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Popular articles by kams
Monthly Challenge – Sofia Air – Solution – Kams
Data Exploration, Observations, Planning
Popular comments by kams
Monthly Challenge – Sofia Air – Solution – New!Bees
Over all good work. I liked the way you presented your approach explanation methodically and commented on the thinking behind your decisions. Like @everyoneishigh, I liked how you plotted Sofia topo data and established a grid and then filtered out the locations.
From business side, I would have liked to see the assumptions you are making about this project. Often, I have found people arrive at a project with different assumptions. Writing yours clearly will give someone an opportunity to disagree or correct them.
Is the data bias that the official data are very reliable a correct one (even though it’s accepted as the eu norm?). Is there a process by which data quality and data gaps are vetted in those stations? Something for you to explore and inform as you look at the AirBG.info mission and more data.
You chose to leave out the data available via API. I also thought your choice of excluding data from 2017 that didn’t have data from 2018 an interesting one. It may be statistically insignificant, but just because citizen data recorders that reported previously didn’t report now, doesn’t mean they reported wrong. That said, given the small amount of this data, it may not be worth the effort and instead use the more recent data from the API.
Over all good stuff – thanks for helping me learn.
Some of the meta data you are looking for is available in a round about way if you look at the official meteorological data. These meta data files include hyperlinks to more granular definitions. T
Air quality week 1
Over all, good job. You seem to be heading in the right direction. You seem to know your technology and tooling and have been applying good reasoning along the way. I am not familiar with tidyverse (so won’t comment on it’s use, but it seems so minimal, I may consider learning it :-). I enjoyed reading your to the point execution to the goals and use of quick plotting to visualize the points.
A few things I would have liked to have seen in the business section: (a) Assumption being made (b) Risks to the project from both an execution and risk perspective. (c) there are underlying assumptions that aren’t clearly stated. For example, AirBG.info seems to question whether the government data is accurate, sufficient etc. Does this impact your analysis or data bias?
In the data section, on the positive side, you walked through it methodically and worked through understanding the citizen data and arriving at a filtered set. Some feedbacks on data section:
1. You seem to ignore the data from the API – perhaps you plan to get to this later but consider if that makes your analysis better, worse or not known.
2. You excluded all data points outside of Sofia. While that is being asked, is there any reason you did this so soon? Could you have done a few iterations of the analysis and then removed them? Especially considering air pollution and inversion, if there is inversion in Sofia, it’s probable the inversion is also in neighboring areas. Is activity in the neighboring areas making Sofia’s problem worse? I think data granularity is an important element. I can tell you that Seattle WA has had thick smoke blanketing the area a few months back from the fires in Vancouver Canada and Oregon. Air pollutants have an odd way of moving around. Perhaps this is a personal choice I would make and explore the neighboring data for patterns first, before eliminating them. From the objectives of the problem scope, your approach is probably just fine – will let the mentors decide.
Monthly Challenge – Sofia Air – Team Yagoda – Solution
Great to see your week1 and week2 work. I am not seeing your week3/4 sections, perhaps there is an error. Please let me know when you have it uploaded.
Monthly Challenge – Sofia Air – Solution – Lone Fighter
When I selected article, I got a docx document for download. Went through your document file. I will caveat my feedback saying that I don’t R at all. That said, I liked your approach to breaking down the problem and filtering. You did great to filter out anomalous stations from citizen data and filtering data to Sofia only. Have you considered normalizing the data to the Sofia ranges for temperature, humidity and pressure. If I am looking at your older page, let me know, will be glad to come back and look at your latest.
Monthly Challenge – Sofia Air – Solution – Jacob Avila
Checking in again on Nov 3rd. for my part of the peer review assignment. Good luck.