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Eye-Tracking Unsinn von Tableau

Don’t trust everything you read. Surely you know this already. What you might not know is that you should be especially wary when people call what they’ve written a “research study.” I was prompted to issue this warning by a June 29, 2017 entry in Tableau’s blog titled “Eye-tracking study: 5 key learnings for data designers everywhere”. The “study” was done at Tableau Conference 2016 by the Tableau Research and Design team in “real-time…with conference attendees.” If Tableau wishes to call this research, then I must qualify it as bad research. It produced no reliable or useful findings. Rather than a research study, it would be more appropriate to call this “someone having fun with an eye tracker.”
— Stephen Few

Stephen Few über eine pseudo- wissenschaftliche Eye-Tracking Untersuchung von Tableau. Wenn man Stephens Art mag, dann ist sein Blogpost auf jeden Fall lesenswert.

tl;dr:

The true key learning that we should take from this so-called study is what I led off with: “Don’t trust everything you read.” I know some talented researchers who work for Tableau. This study was not done by them. My guess is that it was done by the marketing department.
— Stephen Few

 

    Source: http://www.perceptualedge.com/blog/?p=2718

    Political Data Science — Kausaler Effekt zwischen AfD Wahlerfolg und Medienberichtserstattung

    BuzzFeed Deutschland hat Daten zusammengetragen, mit denen sich der Einfluss der Medien auf die Umfragewerte der AfD berechnen lässt. Wir zeigen, dass es einen statistischen Kausalzusammenhang zwischen der Häufigkeit der Berichte über die AfD und ihren Umfragewerten gibt. Hierfür verwenden wir Granger-Causality-Analyse. Das Ergebnis ist, dass vier bis fünf Wochen, nachdem vermehrt über die AfD berichtet wurde auch die Umfragewerte signifikant steigen.
    — Simon Hegelich

    Es besteht definitiv ein kausaler Effekt zwischen dem AfD Wahlerfolg und den Medienberichten. Dieser Effekt wurde anhand einer Datenanalyse nachgewiesen. Wie genau vorgegangen wurde und mit welchen Daten gearbeitet wurde, kann im sehr ausführlichen Artikel auf Political Data Science nachgelesen werden.

    Source: http://politicaldatascience.blogspot.de/20...

    Why So Many Weather Maps Are Rainbow-Colored (And Why They Shouldn’t Be)

    There’s plenty of research that suggests the rainbow makes it harder for most of us to understand scientific data. We perceive the color spectrum not just in terms of red or blue, but through hue and brightness; some colors look lighter or darker to our eyes, meaning some colors look more different than others. Thanks to the distribution of different types of cones in our eyes, we’re pretty bad at detecting changes in color across the spectrum. For instance, as the authors of the same 2009 study explain, our eyes see yellow as more vibrant, so the yellow portion of a map will seem more dominant to us, even if the data it represents isn’t.
    — Kelsey Campbell-Dollaghan

    Toller Artikel, der sich um die Verwendung der Regebogenfarbpalette dreht. Das Verwenden des kompletten Farbspektrums wird ähnlich kontrovers diskutiert wie Kreisdiagramme (#endtherainbow). Ursprung der Diskussion war folgender Tweet des @NWS und die Aussage, dass sie die Farbpalette anpassen mussten, um die Regenmengen abbilden zu können.

    Die New York Times verwendet nur Farbschattierungen und zeigt, dass dies meinst besser funktioniert.

    Source: https://www.fastcodesign.com/90138569/why-...

    WGOITGraph?

    Quelle: New York Times
    Well, we’ve developed a way to do just that. This year, we’re delighted to announce that we’ll be partnering with the American Statistical Association (A.S.A.) for a new monthly feature, “What’s Going On in This Graph?”, or “WGOITGraph?” The A.S.A. educates the public about using data to understand our world and believes that statistical literacy is important for everyone.
    — MICHAEL GONCHAR undd KATHERINE SCHULTEN (NYT)

    Die New York Times startet eine neue monatliche Reihe: What’s Going On in This Graph? Ähnlich zum bereits bestehenden What’s Going On in This Picture?

    If you’re familiar with our popular “What’s Going On in This Picture?” series, you’ll have some idea already of how “WGOITGraph?” will work. In that feature, we partner every Monday with Visual Thinking Strategies to publish an intriguing Times photo stripped of its caption, and to invite students to come to our site and discuss what they see. On Fridays, we reveal the original caption along with related information. Teachers tell us that the exercise helps students practice their visual thinking skills and their use of evidence to support claims, across subjects and grade levels. And — just as important — they tell us it’s fun.
    — MICHAEL GONCHAR undd KATHERINE SCHULTEN (NYT)

    Die Ergebnisse der Diskussionen werden in Zukunft hier zusammengefasst.

