Perhaps it could be improved if the algorithm took an input of common routes and times, then tried to find an optimal location for these routes. This way the algorithm could be scaled as needed and provide a more realistic scenario. Is this something you considered?
So I did my own analysis: I calculated the travel time from every address to every other address in Helsinki around 7:30-8:00am (about 30 billion searches total!). Then I calculated the (weighted) average travel time to anywhere in the city, using amount of jobs in the target area as weight.
That would seemingly bias towards centrally-located addresses (travel time & number of jobs), and his heat maps seem to show this. I believe you could pretty easily duplicate what he's doing with a few dozen randomly sampled routes. Or is there more to it?
Just might spice it up if you partition the bucket of all routes into spaces defined by your lifestyle too. It would be interesting to see what the best address in Helsinki is for hipsters. Or families. Or athletes. Or business people...
This is so cool because anyone who has moved has faced this problem.
Don't get me wrong. I found the map highly interesting but maybe in determining best places to live it's a bit of a stretch. There might more value here for businesses that aim to be easily reachable.