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Why East Germany is a map zombie
Three decades after the demise of the GDR, its familiar contours keep coming back from the dead.
- East Germany has been dead for a little more than three decades.
- But the former GDR just keeps popping up on all kinds of maps.
- It's a sign that life in the east of Germany is still very different from the west.
Forgotten, but not gone
The Berlin Wall in 1986, seen from West Berlin.
Credit: Noir, CC BY-SA 3.0
The GDR may be forgotten, but it's not gone. Apart from a shrinking handful of diehard nostalgics, nobody mourns the passing of the German Democratic Republic, as communist East Germany (1949-1990) was officially known.
It became such an exemplar of the chasm between the high ideals and grim reality of Soviet-style socialism that the regime literally had to fence in its citizens to keep them from running away. Up until the building of the Berlin Wall (1961), hundreds of East Germans each day 'voted with their feet', defecting to West Germany – decadent and capitalist, yes; hence also a lot more fun.
Inevitably, the fall of the Wall in 1989 was the death-knell for East Germany. We've just passed the 30th anniversary of German reunification, which came into effect on October 3, 1990. But after three decades of painful economic, political, and cultural adjustments, the ghost of East Germany lingers on the map.
Like secret messages that become visible under UV light, the contours of the GDR come out when you apply the right data filters. And not just once or twice. Again and again, we see the old (and to some, familiar) borders emerge. In other words, the German Democratic Republic is a map zombie. That's because life continues to be different in former East Germany – even if it's now just the east of Germany.
Below are some examples, selected from the Facebook group with the self-explanatory name: East Germany is discernibly visible on this relatable map.
The unhappy east
Happiness map of Germany. Can you spot the GDR?
Credit: Facebook / ARD, infratest / welt.de
East Germans are less happy than their western compatriots. Out of a maximum of 10 on the happiness scale, most of the former GDR colors red (below 7.2), the rest orange (between 7.2 and 7.4).
In the west, few areas are orange and none are red. Most areas are yellow-happy (7.4 to 7.6), and light-green-happy (7.6 to 7.7). Southern Bavaria (dark green; 7.7 and up) is the happiest corner of Germany.
Too bourgeois for the GDR?
Game, set and match!
Credit: Facebook / Laura Edelbacher
In the old Soviet bloc, sports were a propaganda tool, and athletic excellence a way to prove the regime's supremacy on the world stage.
But apparently, tennis was not the right vehicle – perhaps the East German communists thought it too bourgeois. That would explain why there is such a marked difference between east and west when it comes to the distribution of tennis courts.
The average wage in Wolfsburg is double that as in the adjacent area in the former GDR.
Credit: Facebook / Katapult
Thirty years after reunification, Germany's economy remains unbalanced along familiar lines. This map shows the averages for gross monthly wages: below €3000 in red areas (below €2500 in dark red zones). Almost all of the light red areas are in the east, none of the dark red ones are in the west.
Tantalizingly, Germany's highest-earning area (Wolfsburg, €5089) is right on the former East German border, next to an area with half the average wages. Car aficionados will recognize the name of the city as the home of Volkswagen HQ and the world's largest car plant.
Too many Ronnies
Democratic Republic of Ronnyland.
Older British TV viewers will remember a comic duo called "The Two Ronnies." If they had been German comedians, their names would have immediately pegged them as Ossis (eastern Germans).
'Ronny' is as popular in the east as it isn't in the west. In the eastern German state of Saxony-Anhalt (the dark-blue area on the map), between 66 and 78 out of 10,000 Facebook users carry that first name. In the rest of the former GDR (the middle-blue area), it's 54 to 66. In almost all of western Germany, the rate is below 18.
More public childcare
In the east, more than half the kids under three attend publicly-funded daycare.
The legacy of the communist past isn't all bad, it seems. Some collectivist traditions and provisions survive. Like more public childcare. This map shows the share of under-threes going to publicly-funded daycare centers: over 50 percent in most of the former GDR.
Mosques vs. hazelnut spread
Like twins separated at birth, east and west developed fascinating differences.
