Maps


On Thursday last week the British people voted to leave the EU, sending shockwaves through the British political establishment and the EU leadership. In the aftermath there is a lot of finger-pointing and blame going on, and as I predicted in a comment at Crooked Timber before it happened, people are lining up to blame Labour for what is a very Tory disaster. Here I want to talk about the limited available data on who voted what, to put paid to the idea that this was primarily (or even partly) a Labour failure. I’m then going to talk a bit about the “white working class” and the EU, and also give a brief opinion about what this means for health and the NHS. I intend to be polemical. By way of background, I have British citizenship and British parents, I’ve talked about growing up in Britain before on this blog more than once, and I really am not surprised by this result. I have only lived briefly in Britain since I was 13 – I immigrated to Australia and then worked for a year and a half in the UK on issues related to the NHS (during this period I started my blog, which is why it has the Thames as its header image). All my family still live there and I think in many ways my family present the ideal anti-EU demographic – I grew up in an environment steeped in racism and heirarchies of discrimination that I think people who grew up outside of the Tory working class, or outside of Britain, really can’t understand. This background informs my interpretation of political movements in the UK, and at its base is a simple theoretical position: for many British people, race consciousness always beats class consciousness.

What could possibly go wrong?

What could possibly go wrong?

The demographics of Brexit

There isn’t yet much clear data on who voted what, but we do have two data sources: the electoral returns for the local authorities, and an exit poll conducted by John Ashcroft. Let’s look first at the electoral returns, which are summarized neatly in the Guardian‘s referendum results page. In case that page dies I’ve put some screenshots of its contents here. First is the map, above, which shows clearly the regional pattern of voters: Scotland and the city centres voted remain (yellow) and the country areas voted leave (blue). For reference, the region I grew up in is the area of Wessex in the south west; I’ve magnified it below. This is the land of King Arthur and even contains a tiny separatist movement in the far south west (Cornwall). It doesn’t include Wales, which I’ve had to include a bit of in this map. The yellow (remain) areas are the cities of Bath, Bristol, Cardiff, Exeter and Plymouth. Outside these cities it is entirely blue. I grew up in towns like Salisbury (the furthest Eastward big blue blotch); Frome (south west of that blotch, in light blue); Falmouth (the dark blue patch west of the two small yellow ones) and Cornwall (the light blue patch poking out into the Atlantic). These are areas that benefit hugely from EU funding under the Common Agricultural Policy, were once strongholds for the Lib Dems, and are now shifting fast to UKIP. They’re heavy tourist towns with very low proportions of migrant and non-white people; unlike in London, if you go into a cafe in Torbay (where my parents live now, the dark blue patch east of Plymouth, I think) you’re likely to be served by a white local, rather than an Eastern European worker. These areas have received most of the benefits of the EU, and very few of its migrants, and have been largely isolated from previous waves of Commonwealth migration (ie Indians and Caribbean people).

Oo-arrh, Oi've got a brand new combine 'arvester!

Oo-arrh, Oi’ve got a brand new combine ‘arvester!

These areas are old, with only three major universities in Bristol, Bath and Exeter. They’re rural and tourist-focused, and they’re also repositories of British history, holding places like Stonehenge, Avebury, and Tintagel. They’ve always been a little bit wayward and remote from the concerns of Londoners, so I suppose a bit of restive anti-EU thought makes sense here. But what about the rest of the UK? The Guardian has some graphs showing the proportion of people voting leave/remain by major socio-economic and demographic factors, which I’ve placed below.

Let's make a classic political science error!

Let’s make a classic political science error!

It’s very clear what’s going on here: the more higher-educated, wealthier people, and the more people not born in the UK, the more likely the area is to vote remain (for those not steeped in British class lore, the UK office of national statistics classifies people by their social class, and “ABC1” is the professional and higher class groups). If you remove Scotland from this chart it will probably be even clearer, since Scotland’s poorer areas were more likely to vote remain. Note also that older areas were more likely to vote for leave.

It’s a classic political science error to infer individual voting patterns from area-level statistics, because it’s well-established that these statistics often go in the opposite direction at individual and regional level (Andrew Gelman famously showed this for the USA: richer states are more likely to vote Democrat, but in all states poorer people are more likely to vote Democrat). However, this pattern in this case is so clear that even though we don’t know how individuals in those areas voted, we do know that areas with higher numbers of poor and uneducated people were full of people pissed off at the EU. It’s fundamentally the job of politicians to understand these kinds of big population-level movements in politics, and for Cameron to call a referendum on this topic despite the existence of such a powerful and fundamental dynamic in the electorate is either incredibly reckless, or incredibly ignorant, or both. This stupidity is compounded by the fact that areas with large numbers of poor and uneducated people are more likely to be labour-held areas, so Cameron was going to be relying on his political enemies to support him. I don’t think Corbyn is venal or stupid, but coming hot on the heels of the era of Blair, it’s incredibly risky of Cameron to assume the leadership of the Labour party wouldn’t be venal and stupid enough not to leave him hanging on this issue for cheap political gain.

