I used our group dataset, titled NY Tenements. Our dataset catalogues links to photo records, in addition to date, some locations of photographs and content. A record in this dataset is made up of the categories Item URL, Note, Subject Topic, Date, Volume and Title. The data is pretty raw and hard to interpret.
Originally, I was planning on creating a gallery visualization of the photo collection through Palladio. However, because the links are to records of the photo, not of the photo itself, I had to scrap the idea. I can already see potential problems working with this data, as there is not much wiggle room to experiment with different data visualizations.
Fortunately, I was able to make a simple data visualization with Wordle. Wordle is a straight forward and easy to use platform that analyzes large amounts of text and creates word clouds. Words that appear with greater frequency are featured in larger sizes. After generating the word cloud, you can tweak it with different font sizes, colors, directions and layouts.
Before creating the visualization, I wasn’t able to see any patterns in the dataset. I only saw that the dataset included tenements located in New York and that they were taken in 1934. After generating the visualization I was able to see a few prominent things. First of all, most of the tenements are located in the Manhattan and Brooklyn area. A few of the tenements are in the Bronx area. In addition, there seem to be a large number of storefronts in comparison to apartments. The majority of photographs also seem to display vacant places or places made of brick.
These conclusions were definitely not apparent upon first glance. It impresses me that a tool as simple as Wordle can generate something with so many insights. I can see that the possibilities are aplenty for creating a narrative from this project and I think this platform is a great starting point for anyone looking to get a big picture view of their data. For example, we could correlate tenements listed with vacancies and locations with possible data on evictions in the area. We could also correlate the storefronts and their locations with storefronts in the same locations today to see how ownership has changed over time. We could also examine the vacant tenements to explore whether more apartments or storefronts stood empty.
I loved the insight you had for your data visualization and how useful it can be for your final project. You can gather a lot with the words that they pulled out to create the word cloud. This started trending on Facebook for a period of time (where they analyzed your statuses and captions to create this) and it was really popular because of how simple it is to interpret and how visually appealing it is. I noticed that there are some repeat words/ phrases which may mean you should filter for words or change the wording so that it is all lowercase or the first letter is uppercase (so that it is consistent). Good luck with your final project!
Great post! I never really thought of word clouds as being super useful in general, much less being useful for analyzing data about photographs; however, your creation of a word cloud using the subject type (and maybe notes?) is really smart. You can really get an idea about the subject matter and come up with possible research questions without even having to look at every single photo first.
I enjoyed how you went through your initial thought process and explained why it wouldn’t have worked with this particular dataset. A word cloud was a very interesting choice of visualization. I would have never thought to create a Wordie to figure out where most of the tenements were located, like you did.