    Source: https://www.nytimes.com/2017/09/06/learnin...

    Was sucht Deutschland zur Wahl?

    Welche Begriffe werden am häufigsten in Verbindung mit den Spitzenkandidat/innen der sieben meistgesuchten Parteien auf Google gesucht? Finden Sie es heraus! www.2q17.de

    FullSizeRender.jpg

    Schöne Visualisierung vom Google News Lab in Zusammenarbeit mit Truth & Beauty — Moritz Stefaner, Dominikus Baur und Christian Laesser. Den vertikalen Zeitstrahl auf einem Gerät mit wenig horizontaler Displayfläche finde ich besonders gelungen. 

    Chartmaker Directory

    Over the past 5+ years, during which time I have delivered more than 200 data visualisation training events to over 4500 delegates, the question I unquestionably get asked the most is ‘which tool do you need to make that chart?’.

    It is a question I often find hard to answer elegantly as it is often weighed down with the classic baggage of “it depends”. Above that, there is such variety in the ways of expressing data visually and arguably an even broader variety of tools offering the means to do so, ranging from simple solutions to the more complicated. It is a large, complex and ever-changing landscape to have to make sense of.

    With my training and, by extension, my book primarily emphasising the importance of critical thinking and the underlying craft of data visualisation - the ‘what’ and the ‘why’ - I have been seeking to substantiate this content with solid guidance about the critical matter of ‘how’.

    This is what motivated the development of the The Chartmaker Directory: an attempt to gather and organise a useful catalogue of references that will offer people a good sense of what charts can be made using which tools and, where necessary, how.
    — Andy Kirk

    Andy Kirk startet mit einem Chartmaker Directory. Ein toller Anlaufpunkt für Beispiele und Tutorials, geordnet nach Diagrammklassen und Applikationen.

    Source: http://www.visualisingdata.com/2017/07/new...

    Kontext ist wichtig!

    I use a Misfit activity tracker to count my steps. The Misfit app does a decent job of showing me step counts per day and every month, misfit also sends me a summary of the previous month’s activity. Unfortunately, the numbers in that summary are presented without any context, making that summary almost entirely useless.
    — Robert Kosara

    Robert Kosara schreibt über die Wichtigkeit von Kontext. Gerade im Business Intelligence und Planungs -umfeld sollte Kontext allgegenwertig sein. Beispielsweise ist eine monatliche Hochrechnung weniger aussagekräftig, wenn der historische Kontext oder die Zielerreichung fehlt. Daten ohne Einheiten, Kontext oder Detailinformationen sind leider nicht viel mehr als Dekoration—so wie in Robert's Fitness Tracker App.

    Source: https://eagereyes.org/blog/2017/the-import...

    Warum werden so viele Kinder um 8 Uhr morgens geboren?

    Based on the stories we share, it would be easy to imagine that when a baby is born is random. In the U.S., however, weeks in September have 5 to 10 percent more births than weeks in January. Twelve thousand babies are born on a typical Tuesday compared with 8,000 on a typical Saturday. Sixty percent of babies are born during the day, between 6 A.M. and 6 P.M. And, 3.5 times as many babies are born at exactly 8:00 A.M., the most common minute to be born, than at the least common, 3:09 A.M.

    Zan Armstrong | blogs.scientificamerican.com

    Why Are so Many Babies Born around 8:00 A.M.?

    Die schön gemachte Analyse von Zan Armstrong für "Scientific American", der sich Datenvisualisierungsunterstützung bei Nadieh Bremer geholt hat, hat es sogar über den Atlantik in die "Zeit" geschafft.

    Source: Why Are so Many Babies Born around 8:00 A.M.?

    Paper: Erzählstruktur für Data Stories

    Instead, I simply ignored all the bad stories and looked at just a very small number of the good ones. What do they have in common? It turns out, there is a common pattern for some of them. And I believe it's a very useful one: make a claim, provide evidence, conclude by tying the evidence back to the claim.

    Robert Kosara | eagereyes.org

    Paper: An Argument Structure for Data Stories

    Robert Kosara verfolgt für seine Data Stories den klassischen Ansatz einer Interpretation: "Make a point and proof it". Eine Data Story ist keine Geschichte mit Einleitung, Hauptteil und Schluss.

    Source: Paper: An Argument Structure for Data Stories