Spatial patterns in the US

Here are four interesting graphics representing different kinds of activity in the United States.  The top panel represents population concentrations across the United State.  The second image is air traffic across the country, and the third image is internet traffic across the country.  The final image is a photograph of the United States from space at night, showing the concentration of lights across the country.  Basically the images correspond to where people live; where they travel; and where they exchange data.  Unsurprisingly, the maps line up very well.

The most interesting question to consider is not the structure of the networks represented by air travel and internet activity.  The nodes of both air travel and internet traffic line up exactly with the cities and metropolitan areas represented in the population map, and they align well with the concentration of lighted areas in the bottom frame as well.  The patterns of both air travel and internet traffic take the form of a swath extending from the dense eastern corridor of the US (Boston to Washington) to California (San Francisco to San Diego), with Chicago standing out as a significant node in the middle of the country.  This is entirely obvious and predictable; travel and communication follow population density.

Rather, the more interesting question is this: to what extent is there evidence of internet and air traffic at a node that is disproportionately greater than would be expected given the population of that node?  This is the kind of question that drives Saskia Sassen’s classification of cities as local, regional, national,  and global (The Global City: New York, London, Tokyo and Global Networks, Linked Cities).  (Here is an earlier post on Sassen’s work.)

Fundamentally, a city that originates 10 times the volume of internet traffic compared to other cities of similar size is worth looking at in detail.  Its activity level suggest an exceptional concentration of organizations and people that are unusually integrated into global networks of communication and data exchange.  It is a location for knowledge-intensive activity: high-end services, banking, universities; and it is a place with intensive relations to other nodes.  Likewise, a city that originates 5 times as many air-travel passengers as comparably sized cities elsewhere is likely to be a specialized location for business and high-end service activity (or else a population of very dedicated tourists).  In other words, the most interesting feature that these data sets might show us about the economic roles of US cities is not visible in the graphics presented here.

So it would be very interesting to be able to “divide” the activity levels represented by the middle two graphs by the populations represented by the top graph, so we could see which cities in the US are “super data exchangers” and “super travel generators”.  And this would give us an indication of the degree of high-end, knowledge-intensive activity that is concentrated in the place — thereby providing a measure of its importance in the national and global economy.  It is possible, for example, that Ann Arbor or Madison would show up as spikes of internet activity relative to their relatively small populations; and Raleigh-Durham might show up as a spike of air travel relative to population, reflecting an unusual concentration of high-end service businesses in this region.  The above-average data and air-traffic nodes are perhaps the dynamic centers of 21st-century economic activity.

Likewise, it would be interesting to identify the cities that have lower-than-expected levels of travel or internet activity; this would suggest local economies that are somewhat more self-contained and less integrated into the national economy than other locations.  And it would be interesting to see if there are significant pairings among locations for either kind of transaction; for example, is the volume of data exchange between Los Angeles and New York significantly greater than that between New York and Chicago or Boston?  And would this serve as an indicator of the degree of economic and business integration between these specific locations?

These views of the United States are interesting because they allow us to see the country as an inter-connected system of places and activities. They serve as something like a dynamic CT scan of the brain: certain connections between places “light up”, providing an indication of systemic activities that warrant further investigation.

(The middle panel was published in the Harvard Alumni Magazine (link).  The population map comes from Urbanomnibus (link).)

Skinner’s spatial imagination

images: presentations of Skinner’s data by Center for Geographic Analysis, Harvard University, AAS 2010