This brings us to the next issue: who actually voted how in these areas, and was the failure of the leave campaign the fault of Labour and its racist voters? For this we cannot rely on area-level data, but need to look at individuals, and sadly so far the only information we have is from John Aschcroft’s exit poll. I won’t screenshot this poll, which I linked to above, but the conclusion seems to be that this was a very Tory disaster. Here are some key figures:

  • No difference in gender (52% voting leave in both)
  • Young people were much more likely to vote remain (73% for 18-24 vs. 40% for the over 65s)
  • Big trends by social class, with the wealthier more likely to vote remain (a similar difference between the “lowest” and “highest” social class to that in age)
  • Labour, the Greens, the SNP and the Lib Dems voted heavily in favour of remain (over 2/3 for all groups) while Tory and UKIP voted for leave, so that only 20% of leave votes were drawn from Labour, vs. 40% from Tory
  • 33% of leave voters listed immigration as their main concern, and 79% described themselves as English not British

The big caveat on these statistics is that the party affiiliation is based on voting in the 2015 General Election; turnout for this referendum was higher than the 2015 General Election, and so it’s likely that a lot of people who voted in this referendum did not vote in 2015 but did vote in 2010, or never vote; in this case describing them in terms of the last vote they cast may not be very informative. Nonetheless, of those who were recently involved in an election, those who voted for the tenets of the labour party were not interested in leaving. This fact is backed up by looking at the map, where the big labour heartlands in London were all for remain. The Guardian has analysis of some of these heartlands (because of course journalists immediately latch onto the meme that attacks Labour, not the obvious responsibility of all the Tory areas that voted leave). It describes a strong leave sentiment in the otherwise labour-focused area of the Thames estuary (the land of Eastenders), and a suburban revolt outside the Labour heartland areas of Merseyside and Tyneside. Tyneside is a good example: the former industrial heartland and labour stronghold north of the river voted remain, while the more suburban Tory-voting south side went with remain.

My conclusion from this is that the leave vote was driven by pensioners, the “lumpen proletariat”, and Tory voters. The remain vote was driven by labour stalwarts, the educated, and working people in the big cities and former industrial heartlands, who perhaps understand that their future depends on being part of an integrated market. Obviously this is a broad brush, and a disappointingly large number of Labour voters (about 35%) sided with leave. Some people are saying that Corbyn should have gathered these people up with a better campaign, but I think this claim is doubtful. To the extent that Labour voted leave, they’re largely rebelling against the policies of New Labour, and for Corbyn to be more involved in the remain campaign he would have had to have shared a platform with Vampire Blair and the Pig-fucker General. I don’t think this would have convinced more people to vote remain, and would likely have had the opposite effect. If the Tories wanted Labour to help drag the country back from this disaster, they were going to have to make it less of an obvious Tory shitshow, and tell the idiots from New Labour to stay home and out of the sunlight.

What about the white working class revolt?

People do like to bang on about how the average Labour voter is a racist and the only way Labour will get the “white working class” vote back is by appealing to these baser instincts, but I think this is fundamentally flawed. Yes, many working people in the UK are opposed to immigration and can express shockingly racist views, but a lot of these people were prised away from Labour back in the 1980s, and more left during the era of New Labour. I don’t think Labour will ever be able to get these people back, and it’s silly to talk about them as if they are part of the Labour heartland. The sad reality is that British politics realigned in the 1980s, at the same time as its industrial heartland hollowed out, so that the Tories have a reliable stock of poor white people voting for them on racial grounds. This is the “victory” of Thatcher-era politics and the vicious racism of the Daily Mail and the Sun. Amongst these groups, these newspapers have been pushing an anti-EU agenda for 25 years (just try reading the Daily Mail on Europe!), and also a vicious anti-Labour agenda. Of course these papers were going to do all they could to mobilize these readers against the EU in this referendum, and there’s very little the remain campaign can do against 25 years of constant anti-EU propaganda, much of which is straight up lies. This is hardly helped by the willingness of journalists to consistently let the leave campaign get away with their lies about the 350 million pounds (that Farage admitted wouldn’t go to the NHS the morning after the referendum).

It needs to be made clear too that racism was a central part of the leave campaign, and they weren’t deploying a nuanced critique of immigration. The leave campaign was doing very poorly, well behind remain, until they dug up the claims about Syrian refugees, boats on beaches, the sexual assault “nuclear bomb”, the breaking point poster and the constant terror campaign about Turkey joining the EU very soon. Once that stuff came up, leave started catching up rapidly in the polls. Then of course political geniuses like Osborne screwed up the remain campaign with their petulant threats, and the job was done. When people as unscrupulous as Boris Johnson are willing to put out the kind of misleading, deliberately untrue, and viciously racist stuff they did, there’s very little a principled campaign can do except watch the election getting stolen from them. Fundamentally you can’t win a campaign against people who happily tell juicy lies and a media that supports them.

I think a lot of commentators from both left and right in the UK fail to see how potent this stuff is because they didn’t grow up surrounded by it – they grew up in pleasant leafy neighbourhoods to professional or wealthy families, and didn’t have to put up with this stuff day-in, day-out during their childhood. If they did they would know, as I do, just how filthy and nasty the underbelly of the British polity is, and just how ugly its views are. A previous generation of Labour political leaders might have known this, but Tony Blair flayed those people and replaced them with his soulless ghouls, who know nothing except focus groups and servitude to the Elder Gods. I described this kind of politics two years ago on this blog, and this referendum is the vindication of my analysis. There are solutions to this problem, but “giving the racists the chance to shine” is not one of them.

The implications for health policy in the UK

The UK has been out-sourcing medical training and workforce development to Europe and the Commonwealth for years. Up to 26% of doctors and 11% of all NHS personnel come from overseas, a great many from the EU, and once the UK leaves the EU these EU staff will need to be replaced from elsewhere. More could be drawn in from the Commonwealth, but it’s unlikely to be able to fill the shortfall quickly because many Commonwealth countries have only small numbers of medical staff, and may not be able to provide a great deal more. Furthermore, it’s unlikely that a country that just voted to leave the EU out of fear of immigrants is going to suddenly implement policies to bring in more immigrants. The result of this will be further pressure on the NHS workforce, with even more difficulty in replacing staff as they retire and leave at a time when the aging population is putting more and more demand on health services. It takes 10 years to train a doctor and 5 years to train a nurse, but the government has been cutting funding for these training programs (including the nurses bursary) and has been repeatedly warned that it is facing a shortfall in health personnel even without leaving the EU. Pressure on universities is likely to increase with the sudden loss of EU funding, and in the huge economic readjustment that has to happen when EU funds disappear, universities are going to face major shifts in funding sources and needs. Without central organization they are unlikely to prioritize nurse training – they haven’t to date, why should we expect they will do so in the future, with tighter funds?

