Posts filed under Graphics (394)

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.

Unfiltered.news 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)

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)

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.

This graph, from the Wall Street Journal last year, was one of a series showing the impact of vaccination

Randall Olson looks at ways to redesign it to communicate the numbers more effectively.

Via Jolisa Gracewood on Twitter, a graph from Stanford sociologist Michael Rosenfeld on how people met their partners (click to embiggen)

Obviously the proportion who met online has increased — in the old days there weren’t many people on line. It’s still dramatic how fast the change happened, considering that ‘the year September never ended’, when AOL subscribers gained access to Usenet, was only 1993.  It’s also notable how everything else except ‘in a bar or restaurant’ has gone down.

Since this is StatsChat you should be asking how they got the data: it was a reasonably good survey. There’s a research paper, too (PDF).

You should also be worrying about the bump in ‘online’ in the mid-1980s. It’s ok. The paper says “This bump corresponds to two respondents. These two respondents first met their partners in the 1980s without the assistance of the Internet, and then used the Internet to reconnect later”

Two graphs to help people on Twitter who are arguing about home ownership trends in Auckland vs rest of NZ or in generational differences.

Both are percentages of home ownership based on the census question “Do you own or partly own your home?”, with data from the last three censuses.

First, comparisons between Auckland and the Rest of NZ by age, over time. Blue is Auckland, pink is RONZ

Second, trends over 12 years, by age, for three census years. Blue is 2001, pink is 2006, green is 2013.

Data from the nzdotstat table “Tenure holder by age group and sex, for the census usually resident population count aged 15 years and over, 2001, 2006 and 2013 Censuses (RC, TA, AU)”

Update: And one more. Here the lines connect roughly the same group of people (birth cohort) over time (only approximately because the planned 2011 census didn’t happen until 2013).

From One News, tweeted by various people:

The y-axis label is wrong: this has nothing to do with change, it’s percent support.

The x-axis label is maximally unhelpful: we can guess that the most recent poll is in February, but what are the earlier data? You might think the vertical divisions are months, but the story says the previous poll was in October.

Also, given that the measurement error is large compared to the expected changes, using a line graph without points indicating the observations is misleading.

Overall, the graph doesn’t add to the numbers on the right, which is a bit of a waste.

Every summer, the Department of Statistics offers scholarships to a number of students so they can work with staff on real-world projects. Oliver, right, is working on visualising conservation data with Associate Professor Rachel Fewster. Oliver explains:

“This summer project is called Maps, graphs, and data analysis for community conservation projects, and builds on my Honours project from the past year. It involves developing interactive applications that automate the display of catch data from various conservation projects around New Zealand.

“The aim of this project is to allow volunteers to engage with their data in more depth than ever before. After a day in the field, a conservation volunteer is able to go online and use these applications to produce maps or graphics to view their day’s work. The graphics illustrate exactly how a volunteer’s work is impacting their local environment and will ideally keep them motivated to continue with what they are doing.

“I graduated from the University of Otago in 2014 with a Bachelor of Science majoring in Statistics and minoring in Psychology, before completing an Honours degree in Statistics at the University of Auckland in 2015. In 2016, I plan on pursuing a Master of Science in statistics, completing this degree as a research masters.

“I enjoy statistics due to its numerous applications. Nowadays, data exists in almost every facet of life, and wherever there is data, we can use statistics to try and gain a deeper understanding of what is really going on around us.

“In my spare time this summer, I will, hopefully, be able to watch the Black Caps as they continue with their summer of cricket.”

Every summer, the Department of Statistics offers scholarships to a number of students so they can work with staff on real-world projects. Hubert, right, is working on ways to graphically represent community conservation efforts with Associate Professor Rachel Fewster. Hubert explains:

“Conservation efforts are needed to protect the natural flora and fauna of our beautiful country. This exciting project involves preparing and analysing data collected from volunteers involved in conservation efforts against pests such as rats.

“The data is analysed and uploaded to a website called CatchIT, which is an interactive website that allows the bait and trap information to be presented in graphic form to volunteers, which provides feedback on their pest-control efforts. The data comes to life on the screen, and this engages current and future volunteers in tracking the success of their pest-control projects.

“I am in the final year of my Bachelor of Science majoring in Statistics and Biological Science, having previously finished a Bachelor of Pharmacy (Hons). Statistics has a wide applicability to a wide range of disciplines, and appeals to me because I am passionate about the simple process of getting the most from raw data. It is a very rewarding process knowing that you can make the data more appealing and important to the end user.

“This summer, besides doing this studentship, I’ll be enjoying the sunshine, and relaxing on the beach with family and friends.”