G. William Skinner was a remarkably generous scholar who inspired and assisted several generations of China specialists.  (Here is a link to a remembrance of Bill.)  He was prolific and fertile, and there is much to learn from rereading his work. There is quite a corpus of unpublished work in the form of research reports and conference papers.  Rereading this work is profoundly stimulating. It holds up very well as a source of ideas about social science analysis of concrete historical and social data, and there are many avenues of research that remain to be further explored.
Skinner is best known for his efforts to provide regional systems analysis of spatial patterns in China.   He thought of a social-economic region as a system of flows of people, goods, and ideas.  He argued for the crucial role that water transport played in knitting together the economic activities of a region in the circumstances of pre-modern transport.   
Skinner’s work demonstrated the great value of spatial analysis.  Patterns emerge visually once we’ve selected the appropriate level of scope.  Mapping social and economic data is tremendously insightful.  He was also highly sensitive to the social and cultural consequences of these flows of activity.  For example, patterns of gender ratios show a pronounced regional pattern; Skinner demonstrates the relevance of core-periphery structure to social-cultural variables such as this one. 
Skinner plainly anticipated the historical GIS revolution conceptually.  And this is a feature of imagination, not technology.
A classic series of articles on the spatial structure of the Chinese countryside in the 1960s provided an important basis for rethinking “village” society. They also provided a rigorous application of central place theory to the concrete specificity of China.  Here are several maps drawn from these essays (“Marketing and Social Structure in Rural China.” Journal of Asian Studies 24 (1-3), 1964-65). Here Skinner is trying out the theories of central place theory, and the theoretical prediction of economic space being structured as a system of nested hexagons with places linked by roads.
Another key contribution of Skinner’s work is his analysis of China in terms of a set of eight or nine “macroregions”.  He argues that China was not a single national economic system, and it was not a set of separate provincial economies.  Instead, it consisted of a small number of “macroregions” of trade, commerce, and population activity, linked by water transport.  And macroregions were internally differentiated into core and periphery.  
Skinner used meticulous county-level databases to map the economic and demographic boundaries of the region.  Skinner identified core and periphery in terms of population density, agricultural use, and other key variables.  And he then measured a host of other variables – female literacy, for example – and showed that these vary systemically from core to periphery.  There is also an important ecological dimension to the argument; Skinner demonstrated that there is a flow of fertility from periphery to core as a result of the transfer of food and fuel from forests to urban cores.  (This analysis is developed in “Regional Urbanization in Nineteenth-Century China” in The City in Late Imperial China, edited by G. W. Skinner, Stanford University Press, 1977.)  Here are three maps developed by Skinner and his collaborators on the basis of the macroregions analysis.
This is a particularly expressive map of the Lower Yangzi macroregion, differentiated into 4 levels of core and periphery.  This is pretty much the full development of the macroregional analysis.
Another key idea in Skinner’s work is his analysis of city systems into a spatial and functional hierarchy. He argued that it is possible to distinguish clearly between higher-level and lower-level urban places, and that there is an orderly arrangement of economic functions and marketing scope associated with the various urban places in a macroregion.
So regional analysis of China is a key contribution in Skinner’s work. But Skinner did not restrict his research to China alone. He also did significant work on Japanese demography and family structure and female infanticide in the 1980s (for example, “Reproductive Strategies and the Domestic Cycle among Tokugawa Villagers,” an AAS presentation in 1988).
And he brought his regional systems analysis to bear on France in an extended piece of research in the late 1980s. The maps that follow are drawn from an unpublished conference paper titled “Regional Systems and the Modernization of Agrarian Societies: France, Japan, China,” dated 1991. This paper builds upon a 1988 paper titled “The Population Geography of Agrarian Societies: Regional Systems in Eurasia.”
This analysis builds a view of France as a set of interrelated regions with core-periphery stucture.  Through the series of working maps Skinner painstakingly constructs an empirically based analysis of the economic regions of France in mid-nineteenth century.  And Skinner then asks one of his typically foundational questions: how do these geographical features play a causal role in cultural and demographic characteristics?
This map of never-married/married female ratios is one illustration of Skinner’s effort to relate social, cultural, and demographic variables to the core-periphery structure of a region.  The pattern of high ratio corresponds fairly well across the map of France to the regions identified by demographic and agricultural factors.  And this serves to confirm the underlying idea — that economic regionalization has major consequences for cultural and demographic behavior.
Likewise patterns of female life expectancy and net migration; here again we find the kind of regionalization of important social variables that Skinner documents in great detail in late imperial China.
Finally, Skinner also played an important role as a “macro-historian” of China.  His 1985 Presidential Address to the Association for Asian Studies was a tour-de-force, bringing his macroregional analysis into a temporal framework (Skinner, G. William. 1985. Presidential Address: The Structure of Chinese History. Journal of Asian Studies XLIV (2):271-92).  In this piece he demonstrates a “long-wave” set of patterns of economic growth and contraction in two widely separated macroregions.  And he argues that we understand China’s economic history better when we see these sub-national patterns.  He analyzes the economic and population history of North China and Southeast Coast, two widely separated macroregions, over several centuries.  And he demonstrates that the two regions display dramatically different economic trajectories over the long duree.  Skinner brings Braudel to China.
Here is the pattern he finds for two macroregions over a centuries-long expanse of time.  And significantly, if these patterns were superimposed into a “national” pattern, it would show pretty much of a flat performance, since the two macroregions are significantly out of phase in their boom and bust cycles.
Finally, an enduring contribution that Skinner made is his cheerful disregard of discipline. Economic anthropology, regional studies, demography, urban studies, history … Skinner moved freely among all these and more. It was topics and questions, not disciplinary strictures, that guided Skinner’s fertile and rigorous imagination.  And area specialists and social scientists alike can fruitfully gain from continued study of his research.  Fortunately, work is underway to make Skinner’s unpublished research and data available to other scholars.  Here are some major projects:
  • Data and maps are being curated and presented at Harvard. Here is a beta site and here is the platform the China GIS team is using at AfricaMap.
  • The Skinner Archive at Harvard (link)
  • Skinner’s unpublished papers and research materials are being digitized and presented at the University of Washington.  Here is a link.
  • The China Historical GPS project at Fudan University is presenting an ambitious digital mapping collection as well (link). 
(Presented at the Association for Asian Studies, Philadelphia, March 2010; panel on Skinner’s legacy.)

