{"id":1417,"date":"2017-10-20T10:18:54","date_gmt":"2017-10-20T17:18:54","guid":{"rendered":"http:\/\/miriamposner.com\/classes\/dh101f17\/?p=1417"},"modified":"2017-10-20T10:18:54","modified_gmt":"2017-10-20T17:18:54","slug":"arrest-data-from-2010-to-present","status":"publish","type":"post","link":"https:\/\/miriamposner.com\/classes\/dh101f17\/2017\/10\/20\/arrest-data-from-2010-to-present\/","title":{"rendered":"Arrest Data from 2010 to Present"},"content":{"rendered":"

The dataset from the city of Los Angeles that I chose, is Arrest Data from 2010 to Present<\/a>. The ontology of this dataset is designed in such a way that allows the viewer to search for an arrest in seventeen different ways. One can search by the 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.<\/p>\n

For the most part, this ontology is understood best by those within the law enforcement system who are aware of the codes used. A member of the general public may be able to self-determine what some of the codes mean, but without reading the descriptions or gathering conclusions from their own information, understanding some types of this categorizing may be difficult. For example, the general public may be able to understand that \u201cM\u201d and \u201cF\u201d in the \u201csex code\u201d category stands for \u201cmale\u201d and \u201cfemale,\u201d but when it comes to the codes within the \u201carrest type,\u201d many are clueless as to what \u201cD,\u201d \u201cF,\u201d \u201cI,\u201d \u201cM,\u201d and \u201cO\u201d represent. Several of the categorizes used within this dataset are also largely irrelevant to those who are not part of the authoritative system. The \u201creport ID,\u201d \u201carea ID,\u201d \u201ccharge group code,\u201d and \u201carrest type code\u201d mean very little to the general viewer, but may be crucial aspects of identifying cases to members of law enforcement.<\/p>\n

Albeit not a completely straight-forward system of organization for all viewers, the dataset as a whole is very important to many groups of people and individuals inside and outside of the city of Los Angeles. For anyone living within Los Angeles, this dataset could be a useful key for understanding the safety of themselves as well as their children and families. By looking at the locations and times of arrests, one could determine it best to keep away from certain areas during specific times. Additionally, this dataset gives a heads-up to the safety of a particular community that someone outside of the area may be moving to. This dataset could provide answers to research questions, as well. Correlations regarding race and arrests and locations could be made. All in all, this dataset is illuminating to anyone who is curious about current and past trends in Los Angeles arrests. It could uncover possible target areas, target populations, and may even uncover biases within police department sectors.<\/p>\n

Although this dataset may prove to be very useful and enlightening, it is not without exclusions. At the most basic level, we know that arrests which happened prior to 2010 are not included. This dataset also does not include instances of police presence without arrests. It does not take into account events where the suspect escaped and was unable to be arrested. Arrests that were not recorded for one reason or another would also not be in this dataset. Additionally, since there are only options for recording male and female gendered arrests, proper documentation for all genders is missing.<\/p>\n

From another point of view, a different ontology could be applied. Arrests could be categorized by more than just the binary genders. It also would be interesting to find out who reported the crime which led to the arrest. Was it due to police patrolling, a community watch program, bystanders, or family members? Was violence used during the arrest? Having this information could help researchers look deeper into the dataset and assist with determining new theories of social injustice and inequalities.<\/p>\n","protected":false},"excerpt":{"rendered":"

The dataset from the city of Los Angeles that I chose, is Arrest Data from 2010 to Present. The ontology<\/p>\n","protected":false},"author":151,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1417","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1417","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/users\/151"}],"replies":[{"embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/comments?post=1417"}],"version-history":[{"count":0,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/posts\/1417\/revisions"}],"wp:attachment":[{"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/media?parent=1417"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/categories?post=1417"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/miriamposner.com\/classes\/dh101f17\/wp-json\/wp\/v2\/tags?post=1417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}