The dataset that I chose to evaluate was the “Demolition Permits” dataset. The creator of this data set seems to be a group of people. They are known as the “Building Commissioner”, which makes sense because you need permission from a higher power to destroy a building. The source of their data is the St. Louis region. My guess for why they compiled all this information is to keep track of the demolition permits and when they were issued and approved. This seems like the most logical approach to keep track of this type of data set. The data set is constantly being added to and updated, having a section in the data set for just the last 30 days, showing how often it is most likely used. It has been used to keep a record of how many demolition permits have been issued, where they were issued, how much they were worth, and the average days to get one in that specific area. As previously stated, this data set is constantly being updated, so all of this information is changing, which means when the data is used, the next time it is observed, there may be different values.
When looking at the data itself, there are multiple aspects to it. As previously stated, it is divided into the neighborhood, the total number of permits, the total value of permits, and the average number of days to issue a permit in that location. The only area listed is the neighborhood itself in the St. Louis region. An effect on how it could be used is very positive in this way. Because the data is separated into the different neighborhoods, someone living in that neighborhood would know how long it would take to get a demolition permit. The only bad thing is that there is no real way to know how much a demolition permit costs, but that can be found in other data. This data set just focuses on the amount of permits issued and how much they cost in total, as well as the total number of permits issued.
When looking for the description of the data set, I could not seem to find any. This is expected because the title of the data set is pretty self-explanatory. The only thing that would need to be described is how the data is formatted, but that can be accessed by the user. The creator of the data set is a collection of the building commissioners. Since they seem to do this on a regular basis, the data would need to be easy to read and understandable. This is possibly a reason as to why the data is formatted this way. Using the data is another story. If I was wanting to get a demolition permit for the area that I live in, I would use this data to see the neighborhood I am living in to see how many permits were issued in the last 30 days. If this number is high, then I know that it might take a while for me to get a permit. The opposite is also true, if the number is low, then it should be easy to get a permit in the area. This data might help me understand the dynamics of MOBOT by looking at the surrounding area and seeing if they are trying to demolish areas. Although this data set would not tell me much about MOBOT, the data is still there to evaluate for others to use.