In this post, we are going to present the current state of the visualizations that we have created for the movies dataset extracted from TMDb to answer the questions of our project. Previous iterations on these visualizations can be found in our previous posts of past weeks.
Visual #1
With this visualization we intended to see how the different variables of our dataset evolve over time (i.e. which genres have been more prevalent, what time of the year is more profitable...). To do so we created a visualization that combines a heatmap of different variables with axes for the year and month of release of movies, which allows to look for trends over time and during the months of the year, and a point plot by year of the different genres, which allows to see how the variables evolve and see if there are any differences for the genres of movies.
To make the visualization interactive a widget is included to change the variable that is represented. The variables included are:
Revenue: Box office of the movie
Popularity: Popularity on the site
Runtime: Length of the movie
Budget: Total cost of the movie
Count: Number of ratings by user received
Rating: Average rating of user votes
You can see our implementation in Vega here.
Visual #2
In our second design we aimed to find the actors that usually work together. To visualize that information we chose a static network with the names of the actors displayed in a circle. Each actor is simbolized with a circle whose size represents the level of popularity. Moreover, the width of the arc linking two actors depicts the number of movies they have worked together. We also made the graph interactive as it is possible to select one actor to highlight his connections. In the next step we would like to use the position of the nodes to include an extra dimension. You can see this visualization implemented in Vega here.
Visual #3
In this visualization we have a goal similar to the one in the previous one but now we mix actors and directors. This time we use a force-directed graph which is an example of a dynamic network. The difference is that the nodes do not have a defined position so it is possible to visualize the structure of our data. For example, directors that work with the same actors have a close position in our network as they are attracted by the same nodes.
Again, we used the width of the edges to represent the number of movies and the size of the nodes for the popularity. Moreover, we used a different colour for the directors. We plan to try with more points and also include the possibility to zoom and pan in the network. For more details, you can see our implementation in Vega here.
Visual #4
In our attempt to exploring factors associated with the popularity and the rating of the movies, we have been working on a circle packed visualization. Each movie is a circle, its size represents its revenue, the color hue differentiates among genres, and all the movies for the same genre are packed in a bigger circle.
Additionally, we have added a selection bottom where the user can choose a decade when the movie was released. This bottom let us distinguish the more profitable movies and the most common genres within each decade. You can access to the visualization here
Our next step is to include an additional bottom for the categorized popularity variable and thus we can have an idea of how these variables are connected together and let us have more insights about the association.
Visual #5
This visualization presents how many female and male actors have participated in leading roles across time. The next step is to include this visualization for every genre of movies so we can get an idea of the general pattern. Here you can check the visualization:
Finally, we want to link the popularity and gender of the protagonist of the movies over time. For this, we have proposed this visualization so far. We will be working on adding a bottom to select each gender as well as each gender of movies to see how the trend is. Here you can check this visualization.
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