{"id":1325,"date":"2016-10-24T23:03:33","date_gmt":"2016-10-25T06:03:33","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f16\/?p=1325"},"modified":"2016-10-24T23:03:33","modified_gmt":"2016-10-25T06:03:33","slug":"new-york-tenements","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/24\/new-york-tenements\/","title":{"rendered":"New York Tenements"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-1326\" src=\"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-1-300x199.png\" alt=\"wordle\" width=\"585\" height=\"388\" srcset=\"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-1-300x199.png 300w, https:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-1-768x508.png 768w, https:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-1.png 1021w\" sizes=\"auto, (max-width: 585px) 85vw, 585px\" \/><\/p>\n<p>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.<\/p>\n<p>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.<\/p>\n<p>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.<\/p>\n<p>Before creating the visualization, I wasn\u2019t 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.<\/p>\n<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 &hellip; <a href=\"https:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/24\/new-york-tenements\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;New York Tenements&#8221;<\/span><\/a><\/p>\n","protected":false},"author":49,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1325","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\/1325","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\/49"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=1325"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1325\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=1325"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=1325"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=1325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}