Bonus Tableau graph

Here is a supplementary Tableau graph for a different visualization method for my data. 

Low Income people are unable to get the physical activity they need

Just over half of Canadians making $20,000 a year or less participate in outdoor activities, while almost all of those making $150,000 or more do, according to Statistics Canada.

The differences between amount of people in the highest and lowest income bracket reporting that they participate in outdoor activity close to home are most visible in the overall participation, but they are reflected in every individual sport and activity too. While the numbers for the income brackets in between are inconsistent, in every single instance the richest people polled higher than the poorest. For activities like biking and kayaking, among others, it can be easily understood that some people can’t afford to buy the expensive items needed to participate, but for activities like walking it can be less clear at first.

This article mentions a study that shows that poorer people are more likely to be obese, not only because of a lack of opportunities to be active, but also because healthy food is more expensive and inaccessible for them. In addition to this, poorer people lead more stressful lives on average which can lead to overeating and obesity on its own.

This article cites a survey that showed that many of its lower income respondents said they didn’t exercise as often as they could because they feel unsafe walking or jogging in their own neighborhoods. Those people are also more likely to be unable to pay to get access to gyms and other indoor locations.

This article features a map of the US from a government report showing where adults are and are not meeting the recommended levels of physical activity. It also looks at a poll explaining why that is, which concludes that income is the most important factor. Interestingly, this article also says that only a quarter of all American adults actually do meet the recommended levels, and shows a break down by a number of other factors too, also from the government.  

Activity levels have a severe impact on health and overall well-being. Being unable to exercise because of safety concerns or inaccessible healthy food options can impact every aspect of an individual’s life, from their relationship with family members to work performance. While it may seem easy to suggest that someone not meeting the recommended levels of fitness and suffering for it is simply not exercising enough self-responsibility, there are factors that are out of their control that can be heavily impacting them. This cycle of poverty that pushes people down makes it extremely hard to get the help you need and carries down from generation to generation. If poverty is to be seriously confronted and we are to make progress in preventing it, there are a number of systemic issues that need to be addressed along with income inequality.

While these factors are often the result of age old policies and city planning that are not the fault of anyone alive today, it is in everyone’s best interest to examine what exacerbates poverty and what we can all do to help.

Data Update #3

- Chart

- Unanswered Question

What other differences between household income brackets are there in terms of health? I would be interested in finding other things that might not be obvious but impact the well-being of people making different amounts of money. An example might be the number of doctor’s appointments people who make a lot of money have per year as opposed to people who don’t make as much money, or the amount of times a household eats fast food a week.

- How would you get the data?

To answer these questions I would want to find a dataset similar to the one I use here, with information listed by income bracket, but sorted through different standards. If the brackets are sized or grouped differently that would still be workable, and I think most of these datasets would have to also be based on surveys. I believe the Ministry of Health should have the kind of information I’m looking for, so I would first contact the assistant of the person in charge of gathering and publishing that information. I think they would be understanding of why I want the data and be willing to help me without forcing me to file an FOI request.

Data Update #2

 

Data Visualization Project: Update 2

 

Lead (One Sentence)

Nearly half of households making under $20,000 a year don’t participate in any outdoor activities close to the home, and almost all of those that do only walk.

Excel Workbook Link and Explanation

https://drive.google.com/file/d/1AONk_X1H-acATJGPZrUuvS0PncN9ATdT/view?usp=sharing

This workbook shows the percentages of members of certain income brackets that participate in certain outdoor activities. In my slice I removed many of the activities because a lot didn’t have any reliable data on them and to me the most noticeable thing was that no substantial amount of people in the lowest income bracket did any other outdoor activities besides walking.

Original Dataset Link

https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3810012301

 

News Story/Study Link and Brief Summary

https://thevarsity.ca/2021/03/06/where-are-the-trees-for-black-communities-in-toronto/

This story is about how trees and green spaces in Toronto, which offer more opportunities for outdoor activities as well as foster better mental and physical health, are disproportionately lacking in “low-income and racialized neighborhoods.” The wealthier neighborhoods often times have better tree covers, which improves levels of pollution in the area and make it more likely that the residents will be able to exercise outside close to home.

The existence of more trees in areas of higher income is a factor that might contribute to why those people tend to participate in more outdoor activities in Canada and is a good example of context for my final report.