Like one of those sets of twins separated at birth, East and West Germany are a fascinating study in similarities and differences – some large, some small. The economic powerhouse that West Germany became needed foreign workers. Many came from Turkey, as evidenced by this map of mosques in Germany: only a handful are in the east.
In its decades alone, East Germany developed a range of household products, often barely disguised copies of western consumer goods. Many are on display in Berlin's DDR Museum. Nudossi, often dismissively called 'Ost-Nutella', is one of the rare brands that survived reunification. Perhaps that's because the spread contains 36 percent hazelnuts, almost three times the amount of actual Nutella (13 percent). Still, Wessis (western Germans) are clearly less keen on the stuff.
Far left, far right
Voting patterns in the east tend to be more eccentric in the east.
Credit: Facebook / GeoCurrents
Voting patterns in the east tend to be more eccentric in the east. The map on the left shows the results for the 2013 federal elections of Die Linke (the Left Party), which positions itself firmly to the left of the SPD, the mainstream social-democratic party. Die Linke garnered between 20 percent and a quarter of the votes right across the former GDR, and was nowhere near as successful anywhere else in Germany.
More recently, the right-wing populists of Alternative für Deutschland (AfD) have found a lot of support in the east. The undated map shows voting intentions for recent upcoming state elections. AfD is particularly strong in the south of the former GDR (26 percent in Saxony, 22 percent in Thuringia). Its highest score in the west is 11.6 percent in Baden-Württemberg.
Catholic, Protestant and None
'Nones' are the majority throughout East Germany.
Confessionally, Germany also remains a divided nation. This map shows which religion dominates where. Catholics predominate in the south and west (dark red: majority, light red: plurality). Protestants are a majority in the north and middle (dark blue), a plurality in the southwest (light blue).
East Germany is easily discernible: it's the part where the main religious affiliation is 'none'. That also includes the whole of Berlin (including the western half), plus the western cities of Hamburg and Frankfurt.
Poor overall, but not poorest overall
The western state of North Rhine-Westphalia has an ever higher poverty rate than the former GDR.
Credit: Facebook / Tagesschau
The former GDR has a consistently high poverty rate: an average of 17.5 percent throughout all six Länder (states). But there's a silver lining, of sorts: the poverty rate is even higher in the western state of North Rhine-Westphalia (18.1 percent), which contains the Ruhrgebiet, a.k.a. Germany's Rust Belt.
The R1a haplogroup is a genetic marker associated with Slavic populations.
The former border between East and West Germany mirrors a much older one: the western extent of the Slavic zone around the year 1000. This map shows the spread of the R1a haplogroup among locals.
This genetic marker is associated with Slavic populations. It is prevalent throughout the former GDR, particularly the south – and in eastern Austria, by the way. R1a 'islands' further west may be the result of more recent immigration waves, by Polish guest workers for example.
Streetcars and streetlights
In Berlin, the past is never dead. In fact, it's not even past.
And finally, two images that zoom in on Berlin. Now the reunified capital of a reunified country, before 1990 it was as divided as Germany itself. And that is still visible, if you know where to look.
At the map of Berlin's streetcars (top), for example. West Berlin never took the step to restore the pre-war streetcar network on its territory. East Berlin did. And that's still the case – with one exception: a single line was extended from the east to the west, a rare example of the west adopting anything 'eastern'.
When night falls, the division between east and west can still be seen from the sky. In the east, street lights use sodium vapor lamps, providing a warm orange glow. In the west, the lamps are fluorescent, resulting in a brighter yellow light.
All maps taken from the Facebook group East Germany is clearly visible on this relatable map. Where possible, credit was given to the original content provider.
Strange Maps #1063
Got a strange map? Let me know at firstname.lastname@example.org.
Scientists discover what our human ancestors were making inside the Wonderwerk Cave in South Africa 1.8 million years ago.
- Researchers find evidence of early tool-making and fire use inside the Wonderwerk Cave in Africa.
- The scientists date the human activity in the cave to 1.8 million years ago.
- The evidence is the earliest found yet and advances our understanding of human evolution.
