{"id":1521,"date":"2017-10-22T21:54:09","date_gmt":"2017-10-23T04:54:09","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f17\/?p=1521"},"modified":"2017-10-22T22:00:47","modified_gmt":"2017-10-23T05:00:47","slug":"1521","status":"publish","type":"post","link":"http:\/\/miriamposner.com\/classes\/dh101f17\/2017\/10\/22\/1521\/","title":{"rendered":"Arrest Data from 2010 to Present"},"content":{"rendered":"

The dataset from the City of LA that I chose to analyze was the Arrest Data from 2010 to Present<\/a>. The organizational strategy to divide this data into information, or the ontology of this dataset, is one of concrete facts to document the specifics of each arrest incident which has occurred in the City of Los Angeles since 2010. There are 17 separate categories which divide up the data. Those categories combined record the time and date of a given incident as well as details about the person who was taken into custody. The categories are: Report ID, Arrest Date, Time, Area ID, Area Name, Reporting District, Age, Sex Code, Descent Code, Charge Group Code, Charge Group Description, Arrest Type Code, Charge, Charge Description, Address, Cross Street, and Location. There are over 1 million recordings of arrests in this dataset.<\/p>\n

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In looking through this dataset as a college student who is not specialized in police reports or criminal activity, it appears that there are some categories which are redundant in the presence of others. For example, there are five different categories within this dataset that represent the location of the incident: Area ID, Area Name, Address, Cross Street, and Location (documented in coordinates). The specificity of this data’s breakdown makes it apparent that the purpose of this ontology was not necessarily meant for the average passerby. This ontology appears to be structured for ease-of-use by the police force of Los Angeles. Undoubtedly, this classification of data into several definite categories, albeit data that might seem redundant to someone not well versed in this field, would make sense to both the person making record of the arrests occurring as well as the people who need to be made aware of these incidences. The fact that this dataset’s ontology is seemingly structured for policemen and women is further solidified by the categories of indexed codes; categories like Reporting District, Area ID, and Charge are all listed with numerical indications that would surely mean a great deal more to someone who has been trained to understand their purpose.<\/p>\n

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The dataset claims to report an unbiased record of the arrests which have occurred in various neighborhoods and communities within Los Angeles since the year 2010. I believe that this dataset accomplishes what it claims to describe. The dataset’s description lists a disclaimer that some original arrest reports have been typed on paper and subsequently loaded into the database after-the-fact, leading to potential inaccuracies; in my eyes, this furthers the dataset’s credibility because of the author’s upfront communication. If there are any inaccuracies in the data, the author encourages readers to note questions and concerns in the comments. This is an open-minded and fair approach to data collection, which might seem objective, but can be sometimes affected by natural human error. Information which gets left out of this dataset are the events which occurred on the report’s case after they have been arrested: has this person been charged? Have they been offered the chance to get out on bail? Were they able to pay their bail? Etc.<\/p>\n

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An alternative ontology to this dataset would be of someone writing about arrests in either a criminal fiction novel or in a news piece. I think a novelist or a journalist would have documented this data in such a way as to prioritize the more rare, dramatic, or dangerous arrests that have taken place in Los Angeles since 2010. The ontology of this perspective would leave out several arrest codes that are denoted within this ontology for this dataset and would instead focus on sections like Charge Description as well as the description of the person in question, including Age, Sex, and Descent.<\/p>\n","protected":false},"excerpt":{"rendered":"

The dataset from the City of LA that I chose to analyze was the Arrest Data from 2010 to Present.<\/p>\n","protected":false},"author":184,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1521","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/users\/184"}],"replies":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/comments?post=1521"}],"version-history":[{"count":0,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1521\/revisions"}],"wp:attachment":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/media?parent=1521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/categories?post=1521"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/tags?post=1521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}