I have chosen to examine “All Meals at LAPD Detension Centers” for the financial year 2015 whose earliest transaction dates from August 2014 to the latest one at September 2015.

Link: https://controllerdata.lacity.org/Purchasing/FY-15-All-Meals-at-LAPD-Detention-Centers/xsy9-ysu3

After entering the data set, the various data types (a long and some, I find, overlapping data) that can be found are:

  • ID number (identification)
  • Fiscal year
  • Department Name
  • Vendor Name
  • Transaction Date
  • Dollar Amount
  • Authority
  • Business Tax Registration Certificate
  • Government Activity
  • Fund Group Name
  • Fund Type
  • Fund Name
  • Fund
  • Account Name
  • Account Code
  • 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
  • Department Number
  • Program
  • Vendor ID
  • ZIP
  • Payment Method
  • Payment Status
  • Invoice Number
  • Invoice Due Date
  • Invoice Discount Due Date
  • Invoice Receipt Date
  • Vendor’s Invoice Line Number
  • Invoice Distribution Line
  • PO Number
  • Description
  • Detailed Item Description
  • Unit Price
  • Unit of Measure
  • Quantity
  • Sales Tax Percent
  • Sales Tax
  • Discount
  • Receiver ID
  • PO Date
  • PO Line Number
  • Procurement
  • Buyer Name
  • Supplier City
  • Supplier Country
  • Name
  • Site Location
  • Item Code
  • Item Code Name
  • Currency
  • Value of Spend (Unit price x Quantity)
  • Vendor Num (Vendor ID + Vendor Name)

In this dataset, the transaction details with information on the vendors list, together with the description of the items they provided makes up the record.

Using Wallack’s and Srinivasan’s definition, I think this dataset situates knowledge within the organisational setting of the LAPD division. It makes use of the state meta ontology to allow tractable management for policy purposes (for e.g. planning for space allocation at the detention center?).  In this dataset featuring the amount of meal expenses catered to the LAPD division, it enables readers to gain insights for the “peak periods” of detainees gained and allow for better security management.  That being said, information can be misinterpreted since the dataset did not reveal the target recipients for the meal expenses – which indicates that the increase in meal expenses for a specific period did not necessarily mean that the crime rate has increased. This provides the evidence for the extent of mismatch that can increase when the scale of the group increases with the difference between individual and group ontologies.

This dataset will be the most useful for the police department’s procurement in-charge, especially if the department is considering to find another supplier for the food, and/or attempt to reshuffle the budget for the food purchase.

This database illustrates the total costs spent on food over the financial year with the use of this vendor – Langlois. Overall, information revealed was that there was a total of $580,612.95 spent on all meals at the LAPD department, with all the food purchased from the same vendor, named Langlois Fancy Frozen Foods. The money obtained to pay for these meals are from the general budget and/or fund, and it is regarded as a commodities expense. All the items listed in this dataset are paid, and details regarding number of discounts given was also listed.

In my opinion, since this dataset is more suited to measure the amount of budget allocated and spent on the meals for the detainees, it can also include information like other vendors/suppliers, to compare the costs if another vendor were to be hired. Additionally, to be able to see the cost savings, perhaps the percentage of the total general budget can also be reflected. If i were to start over with the data collection, i will also take note of the peak periods when there was a high surge of the budget spent – so as to predict for future trends. This data can also be used to cross check with the crime rates in the city during specific time periods.

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