This problem will be even more pronounced for small and medium enterprises outside of the NHS that provide services to the NHS, and also to financial services companies. At the moment there are a range of barriers to employing non-EU staff that were put in place in response to past concerns about immigration: you have to prove the job can’t be done by a local, and it’s very hard for non-EU workers to bring in spouses. As a result most small companies don’t sponsor visa applications, preferring instead to recruit from the EU where such rules don’t apply. For financial services companies, the sudden loss of their most qualified pool of staff is going to have huge implications, and I suspect for many of them the simplest approach will be to move to Europe. The same will apply to universities, who will suddenly lose access to the best-educated region in the world. This likely won’t affect senior staff but it will have a huge impact on the supply of graduate students and early-career researchers and teachers. These jobs aren’t just boutique jobs for underwater basket-weavers – the UK has a huge pharmaceutical industry that depends on universities and research institutes, as does its high-tech industries like oil exploration services, the arms trade, aerospace, and growth industries like alternative energy. Suddenly putting up barriers to employing people from the most highly-educated part of the world is going to be really bad for high-tech industries in the UK, at a time when industries that primarily employ lower-skilled professionals (like tradespeople) are offshoring rapidly.

This is going to be an economic disaster for the UK for a very long time to come. Their only chance of a decent economic future is to implement an industrial policy, significantly improve funding to health and education, and shift from austerity to a Japan-style deficit-financed industrial society. The only person with a vision to do this – Corbyn – is about to be eaten alive by the Blairite ghouls still shambling through his own party, which will leave the political landscape ruled by Boris Johnson, who has no vision for the UK economy and is going to be so reviled by the time the UK exits that he won’t be able to make anything happen even if he had a sensible idea.

Conclusion

This was a political disaster that is going to leave Cameron, Osborne, Johnson and Farage the most reviled politicians in modern British history. It will likely lead to the breakup of the Union, and if it doesn’t, a return to civil war in Northern Ireland. It will also plunge the UK into a long period of economic collapse that it has no way out of, and no scapegoats for. The EU, coupled with a decent economic policy aimed at renewing British industry, was the only chance for the UK to remain globally relevant and for its citizens to enjoy a good quality of life. Cameron has wrecked that one chance in order to score a victory over the idiots in his own party, in a reckless and breathtakingly stupid political gamble. The tidal wave of economic and social problems about to hit the UK is the perfect proof that conservative politics is a wrecking-ball through modern life, and they should never ever be trusted with power.

I’ve recently been building a fairly complex series of Bayesian spatial regression models in BUGS, and thought I’d share some tips based on hard won experience with the models. The various BUGS packages have the most cryptic and incoherent error messages of any stats software I have ever worked with, and although various Bayesian boosters claim that their modeling approach is intuitive, in my opinion it is the exact opposite of intuitive, and it is extremely hard to configure data for use in the packages. Furthermore, online help is hard to find – google an error message and you will find multiple websites with people asking questions that have never been answered, which is rare in the modern world. I take this as a sign that most people don’t understand the error message, and indeed the BUGS manual includes a list of errors with “possible interpretations” that reads more like the I Ching than a software guide. But Confucius say Enlightenment is not to be found in Black Box Pascal, so here is my experience of BUGS.

The models I’m running are complex, with nested conditional autoregressive structures and the higher level having more than 1000 areas with complex neighbour relationships, and millions of observations. I originally ran them on a completely hideous Hewlett Packard laptop, with 4 cores and 8Gb of RAM. I subsequently upgraded to a Dell Workstation (joy in comparison to HP’s clunky root-kitted horror) with 8 cores and 16Gb of RAM; I’m not sure that hardware is the main barrier to performance here though …

The HP machine had a secret administrator account (arseholes!) so I couldn’t install winBUGS[1], so I started off running OpenBUGS called through R’s R2OpenBUGS package running in RStudio. I use R to set up the data and initial values, because I can’t think of any other way to load millions of observations into a text file without going stir crazy. But when I call OpenBUGS it just hangs … no error messages or any other kind of indication of what is going on. I also can’t tell if it is happening at the data loading or compiling or inits stage.

Some digging around online and I found an old post by Andrew Gelman, observing that BUGS does not work well with “large datasets, multivariate structures, and regression coefficients.”

i.e. pretty much every statistical problem worth doing. Gelman also notes that “efficiently-programmed models can get really long, ugly, and bug-prone,” which seems like a contradiction in terms.

Anyway, noting that my data was large, with multivariate structures and regression coefficients, I thought maybe I should tone it down a bit so I tried using a higher level of spatial heirarchy, which reduces the adjacency matrix by an order of magnitude. Still no dice. It was at this point that I upgraded to the bigger computer.

On the bigger computer the smaller model actually worked! But it didn’t work in the sense that anything meaningful came out of it … It worked in the sense that it reported a completely incomprehensible bug, something like a node having an invalid value. I tried multiple different values and nothing worked, but somewhere on the internet I found someone hinting that you should try running BUGS directly rather than calling through R, so I tried this … having created the data in R, I killed OpenBUGS then opened the OpenBUGS interface directly and input the model, then the data, using the text files created by R[2].

When I did this I could step through the process – model was syntatically correct, then model failed to compile! Given that loading inits comes after compilation, an error telling me that I had the wrong initial value seems a bit misleading… in fact I had an “index out of range” error, and when I investigated I found I had made a mistake preparing one part of the data. So where the actual error was “the model can’t compile because you have provided the wrong data,” when called through R the problem was “you have the wrong initial values” (even though I haven’t actually loaded initial values yet).

