The Urban Organism – Cities as living beings

By Vishnu Prasad, IFMR Finance Foundation

For the first time in the history of the world, more than half of the world’s population live in urban areas. In the next 13 years, a billion people are expected to migrate to cities, twice as fast as the rate just 30 years ago. Yet, as William Solecki1 argues in a recent article our understanding of the process of urbanization remains fragmented at best. This can partly be attributed to the fact that each academic discipline approaches the study of cities using a narrow analytical lens that precludes a holistic understanding of the process of urbanization and how it interacts with other systems like the environment for instance.

Some recent work has tried to bridge this gap in our understanding by looking at cities as complex adaptive systems. This perspective has been in vogue since the advent of cybernetics, which inspired a study of cities as machines or engineering systems. However, what these studies lacked was a scientific basis as theories of cities. With the increased availability of data on cities, research has now been able to move towards a science of urbanization2.

Biological Metaphor of the city

Researchers have long applied the biological metaphor to the city and likened them to living systems, organisms and eco-systems. In a recent paper, Bettencourt et al argue that this biological or evolutionary comparison cannot be dismissed as just a qualitative metaphor for the city3.

It is well established that almost all physiological characteristics (like metabolic rate, heart rate) of biological organisms scale with the body mass of the organism. As the authors note, “Conceptually, the existence of such universal scaling laws implies, for example, that in terms of almost all biological rates, times, and internal structure, an elephant is approximately a blown-up gorilla, which is itself a blown-up mouse, all scaled in an appropriately nonlinear, predictable way. This concept means that dynamically and organizationally, all mammals are, on the average, scaled manifestations of a single idealized mammal, whose properties are determined as a function of its size.

The authors examine whether similar scaling relationship can be found between cities and its social and material resources using data on cities in US, China and Europe. Using population as the measure of city size, the study finds three categories of scaling:

i. Linear scaling: Parameters of individual need like total housing in the city, total employment, electrical consumption and water consumption scale linearly with city size.

ii. Sub-linear scaling: Parameters of material resources or infrastructure scale sub-linearly. This means that doubling the population of any city requires only about an 85% increase in infrastructure like total road surface, length of electrical cables, water pipes or number of petrol stations. The 15% savings happens due to economies of scale, which enable more efficient and economically viable provision of services.

iii. Super-linear scaling: Social and economic parameters like GDP, total wages, new patents, number of inventors, R&D employment all increase by approximately 15% more than the expected linear growth. The same holds true of instances of crime and new cases of diseases like AIDS.

The most striking result of the study is that social and economic variables show super-linear scaling with respect to city population. However, it is pertinent to note that population size must be seen not as a causal force, but rather as a proxy variable that captures a set of diverse socio-economic mechanisms made possible by the co-location and intense interaction of people. As the paper argues, “these indicators reflect unique social characteristics with no equivalent in biology and are the quantitative expression that knowledge spillovers drive growth that such spillovers in turn drive urban agglomeration, and that larger cities are associated with higher levels of productivity.” Thus, the central idea is that cities are large social networks, not merely a large collection of people. The complex web of social interactions makes the city more than just a sum of its constituent parts.

Extending the biological metaphor, Bettencourt and West argue that cities are approximately scaled up versions of one another4. This is shown for 360 US Metropolitan areas in Figure 1 below.

Source: Luis Bettencourt & Geoffrey West. A unified theory of urban living. Volume 467. Nature. October 2010. Pp 912-13

These results present the first steps towards creating a scientific theory of cities and understanding the process of urbanization in greater depth. Although the theories we have discussed may not be prescriptive for policy-makers, “a new quantitative understanding of cities may well be the choice between creating a ‘planet of slums’ or finally achieving a sustainable, creative, prosperous, urbanized world expressing the best of the human spirit.

Additional resources on the subject can be found at http://www.santafe.edu/research/cities-scaling-and-sustainability/papers/

An interesting TED talk on the subject titled ‘The surprising math of cities and corporations’ can be seen here: http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html

  1. William Solecki is the Director of City University of New York’s Institute for Sustainable Cities. The article is available at: http://www.environmentmagazine.org/Archives/Back%20Issues/2013/January-February%202013/urbanization-full.html
  2. The Kind of problem a city is. Available at: http://www.santafe.edu/media/workingpapers/13-03-008.pdf
  3. Growth, innovation, scaling, and the pace of life in cities. Available at: http://www.pnas.org/content/104/17/7301.abstract
  4. A unified theory of urban living. Available at: http://www.cabdyn.ox.ac.uk/complexity_PDFs/Publications%202010/Nature_Cities.pdf

  • http://www.prassrinivasan.blogspot.com Pras

    By coincidence I caught this topic on edge.org with Geoffrey West on the subject a couple of years ago.

    A Conversation With Geoffrey West [5.23.11]

    You may like to read it as well!

    There are a lot of other such lateral thinking pieces there that you may find relevant and interesting!

    • Vishnu Prasad

      Dear Pras, thank you for the link.

      Geoffrey West reiterates some of these points in his TED talk as

  • @rahulrg

    Excellent post, thanks for sharing. 3 points: a) If this geography and climate independent model applicable to cities, surely it must be applicable to companies as well? (though scaling is largely sub-linear in this case, I would think) b) Silicon Valley has medium population density and is also the most innovative place in the world; on the surface, it appears to be a major exception to the model? (i.e. for its population and sprawl, the model will probably predict lesser gains than is actually realized from the mechanisms you state) c) Largely, the mechanism here is really down to “gains from trade” within the region, what of gains from external trade? (i.e. in today’s world, there is presumably a “gain from globalization”, surely that is extraneous to this model?)

    • Vishnu Prasad

      Dear Rahul,

      Thank you for your comment. We’re very happy to know that you
      follow the cities blog.

      a. The TED talk and the link to additional resources provide
      greater input on the applicability of this theory to corporations (something
      we’re not explored in this post).

      b. Lobo et al (2013) (accessible at: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058407)
      identify Los Alamos (which hosted the Manhattan Project) and Silicon Valley (the San Jose-Santa Clara, Metropolitan Area in California) as the urban areas with highest productivity in the US. However, these cities, as you rightly pointed
      out, are outliers to the model. The paper argues that “there is a systematic
      dependence of urban productivity on city population size.” However, “deviations
      from the average scale dependence of economic output, capturing the effect of
      local factors, including history and other local contingencies, also manifest
      surprising regularities. Although, productivity is maximized by the combination
      of high wages and low labor input, high productivity cities show invariably
      high wages and high levels of employment relative to their size expectation.
      Conversely, low productivity cities show both low wages and employment.”