For this blog, I decided to choose and\u00a0explore the police expenditures dataset which details the financial\u00a0data\u00a0of\u00a0police\u00a0expenses from June 2011 to January 31, 2014. The data types are as follows: ID number, Fiscal year, Department name, Vendor name, Transaction date, Dollar amount, Authority, Business tax registration certificate, Government activity, Fund group name, Fund type, Fund name, Account name, Transaction ID, Expenditure type, Settlement\/judgement, Fiscal month number, Fiscal year-month, Fiscal year-quarter, Calendar month number, Calendar month\/year, Calendar month, Data source, Authority name, Authority link. Along with these columns, there are 226,210 rows of detailed expense\u00a0transactions. The record in this data set is the total sum of all police expenditures\u00a0which amounts to roughly 4.86 billion dollars\u00a0during the aforementioned\u00a0time period. Additionally, this dataset keeps record of each expense transaction made by the police department which makes\u00a0it easier to\u00a0determine the proper allocation of the money into\/from\u00a0appropriate\u00a0funds.<\/p>\n
As Wallack\u2019s and Srinivasan’s definition of ontology suggests that it “merely implies a distinction between groups’ mental maps of their surroundings”, a dataset’s ontology is the transparency of links\u00a0and boundaries which allows us to further understand a given dataset (Page 2). In other words, a dataset’s ontology is essentially ways in which a dataset’s connections can be recognized and traced. Similarly this dataset’s ontology allows for transparency and understanding of the funds being used by the police department. For instance, it can be reviewed to make sure that no illegal expense transaction occurred or for simple accounting purposes. This data can be helpful to those government agencies that have to estimate the amount of money that should be set aside for the police department from the budget. It creates\u00a0accountability by both\u00a0government and public. It also increases\u00a0transparency for\u00a0the tax payers who can track their tax dollars at work. It\u00a0organizes which fund the transaction is to pull money from. It also keeps track of which officer is submitting the expense.<\/p>\n
This dataset can be organized in many different ways to provide more\u00a0information. It\u00a0can give you a lot of information about where or which vendors the department spends most or least of its\u00a0money to help understand where the resources are being pulled. The expenses range from cellphone bills to water bottles. You are able to prioritize whichever column you want allowing you freedom to organize the data in any order. \u00a0\u00a0Dataset can tell us exactly where the department is using its money and can be helpful in times of budgeting.<\/p>\n
I think what got left out was more details. The dataset uses a lot of broad categories and at times uses the same type for many different transactions. For instance, the expenditure type “supplies and other services” is repeated for more than half of the items. It would help to create additional subcategories to keep the data clean and legible.<\/p>\n
If I were starting over with data-collection and describe a completely different ontology, I would create more data types in order to organize the data even more. For instance, I would break up “supplies and other services” to additional data types which would further organize data and specify which commodities were purchased and see if I can’t put those into a separate category or data type. I would also add data types showing different\u00a0communities or regions of LA and the money being spent there. Therefore, if we are policing in one area more than another\u00a0resulting in overspending resources in one area, we ought to\u00a0address the underlining issues of such a region and explore the true cause of turmoil there instead of simply over-policing and overspending.<\/p>\n","protected":false},"excerpt":{"rendered":"
For this blog, I decided to choose and\u00a0explore the police expenditures dataset which details the financial\u00a0data\u00a0of\u00a0police\u00a0expenses from June 2011 to January 31, 2014. The data types are as follows: ID … Continue reading “Blog 3: Police Expenditures”<\/span><\/a><\/p>\n","protected":false},"author":81,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_eb_attr":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-916","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\/916","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\/81"}],"replies":[{"embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/comments?post=916"}],"version-history":[{"count":0,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/posts\/916\/revisions"}],"wp:attachment":[{"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/media?parent=916"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/categories?post=916"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/miriamposner.com\/classes\/dh101f16\/wp-json\/wp\/v2\/tags?post=916"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}