Tagged: city

Malofiej24 Award 2016 for Best Map in printed media

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This is just a short recap of the project that was awarded a Miguel Urabayen Award as the Best Map in printed media and a gold medal for a feature article at Malofiej24. The whole list of awarded projects is available on their website, our project is listed first, and then again under the Features / Reportajes heading. My colleagues – Aljaž Vesel, Ajda Bevc, Aljaž Vindiš and the graphics editor Samo Ačko – got two more awards, and I congratulate them sincerely. Read more about the award here. The article in dnevnik.si about the awards is here (Slovenian).

The project was my first collaboration with the Dnevnik newspaper for the Objektivno feature section, which mainly features various data visualizations. It was a done in a  somewhat ad-hoc fashion for lack of anything else to do. I realized I’ve been scraping the site where the list of towed cars is published for the owners to check if the car suddenly disappears from a public parking in Ljubljana.  The list doesn’t exist anymore, but it used to be on this page. It contained the car make and model, registration plate number, the location from where it was towed, and datetime stamp. We decided to put it all on the map, and analyze it a bit to see where the luxury makes are towed most.

Here’s the map printout from the newspaper. Click it for the PDF, or click this link.

dnevnik-spiders-net
dnevnik-spiders-net

It’s in Slovenian language, so for English speakers:

  • street segment thickness is for number of cars towed (legend top left)
  • color is for ratio between better and ordinary car makes – we arbitrarily decided what is “better”, but we generally considered more expensive cars, like Audi, Mercedes-Benz, etc. as better. Yellow is for uniform distribution, red is for slightly more better cars, blue for mostly better cars, and black for exclusively better cars. Circles denote regions where mostly better cars were towed. That usually happens in the center and around the new sports stadium.
  • on the bottom left there are some statistics, as well as the list of car makes we used.
  • on the bottom right there are some map cutouts of neuralgic points on the map with some commentary.

One wonders if owners of better cars are more prone to get parking tickets than owners of ordinary cars. I believe that is so, and the sad reason must be an inflated sense of self-importance, which translates in the said persons being convinced that the law doesn’t apply to them, leaving their shiny cars parked in inappropriate places. There’s another side to the story – the underpaid traffic wardens, who are all too happy to make a point by immediately calling the tow truck and ignoring the owners’ pleas even if they come before the towing itself. So there is a social undertone to this project, and I’m happy if the jury members realized this as they deliberated.

The whole project was done on Mapbox platform, except for street geocoding and geometry, which comes from my privately curated database, derived from public dataset, which is in turn managed by this public agency. Many thanks to Mapbox team for the turf.js library, which I used in node.js to properly annotate the geometry with numbers and calculate the ratios. The resulting geojson file was then imported into MapBox Studio, styled by the gifted designer Aljaž Vindiš, and prepared for print.

Some time ago, I released a much more comprehensive project with many visualizations of traffic infractions in Slovenia, which took me months to make, but failed to make any significant traffic or impact in public sphere.

The raw development version is still on my server, see it here. I forgot what I meant with the coloring, but I guess it’s the car make ratio.

The whole thing took us around two days to make. After that, we collaborated on a number of interesting projects, but sadly, as is inevitable in life, the merry group self-disbanded and left the newspaper for greener pastures. I’m looking forward to collaborating again with any of them.

gold-award

Image courtesy of Matjaž Erker.

 

Slovenian real estate prices mapped

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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.

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.
realestate-chart-m2

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.

realestate-chart-m2-2008

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.

See also

Structure ages map in Ljubljana.

Building ages in Ljubljana, Slovenia

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Such is the beauty of open data that when I saw the excellent Portland: The Age of a City by Justin Palmer, I immediately wanted to do something similar, but for my town. The people at the government office (GURS) were kind enough to provide me with the files, and after some coding, here it is.

It’s an exploration of how the city grew through the last century. Blue is old, violet younger, res still younger, bright red the youngest.

Launch the interactive map showing structure ages in Ljubljana

ljubljana-ages

Here’s the number of structures built by years. I was able to identify causes for some spikes in building activity, but not all:

  • 1899: four years after the big earthquake,
  • 1919: rebuilding after WW1? I’m not sure there was much destruction here,
  • 1929: more building – in 1929 Ljublaana became the capital of Dravska banovina,
  • 1949: rebuilding after WW2,
  • 1959, 1969, 1979, 1989: might be effects of Yugoslav loans, but I suspect it’s more of an effect of administrative laziness, resulting in entering new buildings into evidence at the end of each decade,
  • 2004: the last surge of prosperity in independent Slovenia.

Generally, it’s been going downhill from 1969 on. The best spots were probably taken by then.

ages-chart

Here’s a animation of the whole thing. It shows city evolution between years 1500 and 2013, since there’s not much happening before that.

City of Ljubljana – growth between years 1500 – 2013 from Marko O’Hara on Vimeo.

Map was made with TileMill, animation in Processing.

See also the real estate prices map.

 

Noise pollution caused by church towers in Ljubljana

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One Sunday I woke up to incessant and very loud tolling of nearby church bell. It was 9 o’clock in the morning. It didn’t seem fair that an institution can cause so much noise so early. As I work hard during the week, run almost every day, and write software, sometimes until late, I would very much prefer to sleep. The clergy would probably say that honest Christians are already awake at that time, so I’m no good anyway.

I then decided to research the matter. A number of facts surfaced, the most startling of which is a state decree, which states that church bells are not categorized as noise. If an inspector came to my house, measured sound levels while this was going on, and found out that they exceed proscribed levels, he would not be able to fine the aforementioned institution. He would probably bill me for the expenses of his time. But I digress.

Action was taken: city geometry was imported into computer along with church bell coordinates. Aggregate sound pressure for each building was calculated, then ranged so it could be visualized. Additionally, a point where there is least such noise was calculated. You can see results below. The point with least noise is on the green marker in the lower left corner. Lucky owner of that house.

Note: please notify me before embedding this map in your page.

I have to admit that the calculation is naive. It doesn’t take into account the elevation model, neither it accounts for building heights. Sound reflection is also ignored. But my curiosity was satisfied. I do live in the red zone.

Here are same maps on different scales. One is for entire country of Slovenia.

Edit: after this post went viral and other media (Dnevnik.si) published their own versions linking to me, I feel compelled to clarify my position about church bells. Personally, that is, as a person, and not a member of any organization, I’m bothered by long intervals of loud tolling on Sunday mornings. I’m told by other people they don’t like that either, and some other people point out that any attempt at playing music at this volume at similar hour of day would not end well.

I do somewhat like single chimes announcing hours of day, even at night. It’s a part of urban environment, and I’d probably subconsciously miss it should they quit. I’m not against Catholicism, the Church, or faith of any denomination.

When you toll so loudly next time please consider:

  • do unto others as you would have them do to you,
  • would Christ approve of that?

Thanks.