For this week’s assignment, I looked at the 311 Call Center Tracking Data (Archived) Dataset. 3-1-1 is a number that people can call for non-urgent concerns or inquiries, such as requesting graffiti removal or asking for information about parking enforcements. This particular dataset contained 7 content types, namely- Date, Time, Department Abbreviation, Department Name, Service Name, Call Resolution, and Zip Code. Just from the names it is not immediately obvious what some content types are referring to. However, after actually looking through the dataset itself, I was quite easily able to figure out what information each content type included.
Each record in this dataset refers to a 311 call. Date and Time, as the names suggest, refer to the data and time of the 311 call. Department Abbreviation and Department Name refer to the department that the call was handled by. Service Name describes the nature of the call content (e.g. Bulky Item Pick-up, Trash Container Services, or Animal Service Centers). The Call Resolution categories how the call was handled (e.g. call was transferred, information was given to the caller, or a service request was processed).
The information included in this dataset would be the most useful to department officials and employees, as this dataset can provide information about what are the most common reasons people call the hotline, which departments are receiving the most calls, and what kinds of calls each department is receiving. However, a lot of information which may potentially be useful is also left out, for instance, the call duration, which can suggest how efficient the requests are being handled. Also, for examples such as the Dangerous Animal calls, it might be helpful to know what animal the call was about and where the animal was spotted.
This particular dataset is probably more useful to department officials and employees, but less useful for members of the general public. If I were to start over with data collection, I would probably have a completely different focus. For instance, personally I am more interested in the kind of concerns being reported and how efficiently they are being resolved. So, I would collect a larger amount of content types about the specific details of the call. For example, I would collect information about a specific address when applicable, such as the address of a vandalism report, to see if it is reoccurring at the same spot. I would also collect data about the call time, and the duration it took to finish processing a service request, to see how efficiency in dealing with these problems differ by case type and by department. Therefore, we can see that different people looking at the same topic can end up with different datasets that emphasize different information, depending on what data they choose to collect, and what data they choose to present in the dataset.
I love how you personally want to switch the focus of the data to being more informative to regular citizens in order for us to take preventative measures. Efficacy in resolution can tell us a lot about how our city works in dealing with these measures and a public address can definitely let people know ahead of time where to look out. Keep up the great work!