One of the oldest activities carried out by humans has been identified in a cave in South Africa. A team of geologists and archaeologists found evidence that our ancestors were making fire and tools in the Wonderwerk Cave in the country's Kalahari Desert some 1.8 million years ago.
A new study published in the journal Quaternary Science Reviews from researchers at the Hebrew University of Jerusalem and the University of Toronto proposes that Wonderwerk — which means "miracle" in Afrikaans — contains the oldest evidence of human activity discovered.
"We can now say with confidence that our human ancestors were making simple Oldowan stone tools inside the Wonderwerk Cave 1.8 million years ago," shared the study's lead author Professor Ron Shaar from Hebrew University.
Oldowan stone tools are the earliest type of tools that date as far back as 2.6 million years ago. An Oldowan tool, which was useful for chopping, was made by chipping flakes off of one stone by hitting it with another stone.
An Oldowan stone toolCredit: Wikimedia / Public domain
Professor Shaar explained that Wonderwerk is different from other ancient sites where tool shards have been found because it is a cave and not in the open air, where sample origins are harder to pinpoint and contamination is possible.
Studying the cave, the researchers were able to pinpoint the time over one million years ago when a shift from Oldowan tools to the earliest handaxes could be observed. Investigating deeper in the cave, the scientists also established that a purposeful use of fire could be dated to one million years back.
This is significant because examples of early fire use usually come from sites in the open air, where there is the possibility that they resulted from wildfires. The remnants of ancient fires in a cave — including burned bones, ash, and tools — contain clear clues as to their purpose.
To precisely date their discovery, the researchers relied on paleomagnetism and burial dating to measure magnetic signals from the remains hidden within a sedimentary rock layer that was 2.5 meters thick. Prehistoric clay particles that settled on the cave floor exhibit magnetization and can show the direction of the ancient earth's magnetic field. Knowing the dates of magnetic field reversals allowed the scientists to narrow down the date range of the cave layers.
The Kalahari desert Wonderwerk CaveCredit: Michael Chazan / Hebrew University of Jerusalem
Professor Ari Matmon of Hebrew University used another dating method to solidify their conclusions, focusing on isotopes within quartz particles in the sand that "have a built-in geological clock that starts ticking when they enter a cave." He elaborated that in their lab, the scientists were "able to measure the concentrations of specific isotopes in those particles and deduce how much time had passed since those grains of sand entered the cave."
Finding the exact dates of human activity in the Wonderwerk Cave could lead to a better understanding of human evolution in Africa as well as the way of life of our early ancestors.
If you ask your maps app to find "restaurants that aren't McDonald's," you won't like the result.
- The Chinese Room thought experiment is designed to show how understanding something cannot be reduced to an "input-process-output" model.
- Artificial intelligence today is becoming increasingly sophisticated thanks to learning algorithms but still fails to demonstrate true understanding.
- All humans demonstrate computational habits when we first learn a new skill, until this somehow becomes understanding.
It's your first day at work, and a new colleague, Kendall, catches you over coffee.
"You watch the game last night?" she says. You're desperate to make friends, but you hate football.
"Sure, I can't believe that result," you say, vaguely, and it works. She nods happily and talks at you for a while. Every day after that, you live a lie. You listen to a football podcast on the weekend and then regurgitate whatever it is you hear. You have no idea what you're saying, but it seems to impress Kendall. You somehow manage to come across as an expert, and soon she won't stop talking football with you.
The question is: do you actually know about football, or are you imitating knowledge? And what's the difference? Welcome to philosopher John Searle's "Chinese Room."
The Chinese Room
Searle's argument was designed as a critique of what's called a "functionalist" view of mind. This is the philosophy that argues that our mind can be explained fully by what role it plays, or in other words, what it does or what "function" it has.
One form of functionalism sees the human mind as following an "input-process-output" model. We have the input of our senses, the process of our brains, and a behavioral output. Searle thought this was at best an oversimplification, and his Chinese Room thought experiment goes to show how human minds are not simply biological computers. It goes like this:
Imagine a room, and inside is John, who can't speak a word of Chinese. Outside the room, a Chinese person sends a message into the room in Chinese. Luckily, John has an "if-then" book for Chinese characters. For instance, if he gets <你好吗>, the proper reply is <我还好>. All John has to do is follow his instruction book.