WTF?! But let’s step back and look at this process for a moment, because it is seven shades of wrong. When you run R2OpenBUGS in R, it first turns the data and inits into a form that OpenBUGS can read; then it dumps these into a directory; then it opens OpenBUGS and gets OpenBUGS to access those files in a stepwise process – at least, that’s what I see R doing. If I decide to do the model directly in the OpenBUGS graphical interface, then what I do is I get R to make the data, then I use the task manager to kill OpenBUGS, then I call OpenBUGS directly, and get OpenBUGS to access the files R made in a stepwise process. i.e. I do exactly the same thing that R does, but I get completely different error messages.

There are various places on the internet where you might stumble on this advice, but I want to stress it: you get different error messages in OpenBUGS run natively than you do in OpenBUGS called through R. Those error messages are so different that you will get a completely different idea of what is wrong with your program.

Anyway, I fixed the index but then I ran into problems after I tried to load my initial values. Nothing seemed to work, and the errors were really cryptic. “Invalid initial value” is not very useful. But further digging on the internet showed me that OpenBUGS and WinBUGS have different approaches to initial values, and winBUGS is not as strict about the values that it accepts. Hmmm … so I installed winBUGS, and reran the model… and it worked! OpenBUGS apparently has some kind of condition on certain variables that they must sum to 0, while winBUGS doesn’t check that condition. A free tip for beginners: setting your initial values so they sum to 0 doesn’t help, but running the same model, unchanged, in winBUGS, works.

So either OpenBUGS is too strict, or winBUGS lets through a whole bunch of dodgy stuff. I am inclined to believe the former, because initial values shouldn’t be a major obstacle to a good model, but as others[3] have observed, BUGS is programmed in a completely opaque system so no one knows what it is doing.

So, multiple misleading errors, and a complex weirdness about calling external software through R, and I have a functioning model. Today I expanded that model back to the original order of magnitude of small areas, and it also worked, though there was an interesting weirdness here. When I tried to compile the model it took about three hours, and produced a Trap. But the weird thing is the Trap contained no warnings about BUGS at all, they were all warnings about windows (something called Windows.AddInteger or similar), and after I killed the Trap my model updated fine. So I think the compile problems I previously experienced may have had something to do with memory problems in Windows (I had no problems with badly designed adjacency matrices in the larger model), but OpenBUGS just doesn’t tell you what’s going on, so you have no idea …

I should also add, for intrepid readers who have got this far, that this dude provides an excellent catalogue of OpenBUGS errors with his plain English explanations of what they actually meant. He’s like this mystical interpreter of the I Ching for Bayesian spatial regressives. Also I want to add that I think the CAR spatial correlation model is super dodgy. I found this article (pdf) by Melanie Wall from the Journal of Statistical Planning and Inference (what a read!) that shows that the way we construct the spatial adjacency matrix is the primary determinant of the correlation structure, and that the correlation structure determined by this adjacency matrix is nothing like what we think we are getting. Today on my whiteboard and with the help of R I imagined a simple industrial process where each stage in the process is correlated with the one before and after it, and I showed very easily based on Wall’s work that the adjacency matrix required to describe this process is completely different to the one that you would naively set up under the framework described for CAR modeling. So I think most of the “spatial correlation” structures described using CAR models have no relationship to what the programmer thinks they’re entering into the model. But I have no proof of this, so I guess like everyone else I’ll just press on, using the adjacency matrix I think works …

So there you have it. Next time you see an opinion formed on the basis of a spatial regression model built in BUGS, remember the problems I had getting to the output, and ask yourself – do you trust that model? Really?

fn1: Well, I could copy winBUGS into the program files folder but I couldn’t patch it or install the immortality key, which, wtf? When I bought Time Series Modelling and Forecasting by Brockwell and Davis, ITSM came as a free disk with the book. When I buy the BUGS book I get to install software that comes with a big message telling me to get stuffed, and years later they finally provide a key that enables you to use it for free …

fn2: If you aren’t aware of how this works, basically when you call OpenBUGS in R, providing data from inside R, R first dumps the data into text files in the directory of your choosing, then OpenBUGS opens those files. So if you aren’t comfortable preparing data for BUGS yourself, use the list and structure commands in R, then kill OpenBUGS and go to OpenBUGS directly … the text files will remain in the directory you chose.

fn3: Barry Rowlingson does a pretty good job of showing how interesting and useful spatial analysis can be: see e.g. his post on mapping the Zaatari refugee camp in Syria.

Even sunlight is rationed down here ...

Even sunlight is rationed down here …

This is a kingdom I created entirely randomly for a one-off of Make You Kingdom, to be played in English this weekend.

Kingdom name: The socialist republic of disasters [yes I really rolled this randomly]

Map Position: E3

Kingdom level: 2

Lifestyle level: 1

Culture level: 1

Order level: 2

Military level: 1

Total population: 70

Consisting of …

  • 63 citizens
  • 4 Court members (PCs)
  • 3 Hurryfoxes

People’s voice (Maximum): 10

Facilities:

  • Royal Palace
  • Ranch
  • Staple [steel]

Background details

The Socialist Republic of Disasters is located in map square E3 of a random part of the labyrinth, and is ruled by Comintern President Mario, who is untroubled by the Ephemeral God. The kingdom is remarkably stable and fortunate given its circumstances: though it only covers three squares of a standard 9×9 labyrinth map, its population is surprisingly large and it is allied with a distant kingdom, the United Dungeon Empire, that supplies it with steel. It is also home to three Hurryfoxes (Gonkitsune). Due to a loan that the wise Comintern President Mario took from the Subterranean One, the Republic is also in debt, owing a mighty 15 MG.

