A personal (but techy) project I’ve been doing on the side recently has just reached a milestone.
Some time in the next year we are going to move out of the city centre. I want to live as far away from the concrete jungle, rumbling juggernauts and grumpy people as I can, but… it would be nice not to be too far away from friends, activities and shopping….
There are many parameters involved (and in time I will put them all on a map!), but probably the most important one is that our new home must be within a reasonable transit time by public transport of the city centre. But reasonable is relative… if the new place is great it could be 40 minutes, for “ok” it would have to be less than 30, and “fantastic” might get us to stretch to 50 minutes. The trips shouldn’t have a frequency (during the day) of more than 20 minutes either, otherwise we’d always be missing/waiting for a train…
So how can I visualise these regions on a map?
- Write an interface to the MVV (the public transport provider in Munich) web interface. Unfortunately they don’t have a documented API, but they do have a RESTful interface, and a Java interface already exists (thanks Andreas!). Between that and some further reverse engineering it wasn’t too difficult. I’ll be publishing that module soon too.
- Localise all the stops in the region, and determine the transit times, and frequencies from the central station (Munich Hauptbahnhof) to each of the stops. Throw all that data into a database for easy access. (SQLite is your friend).
- Write a Perl interface to Google Fusion Tables (there are interfaces in Python and PHP already, but I prefer Perl…).
- Analyse the data, generate polygons around each stop indicating the distance you could walk (assuming 4kmh average walking speed) from that stop after having travelled from the central station, within a time limit (10 minutes, 20 minutes, …)
- Convert the polygons to KML (that’s an XML variant which google uses for representing geographic data).
- Upload the polygons to Fusion tables
- Do some tweaking with the colouring so that the data is visually appealing and understandable.
For my next party trick I’ll be putting proximity to shopping centres, parks on the map, and possibly doing the same representation for transit time with a car. Any other ideas what would be relevant, and pretty on the map?