urban scaling laws - university of sydney · - income / gdp - patents - any measure of economic...
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Urban Scaling Laws:Foundations, Implications, Gaps
Somwrita Sarkar University of Sydney
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Cities as complex systemsPhysically, socially, economically, cities manifest as
regularities, in spite of different historical trajectories and forces of growth. They also show heterogeneities!
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Cities as complex systemsSo, throughout, the science of cities to focus on
what cities are (positive aspects) and on
what they should be (normative aspects).
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Cities as complex systemsRising urbanisationRising inequalities
Are urbanisation and inequality connected?
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Are urbanisation and inequality connected?
85%of Australia's21.5 million peoplelived in urban areasin 2011.Source: Australian Bureau of Statistics, Census 2011, Populations of Significant Urban Areas to Total Population.
Complexity, Criticality, Computation, 2017
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Are urbanisation and inequality connected?
60%of Australia's21.5 million peoplelived in the 5 capital cities in 2011.Source: Australian Bureau of Statistics, Census 2011, Populations of Significant Urban Areas to Total Population.
Complexity, Criticality, Computation, 2017
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Are urbanisation and inequality connected?C
ompl
exity
, Crit
ical
ity, C
ompu
tatio
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61% $2.2 millionper household
1%$31,065 per household
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Are urbanisation and inequality connected?
A highly urbanised population
With significant & growing inequality
Living in a very small number of urban centres
Complexity, Criticality, Computation, 2017
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Are urbanisation and inequality connected?
Larger city = higher inequality?
Is this a proportional pattern? Then, no worries!
Does this grow disproportionately?(Income and housing costs)
Complexity, Criticality, Computation, 2017
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Enter urban scaling laws
City Size (Population)
Urb
an in
dica
tor
● Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., and West. G., 2007, Growth, innovation, scaling, and the pace of life in cities, PNAS, 104(17), pp.7301-7306.
● Bettencourt, L., 2013, The origins of scaling in cities, Science, 340, pp. 1438-1441.
Complexity, Criticality, Computation, 2017
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Enter urban scaling laws
City Size (Population)
Urb
an in
dica
tor
Sub-linear
Economic inputs:
- Lengths of roads- Lengths of
infrastructure networks
- Any measure of per capita expenditure on maintaining a city
● Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., and West. G., 2007, Growth, innovation, scaling, and the pace of life in cities, PNAS, 104(17), pp.7301-7306.
● Bettencourt, L., 2013, The origins of scaling in cities, Science, 340, pp. 1438-1441. Com
plex
ity, C
ritic
ality
, Com
puta
tion,
201
7
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Enter urban scaling laws
City Size (Population)
Urb
an in
dica
tor Super-linear
Economic outputs:
- Income / GDP- Patents- Any measure of
economic output or benefit
- But also diseconomies like crime, pollution, congestion
● Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., and West. G., 2007, Growth, innovation, scaling, and the pace of life in cities, PNAS, 104(17), pp.7301-7306.
● Bettencourt, L., 2013, The origins of scaling in cities, Science, 340, pp. 1438-1441. Com
plex
ity, C
ritic
ality
, Com
puta
tion,
201
7
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Larger cities are more efficient, innovative, productive, diverse...
Sarkar and Searle, SOAC2017
City Size (Population)
Urb
an in
dica
tor Super-linear
Fundamental ideas:
- Urban economics- Increasing returns to
scale- Marshallian
agglomeration principles
Sub-linear
● Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., and West. G., 2007, Growth, innovation, scaling, and the pace of life in cities, PNAS, 104(17), pp.7301-7306.
● Bettencourt, L., 2013, The origins of scaling in cities, Science, 340, pp. 1438-1441.
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Larger cities are more efficient, innovative, productive, diverse...
City Size (Population)
Urb
an in
dica
tor Super-linear Y = aXb,
Where Y = urban indicator, X = population, a = constant, b = scaling exponent.
If b < 1, sub-linear If b = 1, linearIf b > 1, super-linear
Sub-linear
● Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., and West. G., 2007, Growth, innovation, scaling, and the pace of life in cities, PNAS, 104(17), pp.7301-7306.
● Bettencourt, L., 2013, The origins of scaling in cities, Science, 340, pp. 1438-1441. Com
plex
ity, C
ritic
ality
, Com
puta
tion,
201
7
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● Bettencourt, L., Lobo, J., Helbing, D., Kuhnert, C., and West. G., 2007, Growth, innovation, scaling, and the pace of life in cities, PNAS, 104(17), pp.7301-7306.
● Bettencourt, L., 2013, The origins of scaling in cities, Science, 340, pp. 1438-1441.
Larger cities are more efficient, innovative, productive, diverse...
Com
plex
ity, C
ritic
ality
, Com
puta
tion,
201
7
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But, the validity of these findings is open to debate...
● Arcaute, E., Hatna, E., Ferguson, P., Youn, H., Johansson, A., and Batty, M., 2014, Constructing cities, deconstructing scaling laws, Journal of the Royal Society Interface, DOI: 10.1098/rsif.2014.0745. C
ompl
exity
, Crit
ical
ity, C
ompu
tatio
n, 2
017
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But, the validity of these findings is open to debate...
