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<\/p>\nOur group was assigned the Museum of Modern Art (MOMA) datasets. The two separate datasets contain records corresponding to pieces collected by the museum from 2006-2016, including the artists of those pieces (gender, ages, nationalities) and artworks (color composition, size).<\/p>\n
I chose to work with the artists dataset. My group hasn’t begun data cleaning, so it was easier\u00a0to work with the artists dataset, which was much smaller, had fewer complications (like empty cells, language issues, repetitions).<\/p>\n
Being new to data visualization, I chose to keep my visualization small and simple:<\/p>\n
<\/p>\n
Using Tableau, I created this bar graph as a quick and simple way of visualizing\u00a0the gender ratio of artists whose works are collected by the museum. The bar graph makes it clear that there are far more male artists featured than female.<\/p>\n
I designed this bar graph\u00a0with Nathan Yau’s principles of data visualization, as outlined in\u00a0Data Points<\/em>, in mind. Looking at the data in an excel spreadsheet, you cannot determine the disparity between male and female artists easily, for there’s just so much data to work with. If the point of a visualization is to communicate an idea that is not completely obvious in the dataset in a clear, simplistic way, I think my bar graph gets the job done.<\/p>\n The first principle I focused on was length. When comparing the two variables male and female (I’ll get to null later), a bar graph with a clear difference in length communicates the thought that female artists\u00a0are not as highly represented in the collections as men. The lengths of the bars are fitting, with the male bar being a little under five times as large as the female, visualizing the data which shows that there are almost 5 times as many male artists as female (9,792 male vs. 2,171 female). I even included the actual values at the top of the bars to emphasize this point.<\/p>\n The second principle I focused on was direction. English speakers read left to right, thus I thought it was appropriate to orientate the graph in an increasing left-to-right manner. This aides the viewer and is an accurate way of representing the data.<\/p>\n The decision to leave in the “Null” data was difficult. Null represents artists for which their gender is unknown or left out of the dataset.\u00a0When the data is cleaned for the final project, we might choose to leave it out, depending on what narrative we are trying to convey. On the one hand, leaving null\u00a0distracts from the purpose of the visualization. However, to simply ignored this data in the visualization would not be an accurate representation of the dataset. Thus, I left the data in to remain true to the dataset.<\/p>\n Overall I’m proud of my data visualization and my first experience with Tableau. I realize it’s not very flashy, can easily be made using excel, and is not very complex. I think it visualizes the thought I am trying to convey, which is good enough for now. Going forward it would be interesting to view this data using different mediums. I think a pie chart could convey the same message, but I steered clear of a pie chart because of what Professor Posner mentioned in lecture (data viz people hate pie charts). It’d also be interesting\u00a0to experiment with different color compositions, whether changing the color of each individual bar would aid the viewer or distract them from the overall message.<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Our group was assigned the Museum of Modern Art (MOMA) datasets. The two separate datasets contain records corresponding to pieces collected by the museum from 2006-2016, including the artists of … Continue reading “Week 4: Data Visualization for MOMA”<\/span><\/a><\/p>\n","protected":false},"author":68,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1055","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1055","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/users\/68"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=1055"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1055\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=1055"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=1055"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=1055"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}