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Introduction to the Demic Atlas

Submitted by on August 18, 2011 – 11:00 pm 9 Comments |  
Demic Regions of the World

Demic Regions

The Demic Atlas rests on the proposition that socio-economic comparisons work best when based on comparable units, framed at approximately the same scale of analysis. The obscure term demic—“pertaining to populations of people”—highlights the demographic egalitarianism central to the project. Ideally, regions of equal population should be compared against each other; otherwise, the individual inhabitants of some parts of the world are weighed more heavily than those of other areas. Conventional comparisons based on sovereign states necessarily violate this principle, effectively giving the residents of small countries far more attention than their counterparts in big, densely populated states. The premise of the Demic Atlas is that deploying roughly comparable categories will yield a more illuminating picture of global development.

The first step in this project has been to create an alternative base-map: one in which all units have similar numbers of inhabitants. After much experimentation, we have settled on 67 regions of roughly 100 million persons each. This is admittedly a rough grid; only eleven sovereign states have more than 100 million inhabitants. A smaller target figure of fifty or even twenty-five million might have been desirable, but such an option was precluded by data limitations (as explored below) as well as design difficulties. (Across East and South Asia, doubling the number of units would have cluttered the map and made it difficult to read without magnification). As it stands, we are persuaded that the 100-million norm, crude though it may be, is an improvement over customary global maps. A signal advantage is the ability to highlight internal diversity within the world’s demographic giants (India and China), and contrast these with zonal patterns in the Americas or Africa in a single global snapshot.

The data difficulties that stand in the way of creating smaller demic regions stem from the need to rely on conventional categories even while trying to transcend them. Literally as well as figuratively, sovereign states are the units that count; these are the bodies that conduct censuses and gather most data. It is no coincidence that the term “statistics” derives from the Latin for “of the state.” When international agencies such as the World Bank and the International Monetary Fund (IMF) tabulate country data, they steadfastly ignore sub-national divisions, no matter how large or important internal regions may be. Nor is this always a bad strategy. For while most countries collect information on their own subdivisions, they do so in diverse ways. For instance, GDP figures are available for the states of India, the provinces of China, the prefectures of Japan, and so on, but such information is usually gathered in different years by different countries, and is seldom fully comparable. For a number of the poorest countries, usable socio-economic information at the provincial level is simply unavailable.

Demic regions and their constituent units

Constituent Units of the Demic Regions

A non-state-based appraisal of global socio-economic development must therefore use states and their major subdivisions as the building blocks of an alternative scheme. Middling, small, and tiny countries have to be grouped together to form units of a more appropriate size. By the same token, large countries need to be broken down into their provinces or prefectures, which can then be selectively re-aggregated to form units approaching the target population. These two methods alone, however, do not always yield regions of approximately 100 million inhabitants. Consider the situation in North America. The United States, with a little more than 300 million people, could be easily split into three demic regions in the target range. But Canada, with 34 million inhabitants, is far too small to constitute such a region on its own, yet it has no neighbors with which it can be joined other than the United States. Unless one were to create an ocean-spanning region linking Canada to northwestern Europe, Canada has to be combined—whether as a whole or in parts—with some cluster of U.S. states. Similar challenges arise in other parts of the world as well, where the shape and distribution of landmasses and archipelagos is such that the only way to create units in the target range is by splitting and merging countries in highly unorthodox ways. Such an exercise demands tedious data manipulations, but we are convinced that it proves useful for depicting places in which developmental gradients are deeply out of sync with the geopolitical framework. It also helps to unsettle the notion that countries form natural units of observation, one of the overriding goals of the larger project.

Such maneuvers, however, still prove inadequate to the task of generating units of an appropriate scale across the world. On the one hand, several Indian states and one Chinese province in themselves exceed the 100-million guideline. Most are close enough to the target number that they could be mapped as demic regions in their own right. But Uttar Pradesh—the world’s largest “statoid” (as first-order subdivisions of sovereign states are sometimes called)—has nearly 200 million inhabitants. By the logic of our project, a unit of this size needs to be divided. Likewise, to create a grid of geographically contiguous blocks of roughly 100 million inhabitants across India, two other Indian states were split and re-aggregated at the district level. (In our model, Western Maharashtra has been paired with Gujarat, eastern Maharashtra joined with northwestern Andhra Pradesh [Telangana], and the rest of Andhra Pradesh connected with Karnataka.) This time-consuming procedure, however, proved in the end to be of marginal statistical utility, as comparable socio-economic data for Indian districts was not obtained.

