This is parts of the orienteering map used

An orienteering map is very detailed with a lot of symbols and lines showing all sorts of information. For the runner, whos is usually heading from A to B, the most important is to see how fast he or she can run through the area. By looking at the symbols one could decide on the route to take.

In the book Banläggning (pg 122) published by the Swedish Orienteering Federation we could read that they have made calculations of how fast the best runners run on different types of surfaces. For example running in a normal forest will be at the speed of 5 minutes / kilometer. Road has 3.3 minutes / kilometers. This means that running on a road is a lot faster than running in the forest. Also the authors has made calculations of how much slower it is to be running uphill and downhill depending on the degree of the slope.

So to be able to get the GIS applications to do the job we needed not only to class all different types of surface types (paths, roads, forest, marshes), but we also needed all height information so we could calculate slope values.

the countour lines of the map

The problem with orienteering maps is that the contour lines that show height information are not classed with their actual height, they’re just lines. That’s enough for an orienteer but not for us. Since we needed all the height information we had to process the contour lines in AutoDem. This application did the job well, but it took time since I had to class many of the lines manually… Then the problem was to convert this information to one of our applications we were going to use for all the route calculations. It was HARD, but finally we found out a way. There are so many file formats in the GIS world, it’s crazy! It’s like if every developer has been living on their own planet.

Anyway. Finally we ended up with something like this:

This image shows the elevation by colours instead of contour lines, which was great for us! The darker red means bigger hills. After that we started classing all the different slopes in the elevation map so that steep hills gets higher friction values than flat surfaces.

Then we started to class all kinds of symbols and lines in the map, so that each symbol and line had a unique friction value. When adding all together we ended up with the image below. Here the darker red means slower speed. Roads are the fastest with bright yellow and parts of the forest with mashes etc are the slowest.

Next step was to combine these two maps into one. And it looked something like this:

Now we had all information we needed! Next step was to put out all the control points so that the GIS software could calculate how much it would “cost” for to get to every place in the map. Here’s an example, where it’s been calculating the time it takes to run to each place in the map with a start from the northern part (the lightest yellow). The darker colour, the longer time.

Next step was to use a function called pathway which calculate the fastest route from one point (the light yellow above) to any other in the map. The result is a line and the image below shows this line from the start to control number one.

As you can see, the application doesn’t want to take the route straight on. Instead the line follows a lot of paths and roads north of the line, passing roundint the lake from the north side. Just to give you an idea of this route compared to what “human” orienteers would choose, I asked some friends to draw their perfect route choice:

Here’s another route:

Final map:

So is these computer drawn route choices perfect? No, not perfect, but they are good! There is one big problem that we didn’t issue and that is some problems with the slope friction. First of all we had to do use the same friction values for downhill and uphill. In the graph provided by Banläggning, running downhill is even faster than running on flat surface, but only to one extent. When it gets too steep we can’t run fast and have to walk/climb down. Another thing is running along a hill side is not really possible in this model since it calculates the overall slope angle and not the actual direction you are taking in the forest. It is possible to do such calculations. That you do slope calculations for all directions of all points in the map but that was too much for the time we had for this project.

One day if I have more time I’m going to try and figure out a quicker way to convert the contour lines into a DEM.

The diagram above shows the visitor statistics for my ski blog skidpepp.se. As you can see it’s turning out to be a big success now towards the winter. From having only around 20-30 visitors each day last month, yesterday was a new all time high with 268 visitors!

What’s even better is that over 70% of the visitors are not first timers which means that people who read it like it and come back! No one-timers only here.

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This is an ongoing project that started as one of my final projects at LCP/LCC in London 2004. It all began the summer of 2002 maybe, when I was hiking in the mountains with two friends and I took a photograph of a mountain and accidently excluded the horizon in the picture. All one could see was a mountain, but since there were no horizon it was really hard to read the picture. How far away is it? Is it taken from above? etc. were some of the questions that came up.

Then a year or so later I decided I wanted to continue explore that phenomena of nonexisting horizons. I first started out by downloading landscape photos from the internet and started cropping out the horizon and then compared them to the original and looked what happened. Then I made some other experiments (examples below).

Then it was time for some action. I borrowed my friends camera (I didn’t have an SLR at the time) and took the train up to Snowdonia in Wales. There I managed to take some ok photos. Then a few weeks later I went to Sweden where I took some more photos. My only 2 rules when taking the pictures were: 1. the horizon has to be excluded, 2. I should try to take the photo completely horisontal (not from above or from below, just straight on!).

