I
used to make a big deal about definitions of urbanism, and how to distinguish cities from other settlements. This is a worthwhile effort, useful
for many purposes. But I now find that overemphasizing the distinction between,
say, villages and cities can get in the way of understanding some of the major social
dynamics of these settlements. Key dimensions of social life show remarkable
similarities between villages and cities. For years I questioned the similar notion
that major differences characterized rural and urban life, based on my
excavations at Aztec sites (Smith 2008, 2016). In some settings around the world, rural and urban
lifestyles and social conditions are radically different, but in others—such as
Aztec-period Morelos, Mexico—they are quite similar. But my topic here is more
general: what are some of the similarities or continuities between social life
in villages and cities? This question relates closely to research on settlement
scaling.
Our
research group on settlement scaling (AKA “urban scaling”) now has a project
name and a website—the Social Reactors Project: Human Settlements andNetworks in History. I have been thinking a lot lately about how the work in
scaling relates to other research issues and themes in archaeology and the
social sciences more generally. This is the first of several posts about the social
and scientific context of this research. These are exploratory posts—me
thinking aloud about connections between research domains—and not reports of
well-established (and published) ideas. I welcome feedback, particularly if I say something dumb.
One
of the remarkable empirical findings of the work on settlement scaling is that
village settlement systems exhibit some of the same quantitative regularities
as do contemporary (and historical/ancient) urban systems. See the project website or previous posts (here, or here) on this. This finding is predicted from the
“social reactors” perspective on scaling. The actions of large numbers of
people gathered in specific places have predictable quantitative outcomes in
key social domains. The foundation of the social reactors approach is a
mathematical model based on the number of people who interact socially within a
given built environment. This model is presented in Bettencourt (2013). But does the finding of continuities between village and
urban systems make sense from other perspectives? My answer is yes. Here I will
focus on two areas: neighborhood formation; and the process of settlement
aggregation. Recent research in these areas supports the scaling results by
showing how settlement size plays a similar role in both village and city settlement
systems.
The universality of neighborhoods
I
have claimed for a number of years now that the division of urban residents
into neighborhoods is one of the few universals of cities around the world and
through history (Smith 2010, 2012). A few years ago I got a bunch of undergraduates together
to test this idea by investigating whether neighborhoods existed in what we
called “semi-urban settlements” (Smith et al. 2015). These are settlements that are not true cities, but do
have large numbers of people gathered together, often for a short period of
time. The idea was that if semi-urban settlements exhibit neighborhood
organization, this would support the claim that neighborhoods are universal
traits of cities.
Each
student took one or more types of semi-urban settlements, and looked at maps
and read reports to look for the division of the settlement into spatial zones
that had some social significance. We identified a total 17 types of semi-urban
settlement, of which 11 had enough information to evaluate the presence or
absence of neighborhoods. Ten of those eleven settlement types did indeed show
neighborhood organization (only disaster camps lacked neighborhoods). Here is
the total list of semi-urban settlements we looked at:
Sometimes of these settlements are created forcibly by authorities for their own purposes (“force” in the table), and in other cases the settlements are created by
necessity. And in still other cases, the settlements are created by the individual actions of residents ("volunteer" in the table). The fact that
ten out of the eleven settlement types we studied show clear neighborhood
organization supports the contention that neighborhoods are a universal feature
of urban, and urban-like settlements. In other words, when settlement reach a
certain size, neighborhood organization—whether imposed or generated by
residents—is inevitable.
But
the universality of neighborhood organization goes beyond this. In reviewing my
notes, I see that apart from some brief mentions here and there (Smith 2010, 2011), I never published my findings on rural neighborhoods. So,
here is a brief summary. In many traditional settlement systems,
neighborhood-like spatial clusters are found in rural areas. In my paper on
Classic Maya neighborhoods, I show that these rural settlement clusters are
functionally equivalent to urban neighborhoods, and that they are the kind of
unit that serves as an urban neighborhood in low-density tropical cities, such
as the Classic Maya or the Khmer (Smith 2011).
Here
are two examples of named social groups that serve as neighborhoods in both urban
and rural settings: the Aztec calpolli,
and the north African darb. The calpolli was a group of Aztec households
with common economic activities. In rural areas, nobles owned the farmland, but
decisions on how to divide up plots was made by the calpolli, whose members
lived together in a village. Only calpolli members had direct access to land. In
urban areas, a calpolli was an urban neighborhood whose members often had
similar economic specialization. Each calpolli had a temple and a school. For
discussion, see: Smith and Novic (2012); for a non-technical account, see my new book (Smith 2016:chapter 7).
In
traditional north African societies, the darb
is a unit similar to the calpolli—a neighborhood in urban centers, and a
village or village section in rural areas. In rural areas, the darb:
“functions as an intermediate structure between the family and the village. It was once described to me as the ‘middle branch’ (al-far’ al-wasat). It is primarily the place where events pertaining to the individual take on a public or communal significance. (Saad 1998:115).
