Figuring out the best place to live in Helsinki

[alkonaut]

I did a similar one for Stockholm but for the greater metro area rather than on street level, for the commute time into the city (When I was looking for a place to live outside). I What I really wanted to do was find "underpriced areas" where commutes are faster than house prices indicate.

In this case I simply took a large number of addresses, ran them through the API for public transit for a random fixed workday (A tuesday morning 8 am commute) to a fixed central address. Then I plotted the times in a heatmap in the format Google maps uses and made a map overlay with the heat map.

It makes a nice spiral galaxy like map where the commuter train stations make little islands of short commute time far away from the city.

http://commutemap.azurewebsites.net/

(Static pic if you don't want to zoom in it and eat my free azure bandwidth... http://prnt.sc/e57bsc). Thinking about it now the dang thing ended up being completely static so it should be possible to just host for free somewhere (github?) rather than cloud. Uses my msdn "testing" sub now...

Outcome: bought place in the orange/red area and started working from home instead :)

Limitations: 1) target address is fixed, new target (e.g. new job location) needs a whole new map. But central stockholm is pretty small and walkable so this works pretty well if your office is central. 2) Interpolation between known points/addresses uses no kind of path finding. It assumes you can walk e.g. 200m in a straight line from an address to the closest bus stop. That might not be what you want to do if it e.g. means crossing water...

[blackbagboys]

For those interested, there is a website that does this for a number of cities, mostly clustered in the US & Europe: http://www.mapnificent.net/

[alkonaut]

New url (moved off Azure as the quota was reached)

https://andersforsgren.github.io/commutemap/

[muninn_]

That's cool. Could I bother you for more information on how to do this? I'd like to try that for where I live.

Thanks

[dmd]

Error 403 - This web app is stopped.

[janober]

I had actually almost exactly the same problem that is why I created: http://crib.ninja It allows to save apartments across different websites and automatically extracts the information (like rent, size, bedrooms, bathrooms, location, ...). The apartments can then also be displayed on a map with a kind of travel-time heatmap from https://www.route360.net. Additionally is it possible to sort or filter by any of the properties and invite others to collaborate in real-time.

Disclaimer: Like written above I created it so I am obviously the founder

[edwintorok]

The heat map integration is nice, I wonder why real-estate listing websites don't provide something like this already. Maybe there is a business opportunity for you there (to integrate this with some of the websites themselves that you support).

[touristtam]

Would be nice to put that on the front page: Currently we concentrate on the US and Germany.

[juskrey]

In this type of the task only payoff makes sense. With 30B+ paths and additional f(x) of path and sum of paths, real life optimization is not really possible - I'll bet in the middle of the night that some simple heuristics (e.g. proximity to one of the major transport hubs) will perform much much better. And city prices are likely already reflecting this, contrary to what author claims.

[alex_duf]

That's considering commute time is somehow directly related to price, which isn't true.

Your commute depends on your job, where the price depends on safety, size, shops in the area, noise pollution, air pollution, parc proximity etc...

Connection is one factor but you can't just say "it's expensive, therefore faster to go to work"

[jabl]

Nice.

Nitpick: Rainbow (jet) color maps can be confusing. Better to use a perceptually uniform one such as viridis, see e.g. https://bids.github.io/colormap/

[elsherbini]

And if you don't like the green, you can use different flavors of cubehelix family color spaces[0].

[0] https://jiffyclub.github.io/palettable/cubehelix/

[1] http://www.ifweassume.com/2013/05/cubehelix-or-how-i-learned...

[ghuntley]

Nice work, is the source available? I'd love to do something similar for Sydney, Australia.

[lvanhala]

Unfortunately not. I created it with various programs I've written for my data visualisation videos (https://www.youtube.com/user/laurivanhala )

[sleepychu]

Not at the moment, https://github.com/lvanhala?tab=repositories doesn't list it.

[qubex]

This reminds me of the "Space Syntax" school of urban planning and architecture (which basically adopts a computational/graph-theoretic/topological approach by computing the simplicity of paths between various points and all other points).

https://en.m.wikipedia.org/wiki/Space_syntax

[benkarst]

Genius. My only comment is in how you measure the value of an address. It seems very likely that your algorithm would converge to areas that are only centrally located.

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?

[aaron-lebo]

Isn't his algorithm pretty bluntly designed to do this?

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?

[late]

"My only comment is in how you measure the value of an address." This. Having lived in the center (reddish area) and later also in southern Helsinki (bluish green), I strongly prefer the latter due to it's proximity to the sea and as it's way quieter area without busses, trams or general traffic. And judging by housing prices so do many others who live in the city.

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.

[therealmarv]

From what I read there I think he assumes he is always visiting his homebase in between. But when he travels in one day from client A directly to client B (and even C) without visiting his homebase the stuff get's much more complex and results may vary. If you want to know more do research about the "traveling salesman problem". TL;DR: It's not so easy.

[onion2k]

If you want to know more do research about the "traveling salesman problem". TL;DR: It's not so easy.

It's not easy to optimize the travelling salesman problem, but if you're happy to brute force it using 30B searches it's incredibly straightforward.

[adrianN]

TSP is actually pretty easy as NP-hard problems go, because it's important in practice (chip layout!) and hence gets considerable attention. Instances with millions of cities can be solved nearly to optimality.

[andreareina]

Could be that he only works by the day, so going from client A to client B will be rare.

Disregarding changes in travel time due to the different time of say, we know that A -> Origin + Origin -> B is an upper bound on A -> B so the solution is still good even if it's not quite optimal.

[foota]

For a small number of clients tsp isn't so bad. The best known exact algorithm is n^2*2^n. And absolutely trivial for the approximate solution algorithms.

[kayoone]

usually development contract jobs are for weeks/months so i think usually you would go only to one client per day.

[endless1234]

>However, I have never seen any data on how well the public transit works in the different parts of the city.

Have you seen http://mak.hsl.fi/? It's kind of that, though you do have to specify the starting/end point.

[wingerlang]

Did you do it for /each address/ or each building? It seems like comparing each building (or even block?) to the rest would be faster in that case.

How long did it all take?

[lvanhala]

I did it for each street address number. Definitely an overkill but it only took a night or two (didn't check) so I didn't bother to optimize. :)

[chki]

Does this data weigh the importance of certain locations? Because to me it seems that it doesn't which would seriously harm the informativeness of the set.

As a consultant you surely don't travel to every part of the city as frequently, and maybe the expensive neighbourhoods are rightfully expensive because that is the place where those who hire consultants live.

Nonetheless a really cool visualisation/idea!

[baq]

30B searches looks like an overkill... should find a way of creating a graph and use https://en.wikipedia.org/wiki/Floyd%E2%80%93Warshall_algorit... or something on that.

the end result is nice though.

[ageofwant]

Just in case anyone missed it http://www.telegraph.co.uk/content/dam/Travel/2016/December/...

[riskneural]

Really interesting. In the end, how did the results compare with the locations of established client firms, e.g. kpmg, accenture, pwc, e and y, deloitte, bcg, mckinsey, bain?

[kensai]

The lake by the "My Summer Car". :p

[alkoumpa]

if your objective is to optimize the best place to buy a house, shouldn't you account about how the travel times of these 30B routes change over time?

[guard-of-terra]

It looks like a palm touch.