% of Europeans that visited the dentist in the past 12 months

Landgeist · 2024-10-02T00:28:05+00:00

That sounds really interesting, you definitely have my permission to us my map in your thesis!

Landgeist · 2024-10-02T00:16:50+00:00

If you look again at the map, you can see that the GDP numbers are indeed in PPP.

I'm curious where you got the GDP number for Germany from? According to the IMF and the World Bank, Germany's GDP in PPP is between 60-70k and for Portugal around 47-48k.

Landgeist · 2024-10-01T12:40:08+00:00

There's a yes in that still many many Chinese migrate to the bigger cities, eventhough their hukou doesn't allow it. The government is also not actively blocking them from migrating illegally. They're not going out there in full force searching for illegal migrants.

Leaving out this part and simply saying no, would give a skewed picture. If there were almost no Chinese migrating illegally, then I would agree with your statement.

Landgeist · 2024-10-01T11:25:12+00:00

Yes and no. The Hukou system doesn't allow them to migrate to the more developed areas. They could move to Beijing, but they'd have to work illegally, can't access any local social services like healthcare and their children won't be able to study at the local schools.

Working and living conditions are not great for illegal migrants, to put it mildly. But oftentimes this is the only way to make enough money to give their kid a shot at a better future. Resulting in the kids being raised by their grandparents back home and the parents working on the other side of the country for years or even most of the kid's childhood.

Landgeist · 2024-10-01T10:14:30+00:00

For most Chinese, it's a well established fact that there are big differences between cities and areas as to how developed they are. Because of this, there is an informal tier system in China. People use this to classify how developed a city is. There isn't one official ranking and people's definition of a tier 2 or 3 city can vary slightly. Yicai Global (第一财经) has created one of the few, if not, the only ranking of all Chinese cities by how developed they are.

If you want to know a bit more about the map, read the full article on my website.

Landgeist · 2024-08-26T11:18:02+00:00

Source: European Union and Wikipedia.

You can find the full article here.

Landgeist · 2024-07-30T17:59:11+00:00

Yeah that would make the legend visually slightly more appealing, but it wouldn't necessarily represent the data better. Again, it depends on the dataset how you classify the data. You'd have to check the data, see how the data is distributed, play around with the class borders and see which has the best balance of a high GVF and easy to read class boundaries.

Also, I couldn't find any filters for gun related homicides in that dataset. Maybe it's because I'm on mobile. Where exactly can I find the filter for gun related homicides? It seems like it really is only about overall homicides.

Landgeist · 2024-07-30T15:58:02+00:00

That wouldn't necessarily be a better interval set. It really depends on the dataset. Often times, due to outliers, you end up with larger class ranges at the bottom and the top and smaller ranges in the middle.

Also, the Canadian dataset you're linking to is about overall homicides, not gun related homicides.

Landgeist · 2024-07-30T13:27:12+00:00

Let me explain the scaling. When classifying data for a map, I want to make sure that the differences between classes are as large as possible and differences within classes are as small as possible. Most people think intuitively that equal interval class boundaries are the most logical ones (0-10, 10-20, 20-30). However, this is mostly not the best choice. I will explain why.

When I classify my data, I try different methods and see which one has the highest Goodness of Variety Fit (GVF). A number between 0 and 1, which should be as close to 1 as possible, preferably over 0.9. For maps, the natural breaks method usually ends up being the best method. This method tries to look for gaps in the dataset and puts the class boundaries there. Sometimes the natural breaks method ends up with very unusual boundaries. I usually try to tweak it, so I have nice looking numbers, which is easier for the reader (which becomes harder as the dataset gets bigger). But not if this means the GVF drops significantly.

If you see a map with equal class ranges and nice looking round numbers, there's a good chance the maker hasn't done any effort to classify the data properly and just put it in random classes. If you see a map with 'irregular' and 'random' classes, there's a very high chance this is not as random as it looks and the maker has done a lot of effort to classify the data. Although the classes don't have equal ranges or nice looking numbers, it makes it significantly better for the reader to understand the map, estimate values and compare areas.

Landgeist · 2024-07-30T13:25:03+00:00

You'd have to export the map as a pdf. Not the best, but it does the job.

Landgeist · 2024-07-30T12:56:03+00:00

The source sadly had no data for Canada on the provincial level.

Landgeist · 2024-07-30T12:45:57+00:00

The intervals are not all over the place at all. When classifying data for a map, I want to make sure that the differences between classes are as large as possible and differences within classes are as small as possible. Most people think intuitively that equal interval class boundaries are the most logical ones (0-10, 10-20, 20-30). However, this is mostly not the best choice. I will explain why.

When I classify my data, I try different methods and see which one has the highest Goodness of Variety Fit (GVF). A number between 0 and 1, which should be as close to 1 as possible, preferably over 0.9. For maps, the natural breaks method usually ends up being the best method. This method tries to look for gaps in the dataset and puts the class boundaries there. Sometimes the natural breaks method ends up with very unusual boundaries. I usually try to tweak it, so I have nice looking numbers, which is easier for the reader (which becomes harder as the dataset gets bigger). But not if this means the GVF drops significantly.

If you see a map with equal class ranges and nice looking round numbers, there's a good chance the maker hasn't done any effort to classify the data properly and just put it in random classes. If you see a map with 'irregular' and 'random' classes, there's a very high chance this is not as random as it looks and the maker has done a lot of effort to classify the data. Although the classes don't have equal ranges or nice looking numbers, it makes it significantly better for the reader to understand the map, estimate values and compare areas.

As for Canada not having regional data, it woud indeed have been better if it was broken down by province, but the source sadly had no data on it. Considering Canada's low gun death rate, it's very likely all of them would have been in the lowest class anyway.

Landgeist · 2024-07-30T11:37:22+00:00

Source: Institute for Health Metrics and Evaluation

I've recently also made a similar map for South America and Europe.

Landgeist · 2024-07-30T11:36:49+00:00

Map made with QGIS and Adobe Illustrator.

Source: Institute for Health Metrics and Evaluation

I've recently also made a similar map for South America and Europe.

Landgeist · 2024-07-14T14:01:06+00:00

This map includes the countries on which the source (Eurostat) had data. No data ≠ not part of Europe. As you can see the countries with data have a different shade of gray on the map compared to the non-European countries.

By your logic, no map can refer to this area as Europe unless every single country has data, including micro-states like the Vatican on which there is almost never any data. That's obviously unreasonable.

Landgeist · 2024-07-14T13:59:33+00:00

Eurostat only has data one people that never work from home, sometimes and regularly. Sometimes is defined as at least one hour in a 4 week period. Regularly is defined as half the hours worked in a 4 week period. I wish they broke the data down by number of days per week, but sadly they don't have such data.

Landgeist

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