October 2013 - Virostatiq

by virostatiq - October 16, 2013December 31, 2013

My brainwaves during the final episode of Breaking Bad

This is a follow-up to the first self-quantizing post here, my heart rate during the latest episode of the Game of Thrones. See also Graphs of recognized faces per second in House of Cards episodes. This time I thought it’d be fun to measure my brainwaves while watching a critical episode of another TV show.

Breaking Bad is a great TV show, I really recommend it. Even Anthony Hopkins wrote a much publicized fan letter to the crew and the main actor. I watched it avidly until the episode with the fly. Then I took a pause that somehow extended itself up until the finale.

After that all the information has come from the media and from my girlfriend, who still watched it on a regular basis. So these measurements were taken by a person who isn’t biased enough in sense of any emotional involvement with the onscreen characters.

What do brainwaves measure, and what do the levels mean? Here’s a quote from Wikipedia:

delta: adult slow-wave sleep, in babies, has been found during some continuous-attention tasks.
theta: young children, drowsiness or arousal in older children and adults, idling, associated with inhibition of elicited responses (has been found to spike in situations where a person is actively trying to repress a response or action).
alpha: relaxed/reflecting, closing the eyes, also associated with inhibition control, seemingly with the purpose of timing inhibitory activity in different locations across the brain.
beta: alert, active, busy, or anxious thinking, active concentration.
gamma: displays during cross-modal sensory processing (perception that combines two different senses, such as sound and sight), also is shown during short-term memory matching of recognized objects, sounds, or tactile sensations.

There’s also mu, but the Mindwave doesn’t measure it.

Here’s the EEG graph overlaid on the frames. The EEG values have been averaged per shown frame.

The colors are:

red: low alpha
orange: high alpha,
pink: low beta,
light blue: high beta,
green: Attention (synthetic NeuroSky value).

To measure the brainwaves, I used the NeuroSky Mindwave. It’s a convenient and portable personal EEG. It’s a little limited, and one has to learn how to use it properly, but it has a professional quality DSP chip that it uses to calculate two levels the company calls “Attention” and “Meditation”. It also outputs standard alpha, beta, gamma, theta and delta waves.

It looks like this:

: Neurosky Mindwave

By “limited” I mean that it’s sampling brainwave data only twice a second. So whatever it’s happening in your brain now, you can measure after half second in the worst case.

This is the “attention” chart during the episode:

Breaking Bad final episode EEG chart (attention)

Here is the video with onscreen readings. It’s just another way of presenting the same as in the picture above, except there’s more brainwave frequencies shown.

Breaking bad final episode fast forward with EEG readings from Marko O’Hara on Vimeo.

I hope I’m not in copyright violation for that video. It’s essentially unwatchable story-wise.

I’m not totally satisfied with the images and video produced here, but I’m not watching the episode again. I must also admit that I can’t really interpret the charts and video. Attention is self-explanatory, and elevated beta levels also mean increased attention, but do high alpha values mean that I was falling asleep? I was pretty alert while watching.

There’s also possibility of interference. The EEG is essentially a very sensitive voltmeter that measures minute potential changes. Twitching facial muscles, blinking, yawning, … etc., all interfere with the readings. I did look at my second monitor quite a few times to check if the data was being written to a file, maybe some spikes come from that. All in all, I don’t think there are any spoilers here.

Here are some more charts:

by virostatiq - October 8, 2013April 11, 2014

Slovenian real estate prices mapped

There has recently been a flurry of activity by self-made mappers on the net that major media have noticed. It seems that proliferation of tools such as the excellent TileMill does help to make custom maps a relatively painless, yet still laborious process.

In my experience, a major hurdle in this process is getting good data. Governments and corporations around the globe have made acquiring the goods easier, but the quality frequently leaves one wanting. More about this particular dataset later.

This map is my attempt to visualize real estate prices in Slovenia. Buildings are colored according to the most expensive unit they contain, except in some cases where data is bad. More below.

See the map!

A map of real estate prices in Slovenia.

About the dataset

This dataset is provided by GURS, a government institution. I used it before, to make the map of structure ages in Ljubljana. It comes in a variety of formats, such as SHP (geometry) and text (building properties) files, which were clearly dumped from database tables.

It has some severe problems. For example, some bigger and more expensive buildings contain many units, but these units all hold the same value regardless of their useful area. To make matters more complicated, other multiunit buildings don’t hold the same value for the units they contain. They are, in other words, evidenced correctly. Then, there are building compounds, like the nuclear power plant in Krško, in which every building clearly holds the exorbitant value of entire compound. Some other buildings have price value as zero, and so on.

All of this doesn’t even start to address the quality of valuation the government inspectors performed. In the opinion of many property owners, the values are too low. There’s a new round of valuation coming, in which the values are reportedly bound to drop by further five to twenty percent, if I remember correctly. It will be interesting to make another map with the valuation differences some day.

Massaging the data

This means that the above map is my interpretation of the dataset beyond the visualization itself. In calculating values for visualization, there were several decisions I made:

For multiunit buildings, I calculated the cost of square meter for every unit, then colored the building with color value of the most expensive unit. This was necessary, because some buildings contain many communal areas, garages and parking lots, which are all independently valued. I first tried with a simple average value, but the apartment buildings with many parking boxes and garages were then valued deceivingly low. I tried to make the map more apartment-oriented, so this was a necessary decision to make it more accurately reflect the market.
For incorrectly evidenced buildings with same value (high) unit value, I took the price of one unit, divided by sum of unit areas. I could do this on one unit only, but which one? There’s no easy answer. The average seemed the way to go.

I also made a list of the most expensive buildings by their total Euro value. Individual unit values were summed, except in cases described in the second bullet point above. there I simply took the price of one unit. It’s accessible as a separate vector layer under “Most expensive buildings” menu item.

Findings

Turns out the most expensive buildings are mostly power plants, which is not surprising. In Ljubljana, two of the most expensive buildings were completed recently. Well, the Stožice stadium was not really completed. I don’t know whether it was paid for or not – this is a discourse best suited for political tabloids. See the gallery:

It’s also hardly surprising that the capital and the coast are areas with the most expensive real estate available. The state of city of Maribor is sad to see, though, at least in comparison to Ljubljana.

I suggest taking the tour in the map itself, where I go into a little more depth for some towns and cities. Also, be sure to click the “Most expensive buildings”, then hovering the mouse pointer over highlighted buildings to get an idea of their total cost and price per square meter, which in many cases diverges dramatically.

Here are two charts showing price/m2 distribution at different intervals in time.
This one is an all-time chart. Most buildings are valued low, since all ages were taken into account.

This one shows the period between year 2008 and now, in other words, since the crisis struck. Nevertheless, more expensive buildings seem to prevail. No wonder, since they are new. But that probably also means that there’s more apartment building construction relative to countryside development. I’m not really a real estate expert, so if anyone has a suggestion, comment away.

Credits

Inspiration for the tour was this excellent visualization by the Pulitzer center.

I also have to thank the kind people at GURS for providing me with data. They know it’s flawed somewhat, but all in all it’s not so bad.

Disclaimer

As I’ve noted before, this map is a result of my interpretation of government data. I’m in no way I responsible for any misunderstandings arising from this map. If you want to see the actual valuation of your building or building unit, please consult GURS or use their web application to find out.