Tableau: Ist der erste Film immer der erfolgreichste?

Hinter den Kulissen der Tableau Kino Tour – Teil 2: Die Filmreihen

Dies ist der zweite Teil in einer dreiteiligen Serie zu den technischen Hintergründen der Tableau Kino Tour. Teil 1 beschäftigt sich mit dem Auslesen und Nutzbarmachen der IMDb-Daten, hier geht es um Filmreihen, der dritte Teil wird sich mit den Daten zum “Tatort” beschäftigen.

In den letzten Jahren wurde es unter Filmstudios und Produzenten immer populärer, einen oder meist gleich noch mehrere Teile nachzuschieben, sobald ein Film erfolgreich war. Neudeutsch spricht man dann von einem Franchise – um nicht zu sehr in Anglizismen zu verfallen sprechen wir im Kontext der Tableau Kino Tour lieber von Filmreihen. Die Idee ist ja an sich auch nichts neues, mehrteilige Filme oder Fortsetzungen gibt es schon sehr lange. Man denke nur an “Star Wars”: Der erste Film 1977 wurde noch als einzelnes Werk konzipiert, dann wurden nach dem großen Erfolg zwei weitere Teile produziert, 16 Jahre später nochmals drei Teile, und dann wiederum 10 Jahre später nochmals drei Teile – wovon bisher allerdings erst einer tatsächlich veröffentlicht wurde, auf die restlichen beiden müssen wir uns noch ein wenig gedulden.

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Die Tableau Kino Tour 2016/17

Tableau Kino Tour

Tableau Kino Tour

Seit ich denken kann bin ich ein riesiger Kino- und Filmfan. Als andere Kinder Astronauten oder Feuerwehrmänner werden wollten, lautete mein Berufswunsch “Kameramann”. Unmengen meines Taschengeldes sind demzufolge später in die Kassen der Münchner Kinos gewandert. So war es nur logisch, dass ich später als Student versucht habe, über einen Nebenjob im Kino a) besagte Ausgaben zu refinanzieren, und b) so nahe wie möglich an der Quelle neuer Filme zu sitzen. Vom Popcorn-Schaufler hatte ich mich bis zum Teamleiter und schließlich auch zum Filmvorführer hochgearbeitet. Und trotz der damit alltäglich gewordenen Beschäftigung mit Kino und Film, haben sie bis heute ihren Reiz und ihre Faszination für mich nicht verloren. Continue reading →

How to Analyse and Make Sense of Humongous Datasets

This was the title of an invited talk I gave at MongoDB’s first public event in Germany on September 26th. MongoDB is awesome in that it is able to handle large amounts of both structured (read: relational sources) and unstructured (read: NoSQL) data. Also, the ability to integrate data from a number of disparate sources and the fast response times make it a good fit to be used together with Tableau for any kind of ad-hoc analysis task. In order to show these capabilities and also to have some fun I decided to spice up the introduction of Tableau I provided there with a little live demo of how this looks in real life. When it came to select what data to use I decided to go with movie data – a logical choice since we have the Tableau Cinema Tour coming up soon (see below). Also, one of our founding fathers here at Tableau is Prof. Pat Hanrahan, who received his first Academy Award (of three!) for the development of the RenderMan® Software that only made movies like Toy Story possible in the first place. Continue reading →

Wrap-up: 2015 AAG Annual Meeting in Chicago

I’m currently sitting at Chicago’s O’Hare airport waiting for my flight back home to Germany. In an attempt to both not forget too much of it too soon and at the same time to keep me awake so I can sleep well on the plane I will now try to craft a wrap-up of my AAG 2015. I’ll start with some details about the sessions I visited and will finish with a more general recap.
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Upcoming Event: 2015 Annual Meeting of the Association of American Geographers (AAG)

While there’s still some time until the 2015 AAG Annual Meeting kicks off in Chicago next spring the deadline for submitting papers is approaching almost here: November 20th, 2014!

As for me, I will present an algorithm I developed as part of my PhD thesis and in the course of my related research of people’s movements in urban areas:

Konstantin Greger, University of Tsukuba
A Spatio-Temporal Betweenness Centrality Measure for the Micro-Scale Estimation of Pedestrian Traffic

The spatio-temporal mobile population estimation approach I introduce here can be used to calculate an index for the pedestrian traffic volume on street segments divided into deliberately chosen time steps. This is especially useful in the spatial context of highly urbanized areas, as it provides the populations in public space as a complementary element to building populations.

This was achieved by employing a graph theory methodology, namely that of betweenness centrality, and extending it by the temporal dimension. This new model was then applied using a number of datasets that provide information about building populations and train station passenger transfers segregated both spatially and by time.

The introduction of the temporal dimension to the estimation of populations in public space allows for a micro-scale analysis of the actual population figures according to the underlying human activities. I believe that this is the most interesting characteristic of the proposed estimation methodology, since for the first time it allows for a reliable estimation of mobile populations even for large study areas with justifiable requirements in terms of both necessary input data and computational expense.

The output result of the spatio-temporal model can be used to visualize the amount of pedestrians on the streets of a chosen study area. While the data do not represent the absolute numbers of pedestrians, they do reflect the traffic volume and allow for a comparison of crowdedness, which can be used for further quantitative analyses, such as population density calculations for certain points in time.

This year I made an effort to not being placed into some random session as has happened to me both in 2012 and 2014 – in 2013 I went all the way and organized my very own session. Therefore I browsed the (admittedly a wee bit confusing) “abstract and session submission console” on the AAG conference website. There I came across an effort by Prof. Diansheng Guo at the University of South Carolina, who proposed a session (or a series thereof?) labeled “Spatial Data Mining and Big Data Analytics”. I was more than happy to receive an almost instantaneous feedback from Prof. Guo, let alone a positive one!

Obviously I don’t have details about the “where and when”s of said session(s) and my presentation, but I will update this article accordingly once the information has become available. The details are:

Paper Session: Spatial Data Mining and Big Data Analytics (2)
Tuesday, 4/21/2015 10:00 AM – 11:40 AM
304 Classroom, University of Chicago Gleacher Center, 3rd Floor

In the meantime, Here’s the general conference information:

2015 AAG Annual Meeting
April 21 – 25, 2015
Hyatt Regency Chicago

I’m already looking forward to my fourth AAG, and I would be happy to see you there!