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IS A QUANTITATIVE,
  PREDICTIVE, SCIENCE OF
CITIES CONCEIVEABLE? WHAT
CAN WE LEARN FROM PHYSICS
         & BIOLOGY?


               GEOFFREY WEST

             SANTA FE INSTITUTE

   SAID BUSINESS SCHOOL, OXFORD UNIVERSITY
WE LIVE IN AN EXPONENTIALLY
       EXPANDING SOCIO-ECONOMIC
         (URBANISED) UNIVERSE!!
1800    < 4% OF THE US POPULATION WAS URBAN

2011    > 80%

2006    > 50% WORLD’S POPULATION URBANISED

2050    > 75%

    EVERY WEEK FROM NOW TILL 2050
  OVER ONE MILLION PEOPLE ARE BEING
         ADDED TO OUR CITIES
SOCIO-ECONOMIC
  ENTROPY!!
NEED A SCIENCE OF CITIES

 “GRAND UNIFIED THEORY OF
     SUSTAINABILITY”

ARE THERE UNIVERSAL, QUANTIFIABLE
           PRINCIPLES?



COMPLEMENT TO TRADITIONAL (QUALITATIVE)
        THEORIES AND MODELS
ARE CITIES AND COMPANIES JUST
     VERY LARGE ORGANISMS
SATISFYING THE LAWS OF BIOLOGY?




   WHY DO ALL COMPANIES DIE
   WHEREAS ALMOST ALL CITIES
           SURVIVE?
•   METABOLISM

•   GROWTH

•   AGING/DEATH

•   EVOLUTION

•   SLEEP/REPAIR

•   DISEASE/CANCER
THE SEARCH FOR UNDERLYING LAWS
  AND PRINCIPLES LEADING TO A
     QUANTITATIVE PREDICTIVE
 (COARSE-GRAINED) CONCEPTUAL
          FRAMEWORK



 [ENERGY & RESOURCES (METABOLISM)
   vs. INFORMATION (GENOMICS)]
INTERSPECIFIC SIZE DISTRIBUTION
                               All species in a Malaysian Rainforest
                        3

                                                        1947

                       2.5
                                                   N =
log number of trunks




                                                  62 D-
                                                     1981
                                                  2.07
                        2                          N = 55
                                                   D-1.95
                       1.5




                        1




                       0.5
                                            1.5
                         0.5




                                                             2.5
                                   1




                                                    2




                                                                   3




                                       log trunk diameter (cm)
SCALABILITY

RESILIENCE
Mammals vary in size by
8 orders of magnitude




                     Shrew     Elephant
                          2g   2,000,000g




  Blue Whale
  200,000,000g
300

             240
Weight (g)



             180

             120

             60

              0
                   0   60   120    180   240   300   360
                                  Age (day)
SLOPE = ¾ < 1   SUB-LINEAR
EXTRAORDINARY SYSTEMATIC
ECONOMY OF SCALE (THE BIGGER
YOU ARE, THE LESS NEEDED PER
          “CAPITA”)

SIMILAR SCALING HOLDS TRUE
   FOR ALL PHYSIOLOGICAL
PROCESSES AND LIFE HISTORY
   EVENTS OVER THE ENTIRE
      SPECTRUM OF LIFE
Metabolic rate sets the pace of life
small animals live fast and die young




  heart rate scales as
  -1/4 power of body mass
NETWORKS!!!
Relation between number and
size of branches within a tree
INCOMING METABOLISED ENERGY

       
   MAINTENANCE
   (of existing cells)
       +
   GROWTH
   (of new cells)
BIOLOGY (LIFE)
a) DOMINATED BY NON-LINEAR (UNIVERSAL) 1/4 -
   POWER SCALING

b) EXTRAORDINARY ECONOMIES OF SCALE (THE
   BIGGER YOU ARE, THE LESS YOU NEED PER
   “CAPITA”)

c) PACE OF LIFE SYSTEMATICALLY SLOWS WITH
   INCREASING SIZE;

d) GROWTH IS SIGMOIDAL REACHING A STABLE
   SIZE AT MATURITY

e) NETWORKS
ARE CITIES (AND COMPANIES)
SCALED VERSIONS OF EACH
          OTHER?

