{"id":1220,"date":"2016-10-24T19:09:18","date_gmt":"2016-10-25T02:09:18","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f16\/?p=1220"},"modified":"2016-10-24T19:10:10","modified_gmt":"2016-10-25T02:10:10","slug":"1220","status":"publish","type":"post","link":"http:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/24\/1220\/","title":{"rendered":"NY Tenements &#8211; Word Cloud Visualization &#8211; Week 4"},"content":{"rendered":"<p>&nbsp;<\/p>\n<figure id=\"attachment_1219\" aria-describedby=\"caption-attachment-1219\" style=\"width: 721px\" class=\"wp-caption alignnone\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1219\" src=\"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-2-300x199.png\" alt=\"Data Visualization\" width=\"721\" height=\"478\" srcset=\"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-2-300x199.png 300w, http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-2-768x510.png 768w, http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-2-1024x679.png 1024w, http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-2-1200x796.png 1200w, http:\/\/miriamposner.com\/classes\/dh101f16\/wp-content\/uploads\/sites\/5\/2016\/10\/wordle-2.png 1230w\" sizes=\"auto, (max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 984px) 61vw, (max-width: 1362px) 45vw, 600px\" \/><figcaption id=\"caption-attachment-1219\" class=\"wp-caption-text\">Data Visualization<\/figcaption><\/figure>\n<p>For the purposes of this week&#8217;s assignment, I chose to use Wordle.net to create a visualization of our dataset &#8211; NY Tenements. \u00a0The dataset itself is a photo record of the New York tenements taken by inspectors during the 1934-1938 period. \u00a0The photos and any related records were gifted to the New York public library and archived in series of eight volumes, most of which were digitized or placed on microfiche, after which the nitrate based original negatives were destroyed. \u00a0A challenging feature of the dataset is the scarcity of information contained in each individual record &#8211; the majority of the information given is stored as a note attached to the record and each record contains a link not to the photograph but the page on which it is displayed. \u00a0There are a total of 1102 records<\/p>\n<p>Given the wealth of information contained in the notes section, we drew from there and \u00a0created a\u00a0 word cloud based upon the frequency of individual words used in the info provided. \u00a0I formatted the resulting word cloud based on personal taste. \u00a0I wanted to understand with what frequency the records identified location, building types and\/or human presence in the photographs. \u00a0Looking at the visualization, I understand that overwhelmingly the images are derived from properties located in Manhattan, with Brooklyn coming in a second and the Bronx a distant third. \u00a0Interestingly, Queens barely registers at all, from which I might derive different meanings but that I know now that I need to look at more closely. \u00a0I can also surmise from the visualization that the records are predominantly of building exteriors, and that if human presence is noted, it is most often of children. \u00a0What I cannot surmise are\u00a0the motivations of the individual inspectors who were taking the photographs and their choices regarding content. \u00a0I would move forward from this visualization by isolating more specific address information where it is available and looking for patterns that might indicate human condition, ethnic concentrations and\/or the ways of occupying public\/private spaces that might generate insight and the ability to formulate better questions.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; For the purposes of this week&#8217;s assignment, I chose to use Wordle.net to create a visualization of our dataset &#8211; NY Tenements. \u00a0The dataset itself is a photo record &hellip; <a href=\"http:\/\/miriamposner.com\/classes\/dh101f16\/2016\/10\/24\/1220\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;NY Tenements &#8211; Word Cloud Visualization &#8211; Week 4&#8221;<\/span><\/a><\/p>\n","protected":false},"author":54,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1220","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/users\/54"}],"replies":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=1220"}],"version-history":[{"count":0,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/1220\/revisions"}],"wp:attachment":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=1220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=1220"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=1220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}