Design for understanding-Visualizing things lost on New York subway
Information visualization is a powerful way to communicate perspectives to the audience. Manipulating the design of visualizations allows them to realize two main purposes. One is ‘Analysis’: to inspect data to gain knowledge and inform decisions. The other is ‘Communication’: to disseminate or inform about findings from data.
In this design sprint, we tried to visualize the dataset of things lost and yet not found on New York Transit system between 2014 and 2015 according to the purposes of analysis and communication respectively.
There are 252 rows in the dataset and each row represents a date within 2014–08–17 and 2015–12–04. There are in total 222 columns and each column is an item category of the things lost. The values are accumulated counts of each lost item category.
To better visualize the dataset, we preprocessed the dataset by creating broader categories for the 222 item sub-categories. The dataset was reduced to only 23 categories in this way.
We went through the five-stage design process to brainstorm and decide the visualizations, layout, interactions, and animations for the two purposes of visualization.
I mainly worked on the analytics part of the visualizations. As Stephen Few said in Now You See It, “The effectiveness of information visualization hinges on two things: its ability to clearly and accurately represent information and our ability to interact with it to figure out what the information means.” Therefore, I decided to use Tableau in facilitating the analytics purpose of the visualizations as it’s perfect for adding interactions to the visuals.
To represent a whole picture of the dataset, we proposed three visualizations to communicate different perspectives of the data. Firstly, we created a time series line plot to show the trend of the number of things lost over time. Each line of a unique color represents a category of the items such as Tickets, Tools, and Keys. The audience will be able to compare the change in the number of lost items in different categories by looking at the rate of these lines.
Secondly, we visualized all the sub-categories in a word cloud. The size of the text represent the number of count for each sub-category so the audience can easily identify the most common thing to lose on subway. The color-encoding improves the aesthetics and makes it easier for the audience to differentiate categories.
Finally, we used an interactive bubble chart allowing the audience to zoom in any category at their wish. We applied a filter for the audience to select the category and the bubble chart will change accordingly to include only the items in that specific category.
After we finished the design sheets, I asked Chloe in our class for further feedback. For analytics visualization, she suggested that instead of applying the same filter on both the word cloud and bubble chart visualization, manipulating the interaction operation to make them show different perspectives fo the dataset is better. I incorporated the suggestion to our final implementation for the analytics visualization. Below is the link to the finished Tableau dashboard:
We created a demo video to showcase both the analytics and persuasive visualizations implemented on this website:
NYC Subway | Design for Understanding
The dataset we decided to use to tackle the assignment was New York City Transit Authority's dataset called Things lost…
Discussion and Reflection
The two visualizations on our website focus on totally different purposes. As the analytics tries to capture almost all the information in the dataset, the persuasive one uses an aggregated version of the data. The analytics visualizations slice the dataset in three different ways and allows the audience to explore the dataset either in a whole picture or zooming in a specific category. It aims to provide an accurate and wholistic view of the dataset. The persuasive visualization aims to use animation and aesthetics to emphasize the changes in the data overtime. Thus, it shows the increasing trend of the count in each category of lost items. The background music at the same time deepens the impression of the audience on the changing numbers.