When programming effective pieces of software, one must always consider the idea of scale.\u00a0 It may be\u00a0 effective to code at a small scale in order to dissect the inner pieces of your script. When analyzing data or meta data, designing for a small scale will not be effective, as one can be working with up to millions of entries. This is a problem that\u00a0Lauren Tilton,\u00a0Taylor Arnold, and\u00a0Courtney Rivard address in their piece\u00a0Locating Place Names at Scale<\/em> for the 2018 Digital Humanities Conference in Mexico City. They speak about this same challenge of scale in regards to extracting geo spatial data from meta data in order to better visualize the movement of peoples during the 20th century in the United States (Locating Place Names).\u00a0 It is easier to extract this data through close reading, but this is not possible when using a large corpus.\u00a0 They were able to solve this complex problem though analyzing the meta data of when people spoke about migration through their texts, collecting a list of new locations to visualize (IBID). It is interesting to see how they were able to use meta data, and a strong understanding of how to analyze it at scale in order to create a fuller and more accurate depiction of human flow.<\/p>\n","protected":false},"excerpt":{"rendered":"

When programming effective pieces of software, one must always consider the idea of scale.  It may be  effective to code at a small scale in order to dissect the inner pieces of your script. When analyzing data or meta data, designing for a small scale will not be effective, as one can be working with … <\/p>\n

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