Other issues, too, complicated the drive to delineate areas of 100 million inhabitants. One was the design desideratum for our regions to be spatially compact. Although it would have been easier in some areas of the world to reach the target population by devising irregularly shaped regions, such a procedure would have resulted in a fair amount of gerrymandering. Even in the best of circumstances, the underlying geopolitical substrate frustrates the attempt to craft truly compact regions. Many countries have aberrant shapes; the odd outline of Cameroon, for example, contributes to an oddly shaped Region 14 in the demic base-map. Exclaves can be even more problematic, since outliers that fragment the territorial cohesion of individual countries can do the same for the regions to which those countries are assigned. Ideally, exclaves are placed within the spatially appropriate demic regions; Russia’s Baltic exclave of Kaliningrad, for example, is classified in Region 61, rather than in western-Russia-focused Region 59. By the same token, Angola’s exclave of Cabinda should have been placed in Region 14, rather than with the rest of Angola in Region 10. Doing so, however, would have required breaking Angola down into its constituent provinces, a procedure too time-consuming for the current iteration of the Demic Atlas.

A third divisional principle was that, in addition to being spatially compact, demic regions should be characterized by roughly similar levels of socio-economic development. Average figures for a region split into between a wealthy, highly educated area and an impoverished, poorly educated area would tell us little about the region as a whole. Clumping countries and their subdivisions into reasonably coherent developmental regions is possible, as levels of socio-economic development across the world tend to be highly geographically structured. But perfect aggregation of this sort is again impossible. In some parts of the world, areas of extremely high and extremely low developmental standing are spatially interspersed. The Caribbean is particularly diverse on this score, containing both very wealthy areas (Cayman Islands) and very poor ones (Haiti). Since the prosperous parts of the Caribbean are demographically overshadowed by the region’s poorer zones, the region as a whole shows relatively low levels of development.

Archipelagic environments like the Caribbean pose yet another challenge to the regionalization scheme. The guideline of spatially compactness would seemingly rule out maritime-centered regions linking the opposing shores of intervening water-bodies. But the world’s only islands populous enough to stand on their own are Indonesia’s Java and Japan’s Honshu; all others must be grouped with other islands or, more often, with nearby peninsulas. As a result, several sea-focused regions do appear on the map, such as Region 65. The criterion of socio-economic similarity generates further compromises along these lines, as certain islands are in developmental terms best grouped not with their closest mainland neighbors but rather with more distant islands and shores. Region 43, composed of Taiwan, South Korea, and Japan’s Kyushu, Shikoku, and Ryukyu Archipelago, is particularly problematic in this manner. By the principle of spatial compactness, South Korea would have been much better grouped with North Korea and a segment of northeastern China, while Taiwan would have fit better with Fujian in mainland China. Such a maneuver, hoverer, was rejected, as it would have required uniting highly divergent economies. Hong Kong and Macao, however, were grouped with the Chinese province of Guangdong, even though socio-economic considerations would have called for them to be put in the same category as Taiwan and South Korea. In this case, the spatial irregularity that would have resulted was deemed excessive.

One part of the world that stubbornly resisted our regionalization guidelines to the last was Australia and environs. By the principle of compactness, Australia can only be joined to eastern Indonesia; any other scheme would require spanning vast stretches of sea-space. But the Austral lands resist regionalization with Java and the Lesser Sunda Islands, as the developmental gap between them is too large. In the end, since Australia and New Zealand lack sizable neighbors with similar socio-economic conditions, they have been granted the status of a region in their own right, along with most of the rest of Oceania. But considering its meager population, Region 66 is best considered a quarter-region. As is often the case, Australia is revealed to be a most distinctive land.

The demic base-map is thus a product of many agonizing trade-offs, in which the criteria of population, shape, and socio-economic standing had to be constantly weighed against each other. The map consequently went through a number of changes over the course of its construction. Region 10, for example, was originally much larger, including Zimbabwe and Mozambique, as the population of the six countries currently constituting the region was judged inadequate. But linking Zimbabwe and Mozambique, two of the world’s least developed countries, with sub-Saharan Africa’s otherwise most highly developed region seemed unfair. In the end, an additional region was carved out of eastern Africa, resulting in the out-of-order numbering scheme currently found on the map. In Brazil, the state of Mato Grosso was originally slotted on socio-economic grounds with Region 2, while Bahia was placed in Region 3 on the same grounds; the resulting Region 3, however, was deemed too irregular, while the population of Region 2 was considered too small.

The resulting division of the world is thus not merely idiosyncratic, but is replete with vexing compromises. Criticisms and suggestions are welcome; the map remains open to change. The GIS files by which it was constructed will eventually be posted online, allowing others to build their own alternatives to the state-based global framework. If our future plans come to fruition, other sizable countries will also be broken down into their first-order subdivisions, which would allow more complex regionalization schemes.