    DO THEY MANIFEST
     “UNIVERSALITY”?
Kuhnert, Helbing & West, Physica A363, 96-103 (2003)
Example of scaling relationships

a) Total WAGES per MSA in 2004 for the
USA vs. metropolitan population.



b) SUPERCREATIVE employment per
MSA in 2003, for the USA vs. metropolitan
population.




        SUPER-LINEAR
          SCALING
Innovation measured by Patents
TOTAL CRIME (JAPAN)




 Slope = 1.21   [1.08, 1.35]
UNIVERSALITY
THE GOOD, THE BAD & THE UGLY

  DOUBLING THE SIZE OF A CITY
   SYSTEMATICALLY INCREASES
  INCOME, WEALTH, NUMBER OF
      PATENTS, NUMBER OF
 COLLEGES, NUMBER OF CREATIVE
PEOPLE, NUMBER OF POLICE, CRIME
   RATE, NUMBER OF AIDS & FLU
CASES, AMOUNT OF WASTE,………..
   ALL BY APPROXIMATELY 15%
      REGARDLESS OF CITY
NETWORK DYNAMICS DETERMINES
        THE PACE OF LIFE


IF THE SLOPE IS < 1 PACE OF LIFE SLOWS DOWN

IF THE SLOPE IS > 1 PACE OF LIFE SPEEDS UP
Pace of biological life vs.
       Pace of social life




Heart Rate vs. Walking Speed vs.
               Body Size
I. FINANCIAL MARKETS,
   ECONOMIES,
   GLOBAL WARMING,
   ENVIRONMENT,
   URBANISATION,
   HEALTH,
   CRIME,
   POLLUTION,……….

 ARE NOT INDEPENDENT

THEY ARE ALL HIGHLY COUPLED, INTER-
  RELATED COMPLEX ADAPTIVE SYSTEMS
UNIVERSALITY OF SOCIAL
NETWORKS (CLUSTERING
     HIERARCHIES)
II. ACCELERATION OF THREATS TO
SUSTAINABILITY HAS ORIGIN IN SUPER -
EXPONENTIAL URBANISATION
III. NEED A NEW PARADIGM, A NEW
INTEGRATED CONCEPTUAL FRAMEWORK:
SYSTEMIC, HOLISTIC, QUANTITATIVE,
PREDICTIVE
CITIES AND URBAN LIFE

a) SUPER-LINEAR DOMINATED BY INNOVATION &
              WEALTH CREATION

 THE BIGGER YOU ARE, THE MORE YOU GET PER
 CAPITA OF EVERYTHING FROM INCOME AND
 INNOVATION TO CRIME, POLLUTION AND
 DISEASE - ALL TO THE SAME DEGREE (~15%
 FOR EVERY DOUBLING OF SIZE)

b) SYSTEMATIC INCREASE OF PACE OF LIFE

c) UNBOUNDED GROWTH vs COLLAPSE
Growth Equation
N(t)




  N(0)

                               t




b<1   (SUB-LINEAR)   BOUNDED GROWTH
b >1      (SUPER-LINEAR)




SUPER-EXPONENTIAL
UNBOUNDED GROWTH    COLLAPSE
UNBOUNDED GROWTH
         N(t)                REQUIRES ACCELERATING CYCLES
                            OF INNOVATION TO AVOID COLLAPSE


         N(t)

 N3(0)                                    .
 N2(0)                              .
                N(t)
 N1(0)           .
N(t)
  N(0)
                                t               t
                       tc
UNBOUNDED GROWTH LEADING TO
“FINITE-TIME SINGULARITY” & COLLAPSE

UNLESS INNOVATIONS (SYSTEMATICALLY)
OCCCUR FASTER AND FASTER


CONTINUOUS TENSION BETWEEN:

INNOVATION & WEALTH CREATION vs
ECONOMIES OF SCALE
Population growth