This data set within the L.A. Controller’s office compiles Los Angeles County’s street pavement conditions according to a Pavement Condition Index. Each record is a numerical rating of the street conditions, with ranges going from good, fair, to poor. Each record contains a street name, location, and date. The data is arranged on an interactive map, showing green, yellow, and red areas for good, fair, and poor road conditions, respectively. It also highlights the neighborhood councils and council districts partitioning the city and allows a viewer to see the varying road repair plans across time by toggling by year.
This dataset’s ontology organizes data with an aim to understand where and when street conditions have been suffering and where they have been improved. The options to toggle between time periods and view district boundaries implies that those are pieces of information that provide contrast depending on spatial and temporal context. This ontology would benefit any worker under the Bureau of Street Services, which is where this dataset and interactive site originates from. It would be helpful in understanding the terrain of Los Angeles, the current conditions of the roads, and what work has been done in the past to remedy problem areas. It also goes to show what work needs to be done further regarding road conditions in certain areas, as there are certain districts with predominantly green (good) conditions, while others are overwhelmingly red (poor). It can also give insight to where funds are being allocated within those districts, specifically what amount of funds are being invested in street pavement repair.
This dataset gives a lot of insight into what the road conditions are like in Los Angeles, and also accurately shows the road repair plans as well. It communicates this data effectively through visual assets that only strengthen the narrative it sets out to convey. With regards to what’s left out of this dataset, it does seem like the data paints an incomplete picture. Many of the roads in the San Fernando Valley are documented and categorized, but lots of areas are lacking in representation. West LA and mid-city, for example, are sparse in data. Understanding the street conditions form the data presented in those areas would be more difficult and possibly misleading, as one may not be able to reach legitimate conclusions from just this data.
I think an interesting ontology to present this data in would be one that demonstrates some cultural/political information along with the street conditions. Incorporating information about the different council districts and their financial brackets or their budget breakdown would be interesting, so one would be able to make conclusions about the causes of the varying road conditions throughout the LA area.
I enjoyed reading your post and I think the alternate ontology you suggested would be really interesting. It would allow researchers to ask more questions about the causes behind the road conditions and hopefully allow the city to address street conditions in areas that have been neglected in the past.
Great analysis! I like how this data set was represented with an interactive map rather than a typical spreadsheet. Another interesting detail I found was the road repair legend that showed which roads had been fixed and which ones were being currently repaired. You described the data set clearly and with great examples, including how some data was lacking on the map. I also thought that an important detail that the map didn’t provide was how funds were being allocated to the roads that are more “poor” than others.
Good post, I really enjoyed reading your blog. I think this is a really cool dataset, we get to see the road conditions around LA. This dataset can be used for many reasons. I think it can be cross referenced with a dataset that shows the routes of heavy corporate trucks to see if the roads that big trucks take sustain damage faster. That’s been my understand from personal experience that usually 18 wheelers or corporate transportation trucks cause the most damage to a road. For instance, the right lanes on the freeways are always the worst.
Great post! I really liked your alternate ontology suggestion since I feel like thats an avenue that hasn’t really been explored before. One minor critique I would add would be to add images/pictures of the data-sets visualizations that you mentioned in your blog post. While I can sort of visualize it by reading your description, a small screenshot would speak multitudes of what the visualization itself actually was.