The Hurryfoxes live in the kingdom because it has a special property of being able to coexist with monsters: under the wise and benevolent rule of the Comintern President, a ranch was established, and people from all over the kingdom are happy to receive monsters and live alongside them, provided they offer some of their souls and material for use in the ranch, where any new monsters who join the kingdom can be cloned to produce more of their kind. The ranch is an ancient heritage, from a time before the enlightened rule of the Comintern President, when the kingdom was under a sorcerer’s curse that caused all its citizens to be undead. This time is long past, but out of respect for history the Comintern President has kindly allowed the cultural memory of this special lineage to linger, enabling all adventurers to learn any undead skill when they gain a level or a new skill.

Since the demise of the sorcerer and the end of his curse the nation has lived a long and peaceful life under the principled, firm but loving guidance of the comintern; as a result it has a larger population than many similarly-sized kingdoms (+13 population) and has a strong sense of discipline and order (+1 order level).

The ranch: From each monster according to his means

The ranch: From each monster according to his means

How it looks

This is ultimately up to the players, but given the name, the sense of order, and the sinister-sounding nation they are allied to, I can’t help feeling it has a slightly tatty-grandiose soviet-era feeling to it. I imagine it is not a particularly large kingdom, and is composed primarily of wide, spacious, well-lit tunnels similar to the tunnels in some of the Moscow metro, with the same sense of grandeur. These tunnels form a complex network connecting the living spaces, markets and royal palace (the Comintern Palace, I guess!) together in a soviet-styled warren. I even imagine there is an actual train, a rickety old coal-burner that connects the Socialist Republic of Disasters (SRD) with the distant Unified Dungeon Empire. Perhaps it takes a month to chug along on complex paths through the labyrinthine fallen world, eventually returning two months after it set out with a cargo of iron scrap – rubbish, basically – from the Unified Dungeon Empire.

I imagine the ranch as a somewhat sinister place, not a happy sunlit farm at all. The rules state that if you have a ranch, when you manage to bring a monster back to your kingdom as a citizen you can make a check to produce another one of them in the ranch. Given the speed this happens at, I see it as some kind of sinister magical cloning process, not a game of happy-monster-families. Sometimes, obviously, it goes wrong (which would be why the SRD has 3 hurryfoxes, not 2 or 4). I imagine this is some relic of the time before, and though the citizens know how to operate it, they don’t know how it works.

From each according to their means, to each according to their needs

From each according to their means, to each according to their needs

The court

The court consists of four PCs, described briefly here.

Comintern President Mario, who is untroubled by the Ephemeral God

The President’s Job is Daedalist (迷宮職人, see the second from right in the illustration above), his/her sex is undecided, and his/her primary attributes are quest and warfare. He owes 15MG to the Subterranean one, and it is his mission to escape from the Subterranean One’s debt. Mario likes foppery and storytellers, and hates liars and apologizing.

Cocoa “Wise ears” Scarlet

A Knight with the job of Hunter, who came to SRD from the distant kingdom of Autonomic Dark Gotanda [square F1] as an apprentice and has the mission of becoming Mario’s lover. Cocoa’s primary attribute is warfare, and Cocoa has a horse, armour, weapons and a living drill (a stick with a mole on the end). Cocoa likes the countryside and smart people, and hates Citizens and elderly people.

Hairan Blademagnet

An Oracle with the job of thief, who came down to SRD from heaven in an elevator when he was a child, and whose nemesis is a deep sea monster called the Forneus, that it is his mission to thwart. Hairan’s primary attribute is charisma, followed by quest. He is a belly-god, so can consume food and drink without running out of supplies, so he’ll probably end up obese by the end of the first adventure. He likes receiving weapons, and the labyrinth itself; he hates beards and ogrekin.

Cookie the Involuntarily Anointed

Cookie is a ninja, who came to the SRD as a spy for the neo-superhero federation [map square B6], and has the mission of becoming Cocoa’s rival. Cookie’s job is Doctor, so Cookie has the skills of Monsterology and Anti-magic Formula. Cookie is powerful in quest and wit. Unfortunately for a resident of the SRD, Cookie hates narrow places and hospitals; but she likes stars and princes; Cookie herself carries a Blade of Star, a bomb and a trap collection. Really, she’s a perfect spy!

The adventure

This week’s adventure will start when an old associate of the kingdom, a kind of fence and all-round sleazy guy, arrives to tell Comintern President Mario that a debt collector [a type of monster] has turned up in a nearby kingdom, possibly looking to call in the debt that Mario owes to the Subterranean One. The characters will then set off to find this debt collector and … er … deal with him. Their oily friend knows the way to the neighbouring kingdom, though he doesn’t know the kingdom layout or the nature of the creatures that live there. Is everything as it seems, or is their oily little friend causing trouble …?

行ってきます!

This is my first attempt at mapping the Steamlands, the kingdom in which I am now running WFRP 3 adventures. The map was created in Hexographer (the free version) because I’m a terrible artist, designer or drawer. The geography of this kingdom is based loosely on Kyushu, Southern Japan, taking the names and some of the features of parts of Japan for inspiration. I’ve also chucked in a bit of Germany for good measure (the World Forest on the west coast, and the river system lined with towers, is essentially a geographical feature copied from the Black Forest and the Rhine).

At the moment I have no ideas for this world except the names of towns (translated from the Japanese) and a couple of general features. Although the geography is modeled loosely on Kyushu and the city names a translation of Kyushu names, the vegetation and weather is roughly similar to south east Australia – so the World Forest, for example, is a vast wilderness of eucalyptus forest, and the flat areas can be imagined to be rolling pasture dotted with cows. I haven’t decided if it has marsupials (probably not) but it will have a bird that resembles a kookaburra, and the forest will preserve Australian features – quiet, pleasantly fragrant, dry and kind of spooky. Some particular features of the landscape (and the names they’re translated from) are given below.