A spatial twitter feed for Southeast Asia

Quite a few of the items included in the UnderstandingSociety twitter feed currently refer to events and conditions in Southeast Asia — especially Burma, Malaysia, and Thailand. This posting introduces a dynamic map that I’ll try to update with new feed items as they occur when it is possible to identify a specific location. It is possible to change the frame and scale of the Google map, and if you click on the pointers you’ll find a link to the associated news item. Here is a direct link to the map, and it will remain accessible in the sidebar as well.

I have also provided static maps that indicate the provinces and cities of Burma and Thailand.

The integrative power of Google Maps is pretty striking in this example. If you go to the “larger map” through the link above, it is possible to turn on several other layers of the map, including photos, videos, wikipedia articles, and webcams. These are photos and videos that users have posted with geo-tags, so that they are associated with their locations on the Google map. This resource will become vastly deeper over time, as more and more users tag their photos and videos spatially. There are currently very dramatic photos available on the web of Karen refugees passing across the Burma-Thai border near the village of Noh Bo. These photos aren’t geotagged at present, but one would expect that it won’t be long before the user could expand the map around this key area of conflict; zoom in to find the geo-tagged photos and videos that are associated; and gain a much more vivid understanding of the current reality in the region.

This effort at linking a selected stream of news stories about Southeast Asia with their locations across the region contributes to the overall goal of UnderstandingSociety in a fairly basic way: it’s an experiment in trying to organize and synthesize the hundreds of news items and events that can be observed through the twitter stream, into something that is a more structured presentation of part of the social reality of a country or region.

Several important issues are in the twitter feed at present:

  • Burma — the Junta’s trial of Aung San Suu Kyi; the army’s assault on Karen areas; the plight of Karen refugees fleeing into Thailand; the conduct of army units in Kachen; massive corruption and theft of resources by the army.
  • Thailand — aftermath of the Red Shirt demonstrations of March and April; political activities of the emerging parties; the southern insurgency and government efforts to control the southern states.
  • Malaysia — protests against discrimination directed at ethnic minorities (Indian, Chinese, Christian); jockeying among political parties.

Is this a good way of making spatial sense of the news items about what is happening in Burma, Malaysia, and Thailand?

Is a rail network a social structure?

figures: RER map of Paris; E. J. Marey’s graphical representation of Paris-Lyon train schedules, 1885

What role does a rail network play within an adequate ontology of society? Is a rail system primarily a set of physical assets, a set of administrative procedures, or a set of embodied opportunities and constraints for other members of society? The answer is, a transportation system has elements of all of these.

A rail system provides convenient transportation among a number of places, while providing no service at all between other pairs of locations. You can get from Porchefontaine to Sevran Livry with only a change of trains at St. Michel – N Dame in about 30 minutes — whereas from Point X to Point Y there is no convenient transportation connection by Metro or RER. This means, among other things, that some parts of Paris are much more tightly integrated than others. It is possible for residents of arrondissement X to shop and work in arrondissement Y very conveniently, whereas this would not be true for arrondissement Z.

