Visualizing Movie Data

In 2008 we began reviewing movies. In the beginning we were doing that using points that lay between 0 and 10. At a later date we used a more narrative way. That led to detailed reviews of movies that we posted on Facebook. But over time was that too much work and it cost too much time so I decided to introduce a more accurate way of reviewing the movies. I started on 5 January 2015 to use 13 categories for each movie. Storyline, originality, cinematography, involvement, sound, editing, educational, title design, acting, interesting, unusual, exciting and superior. Every category earns a score between 0 and 10. A simple Processing program add’s all points together. And the result of this addition is divided by the number of categories. Then you get an average point for one movie. The aim of Visualizing Movie Data is to give us more insight into the choices we make in evaluating a film. Ultimately, this should tell more about ourselves than about the films.

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Films by flags The idea is very simple. From which countries did we see films during 2015? Represent the number of movies with the flags of those countries.

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Small comparisons At this stage of our Visualizing Movie Data project, I want to see how far you can go with a reduced view of our movie data. How far can you reduce the decoration in exchange for the functional display of data. However, the readability must remain intact. What kind of problems can we expect? And perhaps more importantly … what are the solutions to those problems. In fact, the charts I post ever week on Facebook are my starting point. This is just a smaller variation of it.

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Reviews by categories As a next step, I find it interesting to see what our reviews are telling us when I show each category of a movie. I can imagine that the titles of the films are on the left. Suppose we start from the first 100 movies we have watched from the beginning of 2015? What does it look like? And what conclusions can we commit to? I’ll try programming this version slightly smarter than the earlier version. A quick conclusion. I am tempting to say that if a film did not score one 8, 9 or 10 in the assessment it would be not a good movie. That means it is of a lower level than films who scored at least one 8. Or one 9. Or one 10. This visualization is showing the worst films of all 100 films we have seen since the beginning of 2015. In total these are only 27 movies. So a little over a quarter. That means that three-quarters of the 100 films that we have seen always had something of good quality in them. And that’s very reassuring. For the filmmakers, the film industry and for us.

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Waltzing with bezier When I started this assignment I was interested in how much money is actually going on in the film industry. What costs a movie? What is the budget? How much money does it produce? And how do these figures compare with our ratings. I thought it was easy to check the data on the site of IMDb. But unfortunately all I found was very incomplete data. I checked all 150 films that we have seen since January 2015. And guess what. There are only 56 films that both show you the budget and the profits. In addition, all amounts are mentioned in different currencies. So I have to convert them to dollars or an other currency unit. Additionally, in all the movies descriptions that are not from the United States, there is almost no sign of costs and benefits to find. So I have to check at other websites if there is additional information.

After that extensive check this resulted in 69 films with complete financial information. I think I should leave out the series. These often run over several years and are applying varying budgets. While a film only runs once and receives just one budget. Another thing is that these figures represent only periods when movies are played. Some play longer periods than others. Because they are more popular they bring in more money. But that says nothing about the quality. Our list shows that there are only three films made which costs less than one million dollar. However, there are 14 films which benefits less than 1 million.

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Time Series A dozen years ago I heard the word ‘ubiquitous’ for the first time. I wondered for what it stands for. Looked it up and the word ‘ubiquitous’ means: present, appearing, or found everywhere. So these time series graphs are a type of graphs that you can find anywhere. Because this project is about visualizing our movie data I need three or more data sets. The idea is that I will compare these data sets with our own data set. I hope to find out how our qualifications relate to, for instance, the IMDb (Internet Movie Database), Metacritic and/or Rotten Tomatoes. Suppose I would like to see the first one hundred films compared to results of these websites I should be able to draw the necessary conclusions.

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Mapping Movie Data After some research it seemed suitable to me to use the film locations of the series ‘Breaking Bad’. I display the abstract version of the map of Albuquerque in the background. And I used a reduced version of the Breaking Bad wordmark. But that doesn’t work at all. I also find the amount of film locations insufficient. After some more research I could trace a lot more ‘Breaking Bad’ film locations. I changed the title and background colors. But I think it’s really ugly. When something is designed simple and basically it doesn’t have to look ugly. So I have to work on that. This is the state in which I want to finish this exercise for the time being. All locations can now be read and the amount of scenes are shown after the location name. There is a title and a subtile. You can immediately see that Walter White’s House is the film location which is most used (with 80 scenes). Then Jesse Pinkman’s House follows (with 42 scenes). Hank and Marie’s House (with 29 scenes). The DEA offices (with 27 scenes). The Car Wash (with 22 scenes), Jesse Pinkman & Jane’s House (with 21), Gus’s Laundry Service (with 20) and Los Pollos Hermanos (with 17).