  • The Spear Capes (Nagasaki): a land of inlets and bays, rich swamps and wild rivers, I imagine this is dotted with independent cities of pirates and traders, and is quite primitive and barbaric in many ways. I imagine the swamps will be mangroves, and crocodiles will be common. I also imagine that the inland areas will have plantain and sugar farms, and possibly large plantations owned by strongmen from the other kingdoms (particularly from Twinluck)
  • Twinluck (Fukuoka): Twinluck is the largest city in the Steamlands, and is connected by a railway line to the city of Store. I imagine Twinluck as a chaotic melting pot, with an ancient history and lots of steampunk technology. I also think that the current ruler has styled himself “The Emperor of Infinity” and is attempting to build a great empire, the Empire of the Manifold Path, which is slowly expanding East and Southwest (through the land that in real life is called Saga but in this map is simply farmland and forest that is open to exploitation by the most aggressive settlers). I also imagine that the people of the Manifold Empire (as it is often referred to) are of a different race to those in the Spear Capes, and there is much conflict on the edges of the Empire
  • Store (Kitakyushu/Kokura): I know nothing about Store except that it is at the other end of the railway line from Twinluck, and it constitutes the Easternmost boundary of the Manifold Empire. It is so-called because the coastline is riddled with old caves that connect into a deep dungeon, in which can be found lost artifacts and ancient technology – as well as ancient evils
  • Heavenbalm (Usa): Heavenbalm is the temple-fortress at the centre of a religious kingdom, about which I know nothing. I guess there is human sacrifice and witch burning, and possibly magic is forbidden.
  • Steamline Spa (Yufuin): This town is the centre of a network of hot springs and spas, scattered through the mountains in this area. This is the area where the campaign began
  • Separation City (Beppu): The adventurers are heading here as I make the map. I’m not sure why it’s called “Separation City” or what’s there, but I think it will be a smallish town with a large entertainment industry, and a large port for Eastward travel to the Four Kingdoms and the Summerlands (two other continents not shown on this map)
  • Greathalf (Oita): A large industrial and agricultural city, the last significant town before one heads down the wild east coast
  • The Beastlands (Kumamoto): Kumamoto means “origin of bears,” so for this area I figured it must be full of beastmen. These beastmen regularly spread to the east coast or northeast towards the civilized lands of Greathalf, and the line of towers has something to do with them. The southernmost island of the Beastlands is the Isle of Pan, and it may be the home of some kind of god of the beastmen, though no one has returned to tell the tale of its contents. This island corresponds with the province of Kagoshima (Island of Fauns), and the islands which inspired the mythical forest of Princess Mononoke, which seems apt
  • The World Forest: the home of the elves, especially away from the coast, and continually being contested between elves, beastmen and humans

Besides these rough outlines, I have no other ideas for the world. I know there is another kingdom on the east coast, not yet described, which corresponds with Miyazaki, but I can’t think of a good translation of Miyazaki (“Palace” plus “Promontory” or “Peninsular”). Maybe the “Bay of Palaces”? Sounds rich and powerful! Or maybe it once was, and now is crumbling under the beastman threat.

Because there is a dwarf in the party I need to find a place to fit dwarves in all of this – they might, however, come from over the sea, in the Four Kingdoms.

The PCs are heading to Separation City to get a lawyer to sign a deed transferring property to them, so that they can take ownership of an onsen in the mountains. After that they have adventure options in Twinluck, and they can return and explore the land around their property, or they can head elsewhere to explore the Steamlands. At this stage it’s all pretty open, basically a sandbox to enable me to see how far I can push the WFRP 3 rules before I get bored of them and/or they break. Shall I have a beastman empire? What is buried beneath Store? And does the Emperor of Infinity know something we don’t? Only time will tell…

The Guardian has a couple of pictures today of strange maps, which are pretty cool. My favourite is the map of the US in terms of distance from the nearest McDonalds, but in role-playing terms the railway one is pretty good. If the world were composed of city-states linked by potentially very wild trips on a wide range of steampunk-styled railway lines, I think you’d have a very weird and wild place for adventuring. Neil Gaiman’s Neverwhere meets Erewhon, or something.


Well, that wasn’t the experience I had in mind when I came to Japan. I was at work when this little nugget of chaos hit, and the trains immediately stopped so I spent all of yesterday evening (6.5 hours!) walking home, from Hongo to Kichijoji.  The route is in the map above, it’s between 16 and 18 kms (10.8 to 11.1 miles) and takes 3 hours 40 minutes without traffic lights. My experience of the biggest earthquake to hit Japan in 1200 years was … a long walk. Anyway, this post will describe events from the moment it struck to my arrival home, with hopefully some observations on Japanese life during the ramble. I’ve set it out in sections for your viewing pleasure and I’m approaching it in a light-hearted manner but let’s not forget that while I’m writing this a handful of cities have been completely destroyed and over a thousand people are still missing…

The Quake

I share an office with 4 women, two of whom were in yesterday and one of whom is rather sensitive where earthquakes are concerned (we had several in the last 2 weeks). We’ve already had enough minor rumbles for me to know that she’s got a very good sense for these things, and so we were all aware the moment the slightest tremor started. We sat at our desks while it got worse and my colleague became increasingly agitated, but the building itself wasn’t moving so much, really. Whenever an earthquake strikes I look out my window and thank my luck, because our building is new, made of very solid stuff and surrounded by a kind of cage of concrete buttresses, which are themselves cross-hatched with huge diagonally-placed steel girders. They weren’t even moving, and things were rattling inside and it was a bit … mobile … but nothing bad. It was certainly not like the video footage of Fukushima. But things kept getting worse so after maybe another 5 or 10 seconds the women in my office broke for the door, and we saw other staff rushing by outside. Since I don’t know much about earthquake safety, and figured that Tokyo people know best, I followed. This is sufficient both to show that a lifetime’s exposure to safety information isn’t necessarily particularly effective (as you’ll see, we should have stayed!) and to illustrate how long and devastating this earthquake was. We are on the 5th floor of the Medicine faculty building, and before we left we grabbed our coats and bags (!) – I forgot my bag at the door and went back for it. We then had to walk down the corridor and down 5 flights of stairs, along the corridor and through the (still-functioning) automatic doors, and out under a massive concrete verandah(!) to the path outside. When we arrived, the ground was still rocking, and the earthquake took a few seconds more to subside. I’d say it was more than a minute long (we had to descend those stairs with some care) and it was at its worst when we were halfway down the stairs.

