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{"id":787,"date":"2018-11-11T20:59:14","date_gmt":"2018-11-11T20:59:14","guid":{"rendered":"https:\/\/sub1.dakinyemi.catapult.bates.edu\/?p=31"},"modified":"2018-11-11T20:59:14","modified_gmt":"2018-11-11T20:59:14","slug":"locating-place-names-at-scale","status":"publish","type":"post","link":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/2018\/11\/11\/locating-place-names-at-scale\/","title":{"rendered":"Locating Place Names at Scale"},"content":{"rendered":"<p>This article was very short but provided a lot of information about an issue that arises when trying to extract spatial information. The ambiguity that comes when trying to locate places with similar names is an example of how things can go wrong. In order to help fix this issue, computer scientists utilize the context of documents to associate places by references points to narrow down this such as country, state and city. Through these reference points and programs such as NER and NLP, users can identify places talked about in interviews and then map locations for visualization. This visualization created helped show the moving process of people in Virginia around this time. I think this article was a great way to explore the fault in some programming and show how searching for specific things does not always get you the results you need. Sometimes we need to look outside what we are actually looking for.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article was very short but provided a lot of information about an issue that arises when trying to extract spatial information. The ambiguity that comes when trying to locate places with similar names is an example of how things can go wrong. In order to help fix this issue, computer scientists utilize the context &hellip; <\/p>\n<p><a href=\"https:\/\/sub1.dakinyemi.catapult.bates.edu\/uncategorized\/locating-place-names-at-scale\/\">Continue reading<span> &#8220;Locating Place Names at Scale&#8221;<\/span><\/a><\/p>\n","protected":false},"author":182,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-787","post","type-post","status-publish","format-standard","hentry","category-class"],"_links":{"self":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/787","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/users\/182"}],"replies":[{"embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/comments?post=787"}],"version-history":[{"count":3,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/787\/revisions"}],"predecessor-version":[{"id":1411,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/posts\/787\/revisions\/1411"}],"wp:attachment":[{"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/media?parent=787"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/categories?post=787"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/courses.shroutdocs.org\/dcs104-fall2018\/wp-json\/wp\/v2\/tags?post=787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}