{"id":1039,"date":"2016-10-21T20:24:15","date_gmt":"2016-10-22T03:24:15","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f16\/?p=1039"},"modified":"2016-10-21T20:25:47","modified_gmt":"2016-10-22T03:25:47","slug":"wordle-on-the-nyc-tenements","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/21\/wordle-on-the-nyc-tenements\/","title":{"rendered":"Wordle on the NYC Tenements"},"content":{"rendered":"<p>Creating visualizations are often hard if the data being used is tricky. This is exactly what happened with this week&#8217;s blog post. I decided I would attempt to get a head start in our big project by using my data, as it would give me an excuse to really look into the data and make a visualization. My data consisted of about 1100 photographs of New York City Tenements taken by inspectors between the years 1934-1938. The issue with the data is that instead of there being\u00a0a hyperlink for each photograph, there is a permalink that takes you to the collection website and shows you only that individual photograph (there is no scrolling function on the archive database). Additionally, because the label on all of them are &#8220;NYC Tenements&#8221; and there are only 5 different year options, I decided to use the notes. The notes, on the other hand, had a lot more information that could actually be used to create a visualization (disclaimer: I am sure I can create better digital representations of my data once we have moved further along in the course).<\/p>\n<p>In the notes, there was information about the picture itself, such as &#8220;baby sitting on a bed&#8221;, generic information about what the photograph showed, such as &#8220;storefront&#8221;, and even the address of where the photograph was taken. With this, I copied all of the notes and pasted them onto the <a href=\"http:\/\/www.wordle.net\">Wordle<\/a>\u00a0database. While I waited for Wordle to create a &#8220;word cloud&#8221; of the most common words found in the description notes of over 1100 data entries, I expected to see words like &#8220;storefront&#8221; or &#8220;child&#8221; or &#8220;st (because of the addresses&#8221; be bigger than the rest. Instead, it made me think about a whole other aspect of my data that I had not even considered exploring.<\/p>\n<p style=\"text-align: left\">When the cloud arrived, these were the huge words: Manhattan, Brooklyn, and Bronx. That&#8217;s when I thought that maybe instead of focusing so much on what was in the picture, I could categorize them according to where in New York the picture was taken. I already had previous knowledge that those were neighborhoods in which immigrants at that time flooded to, and thought that could have something to do with why the photos showed small enclosed spaces with big families, crowded storefronts in building corners, tall buildings with many windows signaling many apartments, etc. Thanks to this word cloud, I was able to see that most of these photographs were taken in 3 <em>specific<\/em> neighborhoods, where before I was too busy focused on what each photograph contained. Now with this new outlook on my data, I can attack it in a way that is organized and much easier to manage. In other words, <strong>Online Visualizations-1 Excel Sheet-0<\/strong><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-1040 alignnone\" src=\"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/Screen-Shot-2016-10-21-at-7.44.47-PM-300x199.png\" alt=\"screen-shot-2016-10-21-at-7-44-47-pm\" width=\"567\" height=\"376\" srcset=\"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/Screen-Shot-2016-10-21-at-7.44.47-PM-300x199.png 300w, https:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/Screen-Shot-2016-10-21-at-7.44.47-PM-768x509.png 768w, https:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/Screen-Shot-2016-10-21-at-7.44.47-PM.png 832w\" sizes=\"auto, (max-width: 567px) 85vw, 567px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Creating visualizations are often hard if the data being used is tricky. This is exactly what happened with this week&#8217;s blog post. I decided I would attempt to get a &hellip; <a href=\"https:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/21\/wordle-on-the-nyc-tenements\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Wordle on the NYC Tenements&#8221;<\/span><\/a><\/p>\n","protected":false},"author":51,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"image","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1039","post","type-post","status-publish","format-image","hentry","category-uncategorized","post_format-post-format-image"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1039","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\/51"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=1039"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1039\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=1039"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=1039"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=1039"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}