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. 


So bewegt sich Deutschland

Telefónica NEXT hat anonymisierte Mobilfunkdaten ausgewertet, um erstaunliche Bewegungsmuster und Erkenntnisse über das deutschlandweite Pendler- und Reiseverhalten zu gewinnen. 

Quelle: Telefónica NEXT — So bewegt sich Deutschland

Quelle: Telefónica NEXT — So bewegt sich Deutschland

Die sehr ansprechenden Analysen findet Ihr hier. Besonders empfehlen möchte ich aber auch den ergänzenden Blog Beitrag, in dem die Methodik beschrieben wird, wie die Daten erhoben und analysiert wurden. 


Stravas Global Heatmap

Our global heatmap is the largest, richest, and most beautiful dataset of its kind. It is a direct visualization of Strava’s global network of athletes. To give a sense of scale, the new heatmap consists of:

1 billion activities
3 trillion latitude/longitude points
13 trillion pixels rasterized
10 terabytes of raw input data
A total distance of 27 billion km (17 billion miles)
A total recorded activity duration of 200 thousand years
5% of all land on Earth covered by tiles
Screenshot-2017-11-21 Strava Global Heatmap.png

Strava liefert ein paar Einblicke in die Entwicklung der neuen, globalen Heatmap. Beeindruckendes Projekt.


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.


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




    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.

    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.

    Die Ergebnisse der Diskussionen werden in Zukunft hier zusammengefasst.


    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.


    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 |

    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 |

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


    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 |

    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