In response to suggestions:

As Chad pointed out in my feedback for the last update, the survey that this dataset comes from doesn’t cover a very long period of time. Both datasets I was trying to choose between come from the same one which only covers from 2011-2017, and while I did find another dataset that was similar, it also only had information from 2016. Right now I personally find the dataset showing the differences between income groups more interesting than the dataset showing the differences between the provinces so I decided to go with that one for now.

Data Update #1

 

1. What dataset will you use for your final report? (title of your dataset,

include a link to it and claim it at the URL above).

The title of my dataset is “Participation in outdoor activities, by household income”, which can be found at https://www150.statcan.gc.ca/t1/tbl1/en/cv.action?pid=3810012301 on the Statistics Canada website.

2. Describe the dataset. What kind of data does it contain?

This dataset is sourced from Stats Canada’s “Households and the Environment Survey” and shows what outdoor activities people who live in households under different income groups say they have participated in during a given year. The outdoor activities they asked about are under 16 different sections, and are specified as being “within a ten minute journey from home.”  The data is from surveys conducted in 2011, 2013, 2015 and 2017. The income brackets run from under $20,000, including households reporting “income loss,” to $150,000 and above, with an additional bracket for those with an “income not stated.” There are quite a few activities that don't have any data at all for certain years because Stats Canada is not confident enough with those data points to publish them. 

3. Is there anything about your data that you don’t understand? (i.e.

what a column heading means). How will you find this out?

No, I find the raw data here to be easy to understand. I am curious as to why some of the activities were grouped together instead of stated separately but that is not a confusion with the data itself.

4. What are some questions you hope to answer with your data? List at

least three. (you don’t need the answers at this point)

I am curious to see which outdoor activity’s numbers are the most different between the lowest income bracket and the highest income bracket. Another question I hope to answer is if there are any outdoor activities that have seen a drastic drop in popularity over time, and I also want to find out if there are any outdoor activities that show to be less popular the wealthier the household, and which those might be. If I can find a way to see which activities are more popular in different places in Canada I would be interested in that too.

Data Viz Analysis - How Similar Are Your Favourite Dog Breeds?

 

The data visualization I chose to analyze is this interactive dog breed family tree by Michael Whitehead and Nick Evershed based on work done by the National Human Genome Research Institute.

The visualization works by allowing the user to select what type of dog breed they want to see and showing the similarity between it and other breeds as a number from one to one hundred when the user hovers their mouse over the image. The visualization is very clean and generally simple to view and understand, although when showing some groupings of breeds, the names and numbers overlap and become difficult to read. The image shifts very slightly when the user repeatedly clicks on the image, maybe to try to alleviate this issue, but it is uncontrollable and slow.

In addition to the image showing the number to signify the similarities, the line connecting the images also becomes wider or thinner to correspond with the number. Because most of the numbers are very low, this line is almost the exact same across the board and only draws attention when it is dramatically different, such as the line between the Norfolk Terrier and the Norwich Terrier. This effect achieves success in highlighting breeds that are very similar, but one other method that could have been used would be to make the distance between the images correspond to their similarities, so the closer the images together the more similar.

At the beginning of the article, some examples of images are shown to familiarize the viewer with the processes of the visualization. Instead of multiple examples of the same basic thing, I think it would have been interesting if it showed off, for example, which dog breeds are most similar overall. If a user wanted to follow a trail from one breed to another they would have to simply go into the list and keep track of the trail themselves. It also would have been interesting if there was a feature to allow the user to see how one breed becomes more like another, like it’s teased in the subheading of the article.

The visualization works well in the shape of a sun like this. It could have been presented as a bar graph, but would have been very long across for some of the breeds, so I think it was best presented as it is.

Perhaps one of the best parts of this visualization is the page that is displayed when the user selects the “wild” group option. This page shows a Grey Wolf all on its own in the centre, with no relatives around it. In all, the visualization allows for some fun and interesting experimentation with such a massive selection of breeds available.

Evershed, Nick. “Interactive: See How Your Favourite Dog Breeds Are Related to Each Other.” The Guardian, Guardian News and Media, 24 Oct. 2020, www.theguardian.com/news/datablog/ng-interactive/2020/oct/25/interactive-see-how-your-favourite-dog-breeds-are-related-to-each-other#doggraphic.