The dataset that I have chosen is on electricity permits in the city of Los Angeles. To give a little background to how the permit works, the Department of Building and Safety will typically issue permits for construction, remodeling and repair of buildings and structures. There are a few categories for these permits, namely buildings, electrical and mechanical permits. Another way to categorize it is by looking at speed of issuance. A permit could be issued on the same day with Express Permit or e-Permit (“No Plan Check” category) or it could take longer due to reviewing (“Plan Check” category). It is important to understand the categorization or the types of these permits to understand the relevance of the dataset that I’m looking into.
This particular dataset is laid out in a table format. There are a total of eight content types or eight headings of the dataset, namely “Assessor Book,” “Assessor Page,” “Assessor Parcel,” “Tract,” “Lot,” “Block,” “PCIS Permit #,” “Reference # (Old Permit #).” These headings are in order from left to right with its initial sorting being from the “Assessor Book” heading. What’s interesting is that not all of the cells on the table is filled. The cells under the “Block” heading are are barely filled and so are those under the “Reference # (Old Permit #).”
By the looks of it, this dataset aims to describe or address three questions. First, it answers the question of where the permit comes from, since there are three content types relating to the assessor. Second, it answers the question of what the permit is for, as described by the tract, lots and blocks. Third, it describes a historical point of view where permit holders might have had previous permits before. These three questions that the dataset answers are those that are most significant to someone who wants to track down the permit, for purposes like checking the validity of a permit or perhaps whether a permit even exists. Therefore, this dataset could have been designed for the use of building auditors, who would most likely use this dataset to check the legitimacy of permits, both prior and after construction commences.
However, this dataset is missing the criteria that were actually tested during the process of obtaining the permit. This could be misleading as even though a permit was reflected on this dataset, we have no way of knowing the integrity behind the process of obtaining the permit as well as some of the criteria that was tested to get the permit itself. These details could be significant in preventing specific problems that might come about if not tested for properly.
It might be interesting to organize the data according to buildings instead of according to the assessors. If the data is sorted according to buildings, it could be useful for the building managers themselves to check the validity of their permits and perhaps alert themselves when a permit is about to expire. This then serves prospective function of preparing for permit renewal, rather than a retrospective function of auditing a building prior to construction.