The Chinese speaker outside of the room thinks they're talking to someone inside who knows Chinese. But in reality, it's just John with his fancy book.
What is understanding?
Does John understand Chinese? The Chinese Room is, by all accounts, a computational view of the mind, yet it seems that something is missing. Truly understanding something is not an "if-then" automated response. John is missing that sinking in feeling, the absorption, the bit of understanding that's so hard to express. Understanding a language doesn't work like this. Humans are not Google Translate.
And yet, this is how AIs are programmed. A computer system is programmed to provide a certain output based on a finite list of certain inputs. If I double click the mouse, I open a file. If you type a letter, your monitor displays tiny black squiggles. If we press the right buttons in order, we win at Mario Kart. Input — Process — Output.
Can imitation become so fluid or competent that it is understanding.
But AIs don't know what they're doing, and Google Translate doesn't really understand what it's saying, does it? They're just following a programmer's orders. If I say, "Will it rain tomorrow?" Siri can look up the weather. But if I ask, "Will water fall from the clouds tomorrow?" it'll be stumped. A human would not (although they might look at you oddly).
A fun way to test just how little an AI understands us is to ask your maps app to find "restaurants that aren't McDonald's." Unsurprisingly, you won't get what you want.
The Future of AI
To be fair, the field of artificial intelligence is just getting started. Yes, it's easy right now to trick our voice assistant apps, and search engines can be frustratingly unhelpful at times. But that doesn't mean AI will always be like that. It might be that the problem is only one of complexity and sophistication, rather than anything else. It might be that the "if-then" rule book just needs work. Things like "the McDonald's test" or AI's inability to respond to original questions reveal only a limitation in programming. Given that language and the list of possible questions is finite, it's quite possible that AI will be able to (at the very least) perfectly mimic a human response in the not too distant future.
What's more, AIs today have increasingly advanced learning capabilities. Algorithms are no longer simply input-process-output but rather allow systems to search for information and adapt anew to what they receive.
A notorious example of this occurred when a Microsoft chat bot started spouting bigotry and racism after "learning" from what it read on Twitter. (Although, this might just say more about Twitter than AI.) Or, more sinister perhaps, two Facebook chat bots were shut down after it was discovered that they were not only talking to each other but were doing so in an invented language. Did they understand what they were doing? Who's to say that, with enough learning and enough practice, an AI "Chinese Room" might not reach understanding?
Can imitation become understanding?
We've all been a "Chinese Room" at times — be it talking about sports at work, cramming for an exam, using a word we didn't entirely know the meaning of, or calculating math problems. We can all mimic understanding, but it also begs the question: can imitation become so fluid or competent that it is understanding.
The old adage "fake it, 'till you make it" has been proven true over and over. If you repeat an action enough times, it becomes easy and habitual. For instance, when you practice a language, musical instrument, or a math calculation, then after a while, it becomes second nature. Our brain changes with repetition.
So, it might just be that we all start off as Chinese Rooms when we learn something new, but this still leaves us with a pertinent question: when, how, and at what point does John actually understand Chinese? More importantly, will Siri or Alexa ever understand you?
With the rise of Big Data, methods used to study the movement of stars or atoms can now reveal the movement of people. This could have important implications for cities.
- A treasure trove of mobility data from devices like smartphones has allowed the field of "city science" to blossom.
- I recently was part of team that compared mobility patterns in Brazilian and American cities.
- We found that, in many cities, low-income and high-income residents rarely travel to the same geographic locations. Such segregation has major implications for urban design.
Almost 55 percent of the world's seven billion people live in cities. And unless the COVID-19 pandemic puts a serious — and I do mean serious — dent in long-term trends, the urban fraction will climb almost to 70 percent by midcentury. Given that our project of civilization is staring down a climate crisis, the massive population shift to urban areas is something that could really use some "sciencing."