Eventually these photos ended up in a book and installation for my degree show.

Here are (some of) the photos from that time

And these are “new” (since 2004) photos

This is a brief description of what I’ve been doing at school lately. The assignment was to do some analysis with GIS, preferably using application handling raster imagery (IDRISI) instead of vectors (MapInfo). Me and my two friends decided to work with mountain huts in Jämtlands län in the swedish mountains. The swedish tourist organisation (STF) hosts a couple of mountain lodges around the swedish mountains where everyone can stay. Well, if you pay, of course. The system is similar to youth hostels but without electricity.

Anyway, for quite a while now I’ve been thinking of where STF might locate new huts. Since I love maps I’ve been studying the maps over the area a lot and seen places that could be nice spots to place another hut.

That’s what we did! By using GIS, based on elevation, inclination, distance and different types of surfaces, we managed to decide where new huts should be located.

First we called a dude at STF and asked him some questions. Apparently the current distances between huts are too long, according to modern hikers. So instead of focusing on routes that are between 13-20km, we had to lower the distance to between 8-12km i think. I say I think because what we did was to create a “friction” map where each pixel represented 50 meters. And then we gave each pixel a number, which was the time it takes to walk that 50 meters on that location. If the pixel was a lake, then it took maybe 2000000 seconds to walk it (just so that the software wouldn’t try that direction). Also depending on the inclination the pixels got different values.

This is the inclination map.

This is the inclination map. Dark areas means impossible to walk, brighter colors are easy to walk.

Then when we had the friction map, we placed the existing huts on the map and created a so called COST map which showed the amount of seconds it takes to get to each pixel.

COST-map of one hut.

Then we simplified it so that it only showed the times that we were interested in (around 5h walk).

COST-map with only two classes.

Same but another hut.

After we’d done all 25 huts and small villages we put them on top of each other to see where we could find a match. Where there was a match of more than two huts, we had a spot! This because we wanted the new location to be accessible from at least two existing huts.

Good places. Darker red means better location!

Our location zoomed in.

Finally we decided on location our new hut just north of the big lake in the west. This because you could reach it from four different places!

Finally, the amazing flow chart.

Yesterday, some of my photos were published along with an article about my friends office space/squat.

DN Article

The photos:

To a great suprise it was a magazine called MARK, which is about architecture etc by the same people who makes FRAME Magazine. First I had no clue why I got it but after looking through it I found a whole spread about my Time travel map!!

This is what they say:

“These days, people don’t talk about how far away they live but about how long it takes to get there. With this realization in mind, Oskar Karlin redesigned the London Underground map as part of a grauation project at the London College of Communication. The London Underground map is a classic 20th-century design, of which a slightly revised version is still in use. Created in the 1930s by Harry Beck, the map does not indicate the distance between stations or the geographical locations of the stations themselves. Karlin’s map combines geographical accuracy with travel times between stations. His diagram of underground lines takes into account trains travelling at different speeds, as well as considerable variations in the number of stations. Tube comuters using his map have a brand-new view of the city.”

When I got home I also noticed that my map is visible on the cover of the magazine too! (look above)

FYI, my project is also mentioned in a dissertation recently by Nicholas Street at Department of Computing, Imperial College London.

MARK Magazine
My post about my time travel map

Another month – another SMET. This month I’ve been out skiing. Unfortunately I couldn’t find two good photos of friends so one of the pictures had to be me – again. Sorry about that. But it was very nice skiing!

This month’s SMET posters are still following the theme of the last ones but they also connects to this month’s music theme – drums! Therefore I chose ping pong not only because it feels like drumming when playing, but also because there are a lot of songs sampling ping pong.

To relate to the overall theme of our clubnight SMET, I thought it would be nice to have some nature inside the club. Me and Sebastian selected some photos from my hike in the mountains last summer, and made a slideshow that were shown on all the monitors inside the place. Thanks to Martin for helping me burning the DVD.

Click on the image below to start watching the small slideshow.

Together with my friend Martin Ström, we have released a site which is probably the geekiest site I’ve ever made. It’s called boomcounter and is a page showing the amount of times Steve says “boom” during his keynotes. This years MacWorld the boomcount was 17.

boomcounter.org