1. What dataset will you use for your final report?
The dataset I will use for my final report is titled 'Bylaw Tickets' from Port Moody's Open Data Portal.
2. Describe the dataset. What kind of data does it contain?
The Bylaw Tickets dataset contains over 38000 rows of data about bylaw tickets issued in Port Moody between January 1st 2008, and November 17th, 2020. The dataset contains columns such as:
- Ticket numbers
- Ticket dates and times
- The ID number of the issuing officer
- Vehicle information (make, model, colour)
- Bylaw numbers, sections, and descriptions
- Dollar amounts for fines and discounts
- Long form descriptions with additional detail
3. Is there anything about your data that you don’t understand? How will you find this out?
As a whole, the data is actually quite straightforward and well described. The only column heading I am not currently sure of is the 'Offence_Code' column, which primarily contains whole numbers, but also includes entries like '4(A)', '16.18(F)', and a few numbers with decimals like '3.1', '4.1', and '4.2'.
To figure out exactly what is represented in the 'Offence_Code' column, I will likely need to look into information about Port Moody's bylaw enforcement. I will search through their website, and if I'm unable to find it there, I will have to look into external sources.
4. What are some questions you hope to answer with your data? List at least three.
Some of the questions I hope to answer are:
- Which officers issue bylaw tickets the most? Which bylaw infractions do they issue tickets the most for?
- At each hour of the day, which bylaw infraction is the most likely to generate a ticket?
- Which type of bylaw ticket has seen the most significant monthly increase or decrease over the length of the data set?
- Which type of bylaw ticket are the most common vehicle makes the most likely to receive?
- What is the average number of bylaw tickets issued each month?
There is a significant amount of data in this dataset, so depending on the question, I may be required to filter the data into a subset (10-20) of the most frequently seen data points.
Looks like you have your data (mostly) figured out there, Philip! I'm looking forward to further updates on what you end up learning from this set. Overall, great work!
ReplyDeleteHi Philip,
ReplyDeleteIt looks like you really understand your dataset and you came up with some interesting questions for your dataset. One thing I think could be better is the grammar; there are some long run-on sentences that could be separated into shorter sentences and there are some instances of comma splices.
Hello Philip,
ReplyDeleteI really like the way you explained what is in your dataset. It is very clear and concise for people who are seeing your data for the first time. I did notice a couple of grammar errors (e.g. the fourth question) though.
Hi Philip,
ReplyDeleteThe dataset looks very interesting. It sounds like you know what the data is providing. I do think some of your questions might require more research then your data can provide. Secondly, I did notice some minor grammar issues in your post.
Hi Philip,
ReplyDeleteYour dataset contains good information and you understand it well. Some of your questions may be a bit too specific and could be better if they focused on a broader aspect of the data.
Hi Philip,
ReplyDeleteYou have a really good handle on your data here and I am looking forward to seeing the answers you come up with.
Great work!
Hi Philip,
ReplyDeleteGreat detail on your dataset, response and listed questions. I look forward to your updates and visuals.
Hello Philip! This seems like a cool dataset, and I am looking forward to some of the information you will discover with it. The 38000 rows seemed overwhelming to me, but I am confident that you have a good idea of how to approach the data with sorts, filters, and even just looking at subsets of it. What I am most interested/curious to find out is what bylaw infraction gets tickets issued the most. As well as what hour of the day it is most likely. I also like how the data focuses on Port Moody, so that might be interesting to look at (I don’t know much about that area). I agree that looking at subsets of this data may be more workable.
ReplyDelete