The LA Controller’s Office features data from the City of LA’s Bureau of Street Services that composes an interactive map containing a data set called Street Grades. This data set provides information about the quality and state of LA’s roads, and can be manipulated using this interactive map. The data presented on the map can be filtered using five different criteria: council district, neighbourhood council, road repair, street pavement condition, and 2014-15 road repair, and filters can be applied at the same time, if the user desires. Within these categories there are only three data types: geographic boundaries based on city council, road conditions, and road repairs, both past and scheduled. Therefore a record is a single road and its variables represent the data associated with that record.
Wallack and Srinivasan define ontology as a system of categories by which groups order and manage information about the things around them. In this case therefore, the Street Pavement Condition is the dataset’s ontology, as is provides clearly defined categories into which roads can be assigned. The road conditions are quantified using the Pavement Condition Index (PCI) which runs from 1-100 and is the sum of the road’s present physical condition and how much repair is necessary to restore the road to perfect conditions. The PCI then categorizes each road into five groups based on score: failed, poor, fair, satisfactory and good.
In my opinion, the people who will find this information most useful and illuminating are industries that rely on road transportation networks, such as taxi, courier and shipping services and navigation software. This is because roads that are in good condition tend to provide more efficient traffic flow than, since they are easier and less challenging to drive on. On the other hand, roads that are in poor repair slow down traffic due to drivers avoiding potholes and other nuisances in the road. Additionally, real estate companies can also find this information useful as they can rate geographic regions’ quality of living using this information.
This dataset does a very good job at describing that many of LA’s roads are in poor condition and are in great need of repair. It also succeeds in showing that while many are in need of repair, very few of these roads are actually scheduled for such repair. It also illuminates that more affluent neighbourhoods tend to have better quality roads.
There are some cities within LA that withhold their data on their roads conditions, such as Santa Monica and Beverly Hills. This is an inconvenient gap in the data and it would be nice to have the entire map covered with interactive data.