when considering mortgage protection insurance, applicants stand to benefit from knowing the actual risk of events like serious illness or injury that can affect their ability to meet monthly loan repayments.