Finally, let us stress that the demic regions outlined here are strictly intended as a framework for socio-economic comparison. Having no cultural, political, or historical significance, they are completely useless for (and could cause grave mischief in) many geographical questions. It is our hope that the construction of culturally and historically based alternatives to the standard geopolitical framework will also someday be advanced, but that is not the goal of the Demic Atlas.

The next few posts will consider the sixty-seven (or sixty-six and a quarter) demic regions in more detail. Next week, socio-economic maps using the scheme will begin to appear on GeoCurrents.

 

 

 


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  • http://www.facebook.com/people/James-T-Wilson/682045086 James T. Wilson

    I’m intrigued by the map, but I struggle to think of what comparing these regions would demonstrate.  It is probably because my mind is too much tied to historical, cultural, and legal structures.  I look forward to reading how you use these divisions.

  • http://blog.zolnai.ca Andrew Zolnai

    I followed you prefaces and introduction with great interest, but I’m afraid I fail to see the purpose of such an arbitrary subdivision. I suppose you look at global issues, and I agree that current borders a fallacies, usually bent of dividing ethnicities to better conquer them: my biggest post-colonial beef is why not restore pre-colonial borders along ethnic lines?So why not try an restore those borders were it only on paper (or disk), given your knowledge and resources in this topic? Can you not normalise areas by any unit of measure? I’m sure you thought of all of this, but I still don’t get it! I did a history project in East Anglia from 1067 to today using parishes as the (more-or-less) stable geo-entity, against which I was able to normalise original historian Darby’s data for the region over almost a millennium. 

  • Luis Aldamiz

    You need to work with more real units: 100 million may make sense in India or China but in most places the real meaningful unit is somewhere between 300,000 (Iceland) and 10 million people (Hungary). I’d suggest working with units approximating 5 million.

    The tradeoffs are anyhow crazy: you split Germany (which is almost the size of your basic unit) instead of aggregating something to it (like Austria maybe?) you aggregated Greece with Turkey (they’ll both hate you for that) or South Italy with Spain (no relation whatsoever). I could address other parts of the World but sincerely the one I like the least is Europe: it’d be much more logical to divide based on big actual states: UK with Ireland and the Low Countries make 100 million, so does Germany with Austria and maybe Denmark or Czechia. France + Spain: 110 million (you have to add Portugal, no choice: 120 M), Italy is short, add Greece and former Yugoslavia (and little Albania, Malta and Cyprus): you have your 100 M already. Turkey needs 10 M more after removing Greece?, get then from the Levant or Caucasus…

    Similarly I don’t like splitting Nigeria (the only African state close to your level of 100 M) or including Angola instead o Mozambique in the Southern African region… No point in mixing Yemen with Ethiopia…

    It’s a very arbitrary division. Take for example Japan: either you divide it or you do not but why to gather southern Japan with South Korea and Taiwan and still keep Taiwan separated from China or both Koreas divided? What’s the point?

  • Jim Belshaw

    Like your other commenters, I struggle with your structures. In Australia, we have a remoteness indicator called ARIA. Originally intended simply as a measure of geographic remoteness, its categories (very remote, remote, outer regional, inner regional and major city) came to be used as major analytic and policy units. The result was a mess.

    I will wait to see what the maps show, but at the moment I think that the approach will fail for the same reason ARIA failed – it’s too artificial.   

  • rijopgeorge

    Our planners in India try a lot to look beyond the political boundaries of states and bring a regional basis to the development process – since the state boundaries are not corresponding to the regional boundaries and thus hamper effective policy and their implementation.

    I guess there is a higher degree of uniformity of characters among the inhabitants of regions than those of the demic divisions suggested here. When we go for a fixed unit of population as the base, we are forced to club together and average out the parameters which ultimately beats down the very purpose of the project. Delimiting regions based on a composite index such as HDI might be a better tool.  

  • Martin W. Lewis

    Many thanks to all for the provocative and well considered comments. I will respond to them in order.

    Luis Aldamiz is right that much smaller units would be be vastly preferable. For Europe, a conventional map is of better resolution than the demic map posted here. But for the world as a whole, it is simply not possible to create such smaller units, unless one had access to a well-funded research team that could work on the project for an extended period of time. And even then, acquiring comparable data for almost all of the districts of India and China would be very difficult, perhaps impossible. 

    Luis Aldamiz’s proposed division of Europe makes a good alternative as well, perhaps better than our own, and our GIS data-base would allow the restructuring of the map along his lines. We wanted, however, to divide as many countries as possible, as one of our goals was  to get away from the state-centered model as much as possible. There are also significant socio-economic differences within Germany. The former East Germany still lags behind on most indicators, although I have to admit that it fits better with the rest of Germany than it does with Poland, as we have mapped it here. Also, southern Germany is economically ahead of northwestern Germany, and we wanted to try to capture that difference, just as we wanted to show the high levels of economic production that are found in Europe’s “greater Alpine belt” that extends from central Germany to central Italy. Northern Italy still has elevated socio-economic figures, although that may change in the future.    