In
urban settlements, the darb is a neighborhood (typically called a “quarter” in
English-language descriptions), a social division of a town or city (Eickelman 1974). For more examples of rural neighborhoods, see my discussion
of highland Maya rural neighborhoods (Smith 2011). See also Anthony Kaye’s discussion of rural neighborhoods
in the slave communities of the antebellum southern U.S. (Kaye 2002, 2007a, 2007b).
The
implication of these cases is that when human settlements reach a certain size,
the residents will create neighborhoods, whether they live in an urban center or
in a rural area, in a city or in a village. Unless, of course, the
neighborhoods have been pre-established by the builders or authorities. Two
obvious questions—How large a population is required to trigger neighborhood
formation? and, How big are neighborhoods?—cannot be answered with current
information. Years ago, when I started working on neighborhoods, I tried to track
neighborhood size, but the data are just too scattered and I gave up.
Settlement aggregation
When
I first learned about Luis Bettencourt’s scaling model, and realized that it predicted
similar quantitative patterns for village settlements as for cities, I was skeptical.
One of my concerns focused on the nature of population movements in the two kinds
of settings. The fact that people can move around, into and out of cities and regions,
seemed important for the development of the scaling regularities. I don’t think
such movement is an absolute requirement of the formal model, but for some settlements
to grow larger than others, people have to be able to move around. Thus I was
curious about two processes. First, did people in the past really move around
very much? Or were farmers and peasants “tied to the land,” as in traditional
models? Second, was the process of village aggregation—people moving into villages—similar
to the process or urbanization (in the sense of people moving into cities)?
To
answer these questions, I reviewed the published literature on spatial mobility
and village aggregation in premodern societies. In brief, the evidence supports
the scaling model, in that mobility and movement could be substantial in the
past, and village aggregation was quite similar to rural-to-urban migration. The
journal World Archaeology was just
assembling a special issue on past migration and mobility, so I published the
paper there (Smith 2014).
I
was amazed at the extent of spatial mobility in late medieval and early modern
European villages. Robin Osborne (1991) had reviewed the literature earlier, and I updated his
analysis. In a famous example published by Peter Laslett, censuses were taken
in two 17th century villages twelve years apart. In the second enumeration,
only 38% of the original residents were still in the villages! While the mobility
rate may have been particularly high in Britain for structural reasons, there
are many examples of considerable social turnover in peasant settings around
the world.
In
looking at aggregation processes, studies of three different domains coincide
in identifying defense as one of the key drivers of such movement. First, most
archaeologists agree that the basic cause for the aggregation of population into
early villages was the need for defense (Bandy and Fox 2010; Birch 2013). Second, the same factor is identified for the nucleation
of settlement in rural parts of the developing world (Silberfein 1989). And third, defense was one of the predominant drivers of
urbanization before the modern era in many regions (Adams 1981; Flannery and Marcus 2012). In other words, movement into town, or into the village, was
in many cases caused by the same forces—the need for defense and protection.
Conclusion
These
considerations of neighborhood formation and premodern settlement aggregation
lend support to the social reactor model of settlement scaling. While the idea
that village systems exhibit the same quantitative properties as urban systems
may seem strange at first, it becomes more comprehensible when we focus on
population size and its implications. If we get too hung up on defining
urbanism and highlighting differences between urban and non-urban contexts
(yes, guilty as charged…..), this can blind us to some remarkable similarities
in these two kinds of settings. The key factor uniting them is the role of population
size in generating certain kinds of processes and outputs.
While
it is possible to claim that the data on neighborhoods and aggregation
processes support the scaling model, it is also possible to turn this around
and claim that the scaling results support the models from neighborhood and
aggregation analysis. But I think the most productive approach is to point out
that research in these three distinct domains (and probably others as well) all
point to similar conclusions: Many social processes transcend the traditional
urban / non-urban distinction; these all involve the consequences of the
numbers of people who live or work (and interact) in a settlement; and, human settlement
dynamics are remarkably similar around the world and through history.
Stay
tuned for more exploration of the context of settlement scaling. Next topic: scaling
and social science theory.
REFERENCES
Adams, Robert McC.
1981 Heartland of Cities: Surveys of Ancient
Settlement and Land Use on the Central Floodplain of the Euphrates.
University of Chicago Press, Chicago.
Bandy, Matthew S. and Jake R. Fox
2010 Becoming Villagers:
The Evolution of Early Village Societies.
In Becoming Villagers: Comparing Early
Village Societies, edited by Matthew S. Bandy and Jake R. Fox, pp. 1-16.
University of Arizona Press, Tucson.
Bettencourt, Luís M. A.
2013 The Origins of
Scaling in Cities. Science 340:
1438-1441.
Birch, Jennifer
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Environmental Change) 8: 15-19.
2014 PeasantMobility, Local Migration, and Premodern Urbanization. World Archaeology 46 (4): 516-533.
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