So a rail system certainly has direct effects on social behavior; it structures the activities of the two million or more Parisians by making some places of residence, work, shopping, and entertainment substantially more accessible than other places. And there are a number of other social characteristics that are structured by the commuter rail system as a consequence: for example, patterns of class stratification of neighborhoods, patterns of diffusion of infectious disease, patterns of ethnic habitation around the city, patterns of diffusion of social styles and dialect, … In brief, a rail system has definite social effects. It creates opportunities and constraints that affect the ways in which individuals arrange their lives and plan their daily activities. And other forms of social behavior and activity are conveyed through the conduits established by the transport system.

Moreover, a rail system is a physical network that has an embodied geometry and spatiality on the ground. Through social investments over decades or more, tracks, stations, power lines, people movers, and fuel depots have been created as physical infrastructure for the transportation network. Lines cross at junctions, creating the topology of a network of travel; and the characteristics of travel are themselves elements of the workings of the network — for example, the rate of speed feasible on various lines determines the volume of throughput of passengers through the system. And neighborhoods and hotels agglomerate around important hubs within the system.

In addition to this physical infrastructure, there is a personnel and management infrastructure associated with a rail system as well: a small army of skilled workers who maintain trains, sell tickets, schedule trains, repair tracks, and myriad other complex tasks that must be accomplished in order for the rail system to carry out its function of efficiently and promptly providing transportation. This human organization is surely a “social structure,” with some level of internal corrective mechanisms that maintain the quality of human effort, react to emergencies, and accomplish the business functions of the rail system. This structure exists in the form of training procedures, operating manuals, and processes of supervision that maintain the coordination needed among ticket agents in stations, repairmen in the field, track inspectors, engineers, and countless other railroad workers. And this structure is fairly resilient in the face of change of personnel; it is a bureaucratized structure that makes provision for the replacement of individuals in all locations within the organization over time.

So a rail network has structural characteristics at multiple levels. The physical network itself has structural characteristics (nodes, rates of travel, volume of flow of passengers and freight). This can be represented statically by the network of tracks and intersections that exist (like the stylized map of the RER above); dynamically, we can imagine a “live” map of the system representing the coordinated surging of multiple trains throughout the system, throughout the course of the day. The railroad organization has a bureaucratic structure — represented abstractly by the organizational chart of the company, but embodied in the internal processes of training, supervision, and recruitment that manage the activities of thousands of employees. And the social and technical ensemble that these constitute in turn creates an important structure within the social landscape, in that these physical and human structures determine the opportunities and constraints that exist for individuals to pursue their goals and purposes.

A general problem that confronts assertions about “social structures” is the question, what factors give the hypothesized structure a degree of permanence over time? Why should we not expect that social structures will morph quickly in response to changing uses and demands by opportunistic actors within them? A rail system provides a somewhat more definite answer to this question than is possible for most putative social structures: the physicality of the system is itself a barrier to rapid, radical structural change. The locations of the great rail terminus stations in Paris have not changed in the past century. And this is at least in part a consequence of the vast “sunk costs” that are associated with the embodied structure of track, intersection, and station that had developed over the course of the first fifty years of French railroad expansion. So the need for a passenger from Dijon to Strasbourg to convey himself/herself from the Gare de Lyon (1900) to the Gare de l’Est (1849) is exactly the same today as it was in 1900.

Mapping social data

Fig. 1. Household income inequality (US Census 2000 link)

Fig. 2. Poverty rates (US Census Data 2000 link)

Fig. 3. 60 day bank card delinquency — Q1 2008 (Federal Reserve Bank link)

Fig. 4. 90 day mortgage delinquency — Q1 2008 (Federal Reserve Bank link)

It’s interesting to look at each of these data maps in detail. The comparisons of patterns are very revealing. Figure 4 is the most familiar to us — it shows the geographical distribution of the mortgage crisis across the country. California and Florida aren’t a surprise; but there are other hot spots across the country. For example, the “rust belt” of Michigan, Ohio, and Indiana shows a pretty dense set of high mortgage delinquency counties. But then consider the next crisis that may be coming — consumer credit default. Here again the spatial patterns are interesting. Mississippi and Louisiana jump off the map on the poverty and inequality maps, and on the bank card default map as well. And then you can compare these state-by-state patterns with the top two figures, mapping poverty and inequality across the counties of the United States. And, finally, it’s interesting to compare all these patterns of economic distress — with the pattern of blue and red counties in the 2000 Presidential election.