After scanning the data set on the St. Louis, MO Monthly and Seasonal Mean Temperature set I was provided, I had to do a little digging. This data set seems to have came from the NOAA, who are responsible for collecting and analyzing climate data and environment data. After making sure that this administration is highly credible I also learned that they do these surveys of the environment to this day consistently. Though they are responsible for the data set presented the data probably comes from a long standing weather station in the St. Louis area with the proper tools and instruments to record the data.
The purpose of compiling this data set is to track the temperature trends over time and provide insight into St. Louis’s long term weather patterns. The data could be used to monitor the environment by scientists and researchers. Or to relate it to my area of interest it could be used by investors for urban planning.
The data is presented in a table format with columns and rows. When referring to structure I think this table achieves its purpose in presenting the data clearly and plainly. It shows the yearly data broken down into monthly averages and seasonal averages. The organization makes it useful to identify weather trends over the decades. However, because it only tracks averages, the data does not represent outliers like the weather phenomena we are experiencing this week (3/19). This could cause an issue and makes the data less reliable.
The methods to “clean” this data aren’t mentioned in the PDF provided, but following my NOAA statement made prior; the NOAA typically applies many procedures to ensure the consistency and quality of the data. Some effects to the data may still be present, things like weather tracking technology and station location. Another that came to mind was the slimming size of St. Louis since they began to conduct these weather surveys, I wonder if it could have gotten colder because there is less population? Surely there is less foot traffic, construction, etc…
The creator’s goals likely focused on long term averages and effects rather than short term effects. This could unintentionally downplay odd short term weather events and phenomena, especially in recent memory.
I think that the data set would be most useful to predict future weather patterns. Visualizations like charts or graphs representing the most large changes, this could also prove useful to provide insights into the growing climate change concern.