Now something different.
Off-piste snowboarding in urban or rural areas is a time-consuming hobby especially outside the mountain areas, because the best spots are neither marked nor listed in city maps. A lazy snowboarder wants to find a nice, open hillside with decent slope and sparse treeline, but finding even a few spots requires numerous trips to countless would-be locations. But hey, we have open source GIS tools and Open Data to mine for just that!
So I opened up Quantum GIS and the Raster Calculator.
The prerequisite for a proper snowboarding location is a decent slope. The natural starting point for the analysis was then the digital elevation model for the area close to my home (10 m resolution, made recently available by National Land Survey of Finland). From DEM-raster then calculated the slope of the terrain for each raster cell. The locations where the slope hits the range of 10-50 degrees were easy to filter out, producing the mask raster for possible snowboarding locations (in red, below).
Making Use of Land Use Classification and Land Cover Data
These slope-based locations reside on whatever terrain, so the information about the land use must be assimilated. I used the CORINE 2006 dataset to mask out the built environment, water areas and other irrelevant land use types. Intersecting the resulting mask raster with the previously produced slope-based mask results in the first approximation of the possible off piste snowboarding locations within the urban environment. This raster is then vectorized to facilitate zonal analysis on the basis of land use classification.
Scoring by Zonal Analysis
Now we have the possible locations for off-piste snowboarding, but no way I’m going to visit them all to find the best spots. Therefore I computed a score for each location by conducting a zonal analysis of the land use raster and DEM-raster. The final score was a composition of the following factors:
- the areal proportions of different land cover types inside the location: sparse forests, outcrops or other open areas increases the score of the location; and denser forests decrease the scores,
- the average slope of the terrain inside the given location: highest scores are assigned for 15-30 degrees, lower for the rest, and
- the total area of the location.
The score of a given location represents the propability of finding a good snowboarding spot at the given location. Finally I took the top quartile of the locations by scores and got a nice map to track on my iPad whilst hiking through the urban forests. The colors of the locations refer to the probability of finding a good snowboarding spot (darker is better):
The cover photo on the top of the page was actually taken on the “best” location (shown as the darkest spot on the map above), which actually is the Hirvensalo ski resort – thus validating the map! Of course, since this is all about avoiding exercise, you turn to Google Earth and Google Street View to check those places (too bad the images are all from summer time, so no snow cover is visible):
The mining took less than an hour to complete with free open source software and data. Now I’m just waiting for the next winter season, and the Finnish Meteorological Institute to open their data about the snowcover…