Data Update 1
What dataset will you use for your final report?
Describe the dataset. What kind of data does it contain?
Is there anything about your data that you don't understand? (i.e. what a column heading means). How will you find this out?
Describe the dataset. What kind of data does it contain?
The 42,708 row dataset is for Building Permit Issuances in the City of Vancouver from 2017 onwards. The building permits would be for instances such as construction, deconstruction, demolition, and development. It contains information about application and permit approval timelines; the "Type of Work" the for building permit (e.g. demolitions, additions/alteration, new buildings, etc.); the "Property Use" on a macro scale (e.g. Culture/recreational, Dwelling, Institutional, etc.); the "Specific Use Category" of the building (e.g. Fitness centre, duplex w/ secondary suite, financial institution, etc.); and the building location information.
Is there anything about your data that you don't understand? (i.e. what a column heading means). How will you find this out?
For the most part I understood the column headings, but I have questions about the redundancy of some. For instance, what purpose does it serve to have these three individual columns: Address, Geom (which is the coordinates of address), AND Geo_point_2D (which is more coordinates). Could it not be consolidated under one location heading?
To get clarification, I can contact the dataset "geek."
What are some questions you hope to answer with your data? List at least three. (you don't need the answers at this point)
- Which industries have the highest demand on the development industry?
- Which geo local area is seeing the most development?
- How has the demand for dwelling permits changed over time?
- e.g. Under the Type of Work heading, does it show more renovations compared to new buildings?
- e.g. Is the Specific Use Category trends changing?
- e.g. Are apartments and mixed-use residential becoming more prevalent than single detached housing?
Hi Madi! Your data update could uncover many exciting details and information about your dataset. You have a lot of data to work with, and I like that you included examples when describing the dataset. I like your questions, but you would not be able to determine the last question with just this data set; you would need to look at other sources as well. I suggest changing that question to something we can discover in the data.
ReplyDeleteHi Madison, I think your data set is very interesting. Your description of the data set is detailed and clear, and you can clearly know the confusing place of this data set. Moreover, the questions you raised are also very attractive to me, and the comparison in the questions is very interesting.
ReplyDeleteHi Madi! I really liked how you included a detailed description of the data set - even including the names of columns. One suggestion I do have would be to refine your last question into one big question that captures the themes of all the sub-questions.
ReplyDeleteHi Madi, it looks like you've chosen a rich and engaging dataset, I think you have an especially good list of questions to as well as a clear idea of how parts of the data set might be redundant, both good starting points for the next assignment. One potential question for your data set I though may be interesting was relating to each project's value: how have the valuations of certain projects evolved from 2017-onwards?
ReplyDeleteHi Madi, you did a great job breaking down the different parts of the dataset, especially explaining the various categories like “Type of Work” and “Property Use.” It’s clear that you’ve thought through how to use this information to answer your questions. It might be helpful to explain why having multiple columns for location data could be confusing and how consolidating them would make things easier. This would make your point even clearer.
ReplyDeleteHi Madison, great work on breaking down the extensive dataset in the description, making it easier to grasp. I wonder if there might be some difficulty pinpointing “which geo local area is seeing the most development?” given the vast amount of coordination data within the dataset, which you’ve noted in your update could be consolidated. I look forward to seeing how you approach this dataset. Keep it up!
ReplyDelete