    Originally, Greece was classified with Spain, Portugal and southern Italy. We moved it to create a more compact region — and to, to be quite honest, to intentionally go against Greek and Turkish nationalism. Not that we are arguing against nationalism per se, but rather we maintain that not ALL maps and ALL considerations of the world should be based on a national framework. Again, the purpose of the project is to create AN alternative view of the world, not THE alternative view.  

    Nigeria is split because it has substantially more that 100 million. Also, almost all of the country’s oil is in the south, which therefore has a much higher level of economic production than the north. Unfortunately, we were not able to obtain good data on the economies of the states of Nigeria. Angola, and not Mozambique, is placed with South Africa because of their overall levels of per capita GDP. But for Angola, these relatively high figures almost entirely reflect the oil economy, rather than its overall level of development. By the same token, Yemen’s low economic figures are way out of line with the rest of the Arabian peninsula, which is why it is classified with Ethiopia. The same kinds of considerations led to the division of  the Korean Peninsula, and of China from Taiwan. I readily agree that the required compromises are considerable, perhaps too considerable in the end.

    Rijopgeorge makes a good point about planning and mapping.  But these maps, constructed at the global scale, have a different intention. The point is rather to give a crude snapshot of the world as a whole, to be used for basic educational and comparative purposes. The same consideration applies to Jim Belshaw’s comments. As far as Australia is concerned, the demic map conveys very little information. But that is a reflection of Australia’s small population. As we will see, the demic map is best suited to countries with large populations, especially India and China.    

     Andrew Zolnai also makes some very good points, but again it comes down to the scale of resolution. Doing what he suggests at the global would take years of labor, and for many parts of the world would simply not be possible. Our premise is that crude aggregations are of some use for basic instructional and comparative purposes. We hope that this will be apparent when we post the socio-economic maps using the demic framework and compare them with standard country-based economic maps later this week. As Jim Wilson notes, that should let the reader decide if this alternative scheme is of any utility.  

    • Anonymous

      Thanks for everyone’s reply on this. You are entirely correct this is _not_ an insignificant task, But as we discussed offline before,  geographic information systems (GIS) would makes it possible to (a) redraw the boundaries using your information or UN’s (though that would have to be laid out just as this demic map), and (b) recalculate the statistics on a geographic basis using stock GIS tools (though that would be the bulk of the work)

      I am no historian but I have managed terabyte data transfer projects. May I point to the CLIWOC project posted on my blog (http://bit.ly/qFWVvp), Esri site (http://bit.ly/acoSQ5) and original website (http://bit.ly/dqnKj5)? They had the monumental task of transcribing a century of ships captain’s logs from hand-written records, indeed got an EU funded project to do it, and ran out of time as noted in my postings. And the history project I referred to codified a millennia for geo-economical data for a small geographic area, but it can be done (latest iteration posted here http://bit.ly/qji3bK).

      So yes it would take years, but no it’s not impossible. But as I said I’m not in a position to do this now, unless a crowd-sourcing vehicle  or some funding is found by some institution to achieve that. It could actually be the basis for someone or some agency to propose the redrawing of post-colonial maps to, say, the UN.  But I suppose _that_ would make any geo-historic project look like some afternoon picnic…

    • Luis Aldamiz

      “We wanted, however, to divide as many countries as possible, as one of
      our goals was  to get away from the state-centered model as much as
      possible”.

      That’s a nice idea but for that you’d need to have worked with smaller units. States may make less sense than they usually intent but they still make some sense, and true homogeneous nation-states like Germany or Italy seem gratuitous to split.

      “The former East Germany still lags behind on most indicators”…

      Probably but they speak German. I don’t really like the econo-centric approach and instead prefer the ethno-centric one: the economy fluctuates wildly, culture needs a much longer time (or much greater impacts) to change.

      Even when I look at your GDP maps (new posts) I still have that feeling that the boundaries are crazy and meaningless. We do get some detail info on China and India internal divides but for the rest of the World… we actually lose info and the state map seems preferable.

      “Nigeria is split because it has substantially more that 100 million”.

      I’m getting old: I remembered 80 million (1980s it seems), today it’s 150 million: it has almost doubled its population in just a few decades!

  • Anthony_A

    If you are concentrating on economics, it might make more sense to divide into regions of approximately equal GDP – say $1 trillion, with similar GDP/capita across the region. That would give you about 60 regions, though the divisions would be quite different – the U.S. is 14 or 15 regions, and Canada is two, while sub-saharan Africa might be all one region. Perhaps one could build up regions until either 100 million people *or* $1 trillion GDP is reached, so as to not let any one region get too large.