● Louf., R and Barthelemy, M., 2014, Scaling: Lost in the smog, Environment and Planning B: 41, pp. 767-769. C
ompl
exity
, Crit
ical
ity, C
ompu
tatio
n, 2
017
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But, the validity of these findings is open to debate...
● Leitao, J.C., Miotto, J.M., Gerlach, M., and Altmann, E.G., 2016, Is this scaling nonlinear?, Royal Society Open Science, DOI: 10.1098/rsos.150649. C
ompl
exity
, Crit
ical
ity, C
ompu
tatio
n, 2
017
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Why are these findings interesting to us?
Back to the “bigger is better” claim/idea,
we wanted to explore specifically the dimension of INEQUALITY.
Hypothesis:Larger cities are richer, but they are also more unequal.
Complexity, Criticality, Computation, 2017
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The scaling of income and housing cost distributions
AimStudy scaling
of income and number of people in
income categories
with city size
Data
Australia,Source: Australian
Bureau of Statistics (ABS),
Australian Taxation Office
(ATO)
Variables
X: Population of Significant Urban Areas, Y: No. of people in each income, rent or
mortgage category
MethodLinear regression
of log(popn) against
log(variable)&
Maximum likelihood based
model testing
Sarkar, S., Phibbs, P., Simpson, R., Wasnik, S., 2016, The scaling of income distribution in Australia: Possible relationships between urban allometry, city size and economic inequality, Environment and Planning B, doi:10.1177/0265813516676488
Complexity, Criticality, Computation, 2017
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The scaling of income and housing cost distributions
Sarkar, S., Phibbs, P., Simpson, R., Wasnik, S., 2016, The scaling of income distribution in Australia: Possible relationships between urban allometry, city size and economic inequality, Environment and Planning B, doi:10.1177/0265813516676488Complexity, Criticality, Computation, 2017
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The scaling of income and housing cost distributions
Sarkar, S., Phibbs, P., Simpson, R., Wasnik, S., 2016, The scaling of income distribution in Australia: Possible relationships between urban allometry, city size and economic inequality, Environment and Planning B, doi:10.1177/0265813516676488
● Larger the city, more pronounced the super-linear behaviour of high income categories.
● Higher the cut-off for population size or densities, more pronounced the super-linear behaviour of high income categories.
Complexity, Criticality, Computation, 2017
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The scaling of income and housing cost distributions
Sarkar, S., in review.
● Larger the city, more pronounced the super-linear behaviour of high income categories.
Complexity, Criticality, Computation, 2017
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The scaling of income and housing cost distributions
Rent categories scale in the same way:
Lower rents sub-linear and higher rents superlinear with city size
Complexity, Criticality, Computation, 2017 Sarkar, S., in review.
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The scaling of income and housing cost distributions
Mortgage categories scale in the same way:
Lower mortgage numbers sub-linear and higher mortgage numbers superlinear with city size
Complexity, Criticality, Computation, 2017 Sarkar, S., in review.
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The scaling of income and housing cost distributionsUSA housing cost categories scale in the same way:
Lower costsnumbers sub-linear and highercosts numbers superlinear with city size
Complexity, Criticality, Computation, 2017 Sarkar, S., in review.
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The geographic angle: Location Quotients
Complexity, Criticality, Computation, 2017 Sarkar, S., in review.
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The scaling of income and housing cost distributions
Note that these are not proportional but disproportional rises by city size.
This is empirical evidence for agglomerations of higher earners, higher living costs in larger cities, pushing lower income earners out, driving inequality.
Is there a role for policy and planning?
Complexity, Criticality, Computation, 2017Sarkar, S. and Searle, G., 2017, State of Australian Cities SOAC conference, Adelaide.
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Policy: Region versus largest city
Australia has unbalanced city size distributions: 2 very large cities, 3 large cities, no middle sized cities, lots of small cities. There is a “missing middle” of cities.
If larger cities drive disproportional agglomeration of high housing costs, planning must address this missing middle to take pressure off the largest cities.
Response: the balanced growth of smaller and medium sized cities that can provide similar opportunities as largest cities.
Complexity, Criticality, Computation, 2017Sarkar, S. and Searle, G., 2017, State of Australian Cities SOAC conference, Adelaide.
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Australia’s largest cities show polarisation and segregation in local housing and employment markets.
The larger a system gets, the higher the possibility for more spatial heterogeneities to arise.
If larger cities drive disproportional amounts of segregation, planning must address local segregation and local spatial inequalities in the largest cities.
Response: Having X number of affordable dwellings and Y number of people to be transported each day as targets is insufficient, their spatial distributions are critical.
Policy: Larger cities = more intra-urban inequalities
Complexity, Criticality, Computation, 2017Sarkar, S. and Searle, G., 2017, State of Australian Cities SOAC conference, Adelaide.
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● More research needed into how scaling and agglomeration works: focus on not just the economies of scale and increasing returns, but diseconomies of scale too!
● The role of regions and smaller and medium sized cities as important as the largest cities.
● As system size gets larger, heterogeneities arise naturally, leading to segregation. Planned response needed on spatial distributions of housing and employment.
Conclusions
Complexity, Criticality, Computation, 2017
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Questions?
Thank you!
Complexity, Criticality, Computation, 2017