Yes, there is a job WAY up in the Arctic, and 28 people applied to it. I could have sworn too that there used to be jobs in New Zealand, Hawaii and Australia, but oh well.
A similar map counting the number of applications going to each city is available here (this also takes forever to load). However the two maps look pretty much the same, since the resolution of the bubbles are not that great.
What's also not clear in the maps is the actual number of jobs in Waterloo and Toronto areas. To make it clear, here are the top 10 locations with the most number of job openings.
If we take the top 10 locations with the most number of applications, we get something similar.
Now what happens when we look at the cities that get the most applications per opening? We get something COMPLETELY different.
The more "exotic" places like Saint-Hubert (where the Candian Space Agency is located), California and others don't offer many jobs, but the ones that are offered attract a lot of applications. Note though that I would take the exact ordering in the last chart with a grain of salt for two reasons: (1) the position that actually attracted the most number of applications is offered in "Various Locations" -- take a WILD guess what company offered that position (hint: start's with a "G") and (2) a lot of companies lie about the number of openings they have.
What about the places with the lowest applications per job?
I'll leave you to come up with your own conclusions here.
Finally, a note about data and methodology. Jobmine is the system co-op students/employers at Waterloo use to manage job postings and applications. Job name, location, opening and applications data were pulled off of Jobmine 8am this morning (posting closed 12am last night). Some location names are changed slightly to avoid multiple entries per name, and so that Google's Geomap tool would map it correctly (it mapped "London" to "London, England", etc.). When a job opening was in multiple locations, I took the first one. Jobs that put "Multiple Locations" or "Various" or something ambiguous as their location were deleted.
I plan to squeeze more goodies out of this data, so stay tuned. Incidentally, if you are familiar with Google Charts API and know how to make it go faster, please let me know.
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