So why was going outside so unwise? First of all because the stairs were not the most negotiable of rocking, twisting obstacle courses, and we could have fallen. But mostly because when we got outside we found ourselves standing in a narrow valley between two 8 storey buildings, with nowhere to run if one collapsed, right next to a truck full of gas bottles. Imagine the timing, if a single bit of concrete set off something in those gas bottles, and wiped out the cream of Tokyo University’s medical faculty so thoroughly that there wouldn’t be enough flesh left to clone them[1].

The Wait

We then engaged in every post-apocalyptic drama’s most tedious part, the wait. Everyone stood around in the cold, trying to get a reception on their phone, while a loud speaker gave us increasingly disturbing news – first it was a magnitude 5, then a magnitude 6, then we discover the whole coast is affected, etc. The ground kept swaying occasionally, and we were all quite scared, so that sometimes you couldn’t tell if it was the ground or your own fevered imagination. At which point you could just check that truck, to see how much the gas bottles were wobbling… until the delivery chap came out wheeling a gas bottle, and tightened the whole lot up. People were wandering around, trying to call loved ones, looking around at the clear cold day and talking about how damnably scary a big earthquake is, and I was looking at that cage of girders and buttresses around our building and thinking, “bravo for Japanese engineering.” Eventually, after about 20 minutes, the loudspeaker informed us that we should all move to an open area and wait for further instructions. Sometime in this period the three women from reception emerged from the building, having taken the much more sensible approach of hiding under their desks while the world wobbled[2].

So off we all went, me and the women from my office at quite a pace, because that gas truck was a bit disturbing. Halfway there another big tremor hit, so you could see all the topiary of the medicine faculty grounds shaking and grooving – had the topiary been dinosaurs and not mere tree-shaping, the effect would have been quite excellent. My colleagues and I decided to move more rapidly at this point, because I have already decided that I intend to die at the wheel of a ferrari during my mid-life crisis, not in a hail of broken glass from the university admin building. So we arrived expeditiously at the centre of the campus, and more standing around ensued. One of the reception staff managed to produce another ingenious Japanese invention – a combined torch and radio – and we listened to increasingly alarming news from up north – a 6m tsunami forecast for Fukushima, all underground trains halted, risk of aftershocks. Which kept coming and coming, so that every few minutes the ground kept shaking.

After another little while two of our colleagues were dispatched to check the building, and the all clear was given. We returned to our offices but no-one was in the mood for work. One of my colleagues walked around distributing water bottles “just in case” and we all spent the afternoon checking the internet. At about 5pm it was decided to leave early, because none of the trains were working, so we were going to have to walk home. With typical Japanese quiet calm, teams were organized, to ensure that the foreign staff who speaks no Japanese could get home, and the completely new guy who doesn’t know Tokyo (i.e. me). One staff member had ordered sushi for a party that was now cancelled, so we ate some sushi and off we went, leaving behind four staff members who live so far away that their only choice was to stay the night in the office.

Disaster Japanese

I should mention at this point that my Japanese is neither good enough to understand Japanese spoken on loud-speakers, nor sufficiently stocked with disaster words. Also, although I can read some Japanese I don’t read nearly enough to be able to navigate information sites quickly, nor can I understand much of radio broadcasts, so I was very much dependent on my colleagues’ support when it came to working out what was happening. I also don’t know anything about Tokyo so had no idea how to get home. My heart goes out to all those people in Japan who don’t speak or read Japanese and found themselves stranded and far from home in such a situation, because it can be bewildering even if most day-to-day conversation is manageable. By the end of the day my colleague who doesn’t speak any Japanese (a British visiting professor) was beginning to get quite frustrated, because even though people translate the essential stuff, when people are scared and confused they naturally exchange a lot of information very rapidly in their mother tongue. Certainly in the shock of the event my Japanese went a little backward, and my sentence construction fragmented. Plus, who prepares the necessary vocabulary for a situation like this in their second language? Who thinks to themselves “I really should learn all the apocalypse words in my second language”? Well, actually, I have learnt a pretty weird vocabulary in my time here, but I’m a nerd. And my weird vocabulary might include monsters, but it doesn’t include words like “evacuation” and “elevated ground”. So, handling a disaster in a second language… not the best way to deal with the situation[3].

The Walk

So we set off, me and two colleagues, for a walk we predicted to take about 3.5 hours. One colleague was separating at Shinjuku, and one at Shin Nagano. At that point I would be on my own, and I had rather sensibly elected not to print a map. Of course, this is Japan so you can guarantee that someone will help you, but I think it should be clear here that I’m not part of that small elite of people who are going to survive the apocalypse. Though I did have good walking shoes (I recommend Whoop-de-doo shoe company for all your apocalyptic footwear needs). We set off at 5:30, and as soon as we emerged from the campus we entered a river of people. As we got closer to Shinjuku station this river widened, like the famous graphic of Napoleon’s advance on Moscow; everyone was heading the same way, towards the huge junctions at Shinjuku and Ikebukuro. The same river was flowing on both sides of the road, and in between us was a river of traffic, all moving very slowly and forced to delay at every crossing as thousands of people crossed the roads. The crowd was cooperative and quiet, as crowds always are in Japan, not pushing or getting in each other’s way even at the stupidly-designed crossing near the Shinjuku rail bridge, where a crowd 10 abreast coming one way hits the same crowd going the other way, at a corner where the pavement is barricaded from the road and narrows to two people in width. Even bicycles negotiated this chokepoint without yells or complaints. People just accepted that we were in this situation, and moved through and past each other with that quiet Japanese manner that makes everything here flow so smoothly.

