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So Far, VR-Enabled Data Visualization is Nonsense

Few data technologies are subject to more hype these days than VR-enabled data visualization. I have never seen a single example that adds value and therefore makes sense. Those who promote it don’t base their claims on actual evidence that it works. Instead, they tend to spout a lot of misinformation about visual perception and cognition. Those who have actually taken the time to study visual perception and cognition could take each of these claims apart with ease. VR has the cool factor going for it and vendors are capitalizing on this fact. www.perceptualedge.com

So Far, VR-Enabled Data Visuailzation is Nonsense

Auch in meinen Augen ist VR/AR eine neue Technologie, die sich noch beweisen muss. Mir ist ebenfalls noch keine VR- Datenvisualisierung untergekommen, die wirklich Mehrwert bietet. Jedenfalls ist es völlig unnötig, etablierte 2D Visualisierungen einfach im 3D Raum abzubilden und so sich neue Erkenntnisse zu erhoffen.

Source: So Far, VR-Enabled Data Visuailzation is Nonsense

Source: http://www.perceptualedge.com/blog/?p=2865

Strava Heatmaps — Datenvisualisierung verrät Geheimnisse

Einen besseren Beweis, dass Datenvisualisierungen Informationen erst zugänglich machen, kann man sich nicht ausdenken. Strava, Anbieter einer Fitnesstracking App, visualisiert in einer groß angelegten Aktion anonymisierte Laufstrecken und Fahrradrouten seiner Nutzer auf einer globalen Heatmap. 

 Bildquelle:  The Guardian

Bildquelle: The Guardian

Problem ist nur, dass sich Läufe und Radtouren der Nutzer im "Nirgendwo" finden lassen, und somit auf "geheime" Einrichtungen — wie Militärbasen — hindeuten. 

Source: https://www.theguardian.com/technology/201...

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

    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...

    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.?

    How to measure success in data journalism and other tips

    Last month we put 45 data journalism experts from around the world in a room in central London and got them talking about today’s challenges in the world of data-driven storytelling. This article is a roundup of what we’ve learned at this one-day event hosted by the BBC.

    Marianne Bouchart | medium.com

    How to measure success in data journalism and other tips from experts at the Data Journalism Unconference 2017

    Quelle: medium.com

    Die Ergebnisse des Workshops ist nicht nur für Datenjournalisten interessant.

    Your reports must be racetracks

    A bar or a line remains geometry that has to be decoded. To understand numbers, we need the left side of our brain that is made for abstract and logic thinking. Seeing profit and loss in bars and lines brings the two sides of our brain in conflict.

    Dr. Nicolas Bissantz | blog.bissantz.de

    Your reports must be racetracks I

    Dr. Nicolas Bissantz mit einem interessanten Ansatz. Anstelle einer visuellen Tabelle mit Incell Grafiken bzw. Microcharts zu verwenden, schlägt er vor, die Zahlen selber zu formatieren. Wichtiges soll ausschließlich über Größe und Farbe hervorgehoben werden. Er greift dabei auf Erkenntnisse aus dem Rennsport und der Neurologie zurück.

    Source: Your reports must be racetracks I

    Lisa Charlotte Rost: Warum visualisieren wir Daten?

    Finding a definition for data vis is ok-ish easy: Data vis represents data with visual elements to communicate information. Today I want to focus on a part of the data vis definition that is a bit overlooked: The part that tries to answer my favourite question of them all: “Why?”

    So yes, why. Why the heck are we actually visualising data?
    — Lisa Charlotte Rost

    Warum visualisieren wir überhaupt Daten? Lisa Charlotte Rost gibt in fünf Schritten hervorragende Antworten

    Source: about:blank

    Tapestry 2017 - 10 Storytelling Präsentationen als Videostream

    The 5th annual Tapestry Conference was held on March 1st, 2017, and over 100 invitees from journalism, academia, government and both the non-profit and for-profit private sectors gathered at the beautiful Casa Monica resort in St. Augustine, Florida to discuss the emerging discipline of data storytelling.

    Below are the ten presentations from the one-day event that spurred rich conversations both in-person as well as on social media, and also inspired wrap-up blog posts by attendees Catherine Madden, Francis Gagnon and Andy Kirk.
    — Tapestry Conference Blog

    Zehn "Storytelling" Präsentationen als Video von der eintägigen Tapestry Konferenz 2017 stehen als Stream zum Anschauen zur Verfügung. 

    Source: http://www.tapestryconference.com/blog/201...

    How to Fail - Fast! Reportingprototypen mit Stift und Zettel

    […] when you start a graphing project, how often do you pull up Excel, Google Sheets, Tableau, or whatever graph-o-matic as your first step and just start clicking around?

    If you’re anything like me, that’s usually a recipe for disaster. Suddenly you’re perfecting an already-spectacular y-axis while ignoring the fact that you’ve chosen the wrong graph type and will eventually have to start over.
    — Andrea Robertson

    Ein Plädoyer für Stift und Zettel von Andrea Robertson. Nutzt Ihr auch auch Skizzen für Prototypen oder experimentiert Ihr direkt mit Software?

    Source: https://hypsypops.com/how-to-fail-fast/