DATA EXPLORATIONS
  • Data Science Lab
  • Lucid Analytics Project

The World Through Whose Eyes?

4/3/2017

 
This is part 3 of an analysis of costs of living and local purchasing power, in five hundred major cities around the world.

For part one, click here: Primary Drivers of Costs of Living, Worldwide
For part two, click here:  Are People in More Expensive Countries Richer?

The data was sourced from Numbeo.com, which hosts user-contributed data - current within the last 18 months.
The IPython Notebook for this project is available on github.

Rich and poor, cheap and expensive - these are all relative terms. To an American tourist, for example, China might be cheap. But to a Cambodian, China is fairly expensive. What we are interested in determining is: how does the cost of living in various countries around the world look,  depending on your country of origin.

To this end, we've put together two visualizations, using Tableau. (This was fairly easy to do, because we'd already crunched the numbers in the first two sections of this analysis of data from Numbeo.com.)

  • For each region in the world, which are the relatively rich countries, and which are relatively poor?
    • Wealth here is expressed in terms of purchasing power parity, rather than absolute income
  • How do the costs of living relate to the costs of living "back home", for citizens of these four countries?
    • America
    • The UK
    • Russia
    • China

Note that for the second visualization, we define the baseline cost of living in each country as the median of the top five most expensive cities in each country - except Russia. For Russia, we used the mean average of costs in St. Petersburg and Moscow. The logic here is that the typical tourist from a given country tends to be richer than average. People with the resources to travel tend to live in bigger, more expensive cities. A disproportionate amount of tourists come from these regions, and the relative costs of living around the world should be in proportion to the costs these tourists actually experience back home.

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  • Data Science Lab
  • Lucid Analytics Project