On the way we passed many things, but one thing we didn’t see was any evidence of earthquake damage, and everyone was chatting and joking as if this were a funny little outing, or a charity walk. At about 8pm everyone in possession of a docomo phone finally got their earthquake warning call (for the 2:40pm earthquake), and there was more joking about this. We didn’t have proper reception so noone could watch TV or receive information, so mostly we didn’t know about the catastrophe unfolding further North. I passed a bicycle shop where a queue of maybe 20 or 30 people were waiting patiently to buy bicycles, the staff frantically trying to assemble and register the bikes as quickly as possible; every macdonalds had huge queues outside as people gathered for food, and all the convenience stores where thronging with people, many queueing for the toilets. Some restaurants had put out signs saying “We have toilets, please use them,” which was a nice touch. Some shops had to close due to damaged stock (particularly the alcohol shops) but most restaurants were open and doing a roaring trade. I saw a cute scene of a man entering a rental car shop to be greeted by a staff member bowing with good-humoured and exaggerated obeisance, to make clear that this time, at least, the lack of available cars was entirely beyond his control. I passed a group of girls standing around their friend, whose feet just weren’t up to the task in her work shoes – I think this must have been a problem for many people. Groups of people were camping out in the rooms where the cash corners are located, some with their laptops out. At Shinjuku I saw the fascinating contrast of twenty or thirty people crouched under a shop entrance, with nowhere to go for the night; in amongst them was a homeless man with his possessions and, of course, his cardboard house, suddenly a prince among paupers as the usual order of Japanese life was turned on its head by nothing more than the collapse of the transport system. But for all of this sea of humanity with its congestions and minor tribulations and difficulties, I didn’t see a single person get in a fight. And no one smoked as they walked. They stopped at the smoking spots before continuing, preserving even the smallest of Japanese manners at this moment of confusion.

All these people of all walks of life converged on Shinjuku, the hosts swaggering through the crowd past salarymen and schoolgirls and office ladies in little elegant gaggles, every tenth person wearing a mask. The traffic was still trapped in gridlock, inching forward, and we were moving much faster. Under the Shinjuku bridge and onward up Blue Plum Road, already 3 hours into our journey and me only halfway home. At Shin Nagano when my colleague left me I bought some hand-warmers (kairo) and stuffed them in my pockets, and kept walking until I stumbled on a cute little cafe, Doggie Boogie Cafe, where I took a break and had what I think is the best Thai food I’ve eaten in a long time. Here I rested for an hour before continuing, and now I walked alongside a pair of office workers who had set off an hour before me from Tokyo station, and had just finished their second break (this one, in the cafe with me, was for booze). They were still cheerful despite 4 hours of walking and 3 more to come, and they and the restaurant owner helpfully directed me to a shortcut to Kichijoji, down Itsukaichi Road. Here I found a bus stop for a bus going to Kichijoji station, but it was 10 pm and the last bus left at 9:20pm. Too bad! I had my ipod on now, and kept walking. At 10:50pm I passed that last bus, stuck in traffic and jammed with people. Further on I found the 9pm bus, stopped at a bus stop, and finally got to see something I have always heard of but never seen – two bus company employees actually pushing passengers into the bus to fit more on. One often sees this on TV but I’ve never seen it in real life, so that’s a Tokyo experience I can tick off… and I’m glad I didn’t have the experience of being pushed onto that bus, because I beat it to Kichijoji station when I arrived at midnight.

So, I finally got home at about quarter past midnight, my only information about the disaster unfolding to the north coming from a single mail from my partner, that arrived during a patchy period of uncongested transmission at about 9pm, telling me it was bad. I have a friend in Iwaki City, which has been partially destroyed and may have to be evacuated due to the nuclear plants; I spent the evening occasionally trying to call him but the reception was impossible. Occasionally mails would reach me from various people, asking if I was okay or telling me they were okay, but this was intermittent. It was just me, alone in the cold neon night amongst a river of a million people just like me. And when I got to Kichijoji at midnight that river was still flowing but I, thankfully, was at the end of my earthquake odyssey and able to find out the true magnitude of the horror unfolding to the north. This morning Tokyo feels just like it did yesterday, as if nothing happened, except for the regular little aftershocks. I think it’s safe to say that this is a very good country to experience a disaster, even if (or maybe especially if) you don’t speak the language. Nonetheless, I’d have happily traded this experience – especially that minute in my office, wondering if I’m about to become a statistic (東京外国人1人死亡)- for a quiet evening with a glass of wine and a book.

fn1: We’re across from the experimental research facility, where they probably have that technology.

fn2: As a general approach to problem-solving, this is probably excellent

fn3: Though I pride myself on understanding all of “A tsunami warning is being broadcast for the Fukushima Prefecture, and all people living in coastal areas should immediately evacuate.”

NASA today reports the initial results of the Kepler mission, showing that there are potentially millions of habitable planets around stars in the vicinity of Earth. The resulting menagerie of planetary candidates reads like a Traveller or Spacemaster planet generation table – it includes planets bigger than Jupiter, one planet with the density of sytrofoam, and some planets whose moons may hold liquid water. There’s also surely a hat tip to Star Trek in the report:

It’s exploring a new part of phase space, a new part of the universe that could not be explored without this kind of precision, so it’s producing absolutely beautiful data

(my emphasis). Oh, those NASA nerds, how we love them[1].

Something about science fiction that I find continually fascinating is how much of it ceases to be fictitious with the passage of time.

(I think for this post I need to do a hat tip to Larvatus Prodeo, the Australian left-wing blog).

fn1: Or is this a real term? If so it’s surely been named after a Star Trek episode anyway.

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