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Fantasy map of Bard College
Fantasy map of Bard College
Fantasy map of Bard College. My first idea was to take a map of Sawkill River watershed, and reinterpret its sub-basins as independent medieval states (right)! They kinda look like states from an old European map; I guess because the borders were natural, and not just randomly drawn.
I had to reduce the scope later on, but add some fantasy features to the landscape (below :) The final version of the map was enthusiastically met by the local D&D community :)
You can download a high-res version here (GoogleDriveLink)
Tivoli, ny: all buildings by construction year
Tivoli, ny: all buildings by construction year
Tivoly, NY, is a reasonably old town, but the US standards, and Zillow.com happened to have an estimated construction date stated for every house in Tivoli. So I clicked through all houses in the neighborhood, and colored them on a map, according to their age! Yellow for 1800s and earlier, orange for 1850s; bright red for 1900s, dark red for 1950s, and down to dark brown for 2000s. The brighter - the older.
You can immediately see the old streets, and the churches. Also a few manors along the Hudson (on the left). But there are also some unexpected houses that look like nothing special if you pass them on the street, but that actually happen to be quite old (one is actually pre-1800, even though the only old part is now buried in the middle, surrounded by later additions and extensions).
Those blurry parts near some buildings are two cemeteries, one park, and one old water mill: places where the landscape itself is old, not just the buildings. I could have also colored some old walls that divide the parcels, and two historical sidewalks, but somehow forgot.
Click on the image if you want; the original is a bit more high-res than it seems!
Walking in Berlin
Walking in Berlin
After moving to Berlin in 2021, I decided to finally give a try to a project I wanted to do for years. To start mapping (or rather, marking on a map) every single street I walked. Just to see how the map develops!
It's a really fun activity that I highly recommend! It helps you learn the city, as if you do it manually (and you should totally do it manually, that's the point!), you have to remember and mentally retrace your steps. Which maps experiences in your head on an internal map, and breathes live into these squiggly intersections on a plan. It also helps you to investigate the city better: as you're revisiting a district, you can also reference the map and check which side-streets are still unexplored, unseen by you, and make a point of walking through them, "collecting" them. Thus, again, better learning the city!
A map after two months of living in Prenzlauer Berg, and moving only on foot.
The same map after 6 months in the city. The central part of East Berlin is now decently explored, but the posh part of West Berlin remain a complete unknown.
Three years in (I shamefully forgot to save good screenshots from one and two years in 😢). One can notice that we moved from expensive Prenzlauer Berg to cheaper Wedding, but still remain confined to one side of the river. Schöneberg remains completely unexplored! It's amazing how one can enjoy a city, explore it, regularly walk through it, and still have no reasons and almost no opportunities to get to the other half of it!
Four years in! And now the map also shows bike rides (in red), as I finally got a bike. Finally visited Marzahn. Haven't visited Wilmersdorf or Köpenick yet.
Etymology of Russian city names
Etymology of Russian city names
This is such an engaging idea: to create a map that shows etymology of each big Russian city, translated to English! But then, research takes forever, and placing names by hand is also so slow! So I gave up.
And yet, isn't it fun that the name "Moscow" probably originally meant "A moist place", while Kaluga was "A Meadow"? Moreover, while most city names in Central Russia have Slavic etymology, some have Finnic origins, like Shatura, which apparently means something like "Snake King". At first I even tried encoding different languages of origins with color, but it only added a whole another layer of complexity.
Overlap of world religions, at a province level
Overlap of world religions, at a province level
The data for this map was mostly taken from Wikipedia. For the CMYK version, color hue encodes the nominal share of different religious traditions: % of cyan for the share of Muslim population (source), magenta – for nominally Christian population (source), yellow – for all other religions, which by exclusion mostly happens to be Eastern religions (Buddhism, Hinduism, Sikhism and Chinese religions), but also local Indigenous religions, where applicable. Judaism as colored purple (which seemed to make sense at the moment, but actually it doesn't).
The amount of black added to the hue shows the measure of openly non-religious people within the population. These numbers are mostly based on the 2006-2008 Gallup poll, in which people were asked whether the find religion is important in their daily lives. 10% of black corresponds to "most religious" regions, while 50% corresponds to "least religious". Countries and regions that were not sampled (China, Algeria, some regions in Russia) were extrapolated from other sources. For the US results of Gallup polls are available per state.
Several largest countries were colored separately per state / province. For Africa data on indigenous beliefs was taken from "Religion in Africa" article; for Russia all colors were based on the data from sreda.org polls. Brazil and Argentina are shown flat (as I couldn't find the data), while Canada and Australia are taken from respective articles.
For India shares of Islam and Christianity are taken from Wikipedia, and all remaining population is assumed to be affiliated with "Eastern Religion". Essentially, I assumed that there are relatively few atheists in India on a per-state basis, which of course may not be true.
For China, distribution of Muslim population was taken from Wikipedia articles for respective provinces, while distribution of Christians was taken from this map, but normalized to lower values (10%), to meet the official numbers (5%) about mid-way, to account for the opposite bias of both sources. As 40% of Chinese people describe themselves as non-religious, but are Chinese culturally, on this map I counted them as "Eastern", but set saturation levels to 50%. Exceptions were made for provinces with religious minorities, such as Tibet and Xinjiang, which are assumed to be proportionally more religious.
Click to see a full resolution version (about 1357x628 or so):
Other color schemes:
Nominal religion only, without black channel to reflect attitude to religion.
"Fluorescent" RGB-version, in which share of green stands for Islam; share of red – for Christianity, and share of blue – for all other religions, except Judaism, which is yellow. Lightness of the color reflects attitude towards religion (same data that is used for "black" channel in the CMYK version). [1357x628].
Scientific research per capita in different countries, 2013
Scientific research per capita in different countries, 2013
Total spending was taken from this Wikipedia page, population came from here, and for countries that are not shown in the R&D list (those with smaller spending values) I linearly extrapolated R&D spending based on their health expenditure per capita. It's an arbitrary choice, but it probably does not matter too much, as these countries all ended up as pale green on the map anyway. The map was generated in Google Fusion.
Known mistakes: Svalbard actually belongs to Norway; Taiwan should not be colored like mainland China, but should be about the same shade as Canada, while Netherlands should be about the same shade as Australia.
One possible interpretation of this map is "How much of their pocket money every citizen of this country spends on R&D". The drawback is that a) people in different countries may have very different amount of pocket money; b) R&D is obviously funded not only from income taxes, but also from corporate taxes.
Another interpretation is the "potential share of engineers and doctors in the population". Number of R&D jobs = R&D spending / average salary, but average salary is (probably) proportional to the cost of living, so "Adjusted (real terms) R&D spending" is proportional to the number of R&D positions in the country. If we now divide this value onto country population, we should get a value proportional to the share of R&D jobs in the country. A potential issue here is that the average R&D salary, as compared to the average salary across all occupations in the country, would probably be very different in different parts of the world. For example in Russia all R&D people live below poverty level, while in the US most of them live rather decently. Still, at least as a rough estimate, it may work. In a way, one may argue that this map assumes a "global world" situation, in which an R&D specialist can move from one country to another, depending on the funding situation.
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