This dataset has an ontology based around measurement of Kilowatt Hours used for certain energy efficiency projects around Los Angeles.
The columns included in this ontology are as follows:
-Type of Program
-Program
-Metric
-Months from July 2013 to June 2014
– Total YTD
Programs are repeated twice, with the top row being the goal for the year and the bottom row being the Actual energy usage.
This ontology makes the most sense from the point of view of a board of advisors or government officials who want to know how much energy was used in a year by clean energy programs, and have a record of whether the programs are achieving their goals.
The next question for the blog post is asking what the dataset tells about the phenomenon that it is describing, but there is no consistent phenomenon, the data for each month was not entered for every month in some cases, and in other cases there was no entry for the goal or the actual metrics, like the one pictured below.
What this ontology describes in reality is the inadequate record keeping of LADWP, you cannot retrieve any accurate information on the effectiveness of the programs described by this dataset, as the dataset is incomplete, and due to the highly variable nature of energy usage, there is no way to accurately extrapolate data values without secondary information. The ontology was created with tabulation in mind, due to the columnar organization around months, but the YTD column shows that summation was a priority of the ontologist.
If I was starting over, there are two different methods of data organization I would employ depending on the goal of this new perspective. The information could be organized from top to bottom by most recorded programs to least recorded programs, due to the incomplete nature of the majority of the data sets, If the goal was to analyze the most functional data as quickly as possible.
Another method of Organization could be to prioritize months even more clearly using coloring, and I would delete the last three months, as there were no actual entries during these months. By removing the summation column, there is less feeling of incompletion, or deception. An ontologists role is not only to organize data in an understandable fashion, but to provide a consistent method of cataloguing. The lack of consistency in this dataset is not only constrained to consistency of entries, but is also present in the null entries. Some are marked with a zero, and some are left blank. The lack of information in this dataset is bizarre, as it was published on the LADWP website. If you are interested in seeing the data for yourself here is the link.