Is urbanization going to make things worse? Will it make things better? Will it lead to more human thriving or more grinding poverty and inequality? These questions need answers, and a science of cities, if there was such a thing, could provide answers.
Good news. There already is one!
The science of cities
With the rise of Big Data (for better or worse), scientists from a range of disciplines are getting an unprecedented view into the beating heart of cities and their dynamics. Of course, really smart people have been studying cities scientifically for a long time. But Big Data methods have accelerated what's possible to warp speed. As "exhibit A" for the rise of a new era of city science, let me introduce you to the field of "human mobility" and a new study just published by a team I was on.
Credit: nonnie192 / 405009778 via Adobe Stock
Human mobility is a field that's been amped up by all those location-enabled devices we carry around and the large-scale datasets of our activities, such as credit card purchases, taxi rides, and mobile phone usage. These days, all of us are leaving digital breadcrumbs of our everyday activities, particularly our movements around towns and cities. Using anonymized versions of these datasets (no names please), scientists can look for patterns in how large collections of people engage in daily travel and how these movements correlate with key social factors like income, health, and education.
There have been many studies like this in the recent past. For example, researchers looking at mobility patterns in Louisville, Kentucky found that low-income residents tended to travel further on average than affluent ones. Another study found that mobility patterns across different socioeconomic classes exhibit very similar characteristics in Boston and Singapore. And an analysis of mobility in Bogota, Colombia found that the most mobile population was neither the poorest nor the wealthiest citizens but the upper-middle class.
These were all excellent studies, but it was hard to make general conclusions from them. They seemed to point in different directions. The team I was part of wanted to get a broader, comparative view of human mobility and income. Through a partnership with Google, we were able to compare data from two countries — Brazil and the United States — of relatively equal populations but at different points on the "development spectrum." By comparing mobility patterns both within and between the two countries, we hoped to gain a better understanding of how people at different income levels moved around each day.
Mobility in Brazil vs. United States
Socioeconomic mobility "heatmaps" for selected cities in the U.S. and Brazil. The colors represent destination based on income level. Red depicts destinations traveled by low-income residents, while blue depicts destinations traveled by high-income residents. Overlapping areas are colored purple.Credit: Hugo Barbosa et al., Scientific Reports, 2021.
The results were remarkable. In a figure from our paper (shown above), it's clear that we found two distinct kinds of relationship between income and mobility in cities.
The first was a relatively sharp distinction between where people in lower and higher income brackets traveled each day. For example, in my hometown of Rochester, New York or Detroit, the places visited by the two income groups (e.g., job sites, shopping centers, doctors' offices) were relatively partitioned. In other words, people from low-income and high-income neighborhoods were not mixing very much, meaning they weren't spending time in the same geographical locations. In addition, lower income groups traveled to the city center more often, while upper income groups traveled around the outer suburbs.
The second kind of relationship was exemplified by cities like Boston and Atlanta, which didn't show this kind of partitioning. There was a much higher degree of mixing in terms of travel each day, indicating that income was less of a factor for determining where people lived or traveled.
In Brazil, however, all the cities showed the kind of income-based segregation seen in U.S. cities like Rochester and Detroit. There was a clear separation of regions visited with practically no overlap. And unlike the U.S., visits by the wealthy were strongly concentrated in the city centers, while the poor largely traversed the periphery.
Data-driven urban design
Our results have straightforward implications for city design. As we wrote in the paper, "To the extent that it is undesirable to have cities with residents whose ability to navigate and access resources is dependent on their socioeconomic status, public policy measures to mitigate this phenomenon are the need of the hour." That means we need better housing and public transportation policies.
But while our study shows there are clear links between income disparity and mobility patterns, it also shows something else important. As an astrophysicist who spent decades applying quantitative methods to stars and planets, I am amazed at how deep we can now dive into understanding cities using similar methods. We have truly entered a new era in the study of cities and all human systems. Hopefully, we'll use this new power for good.
A small percentage of people who consume psychedelics experience strange lingering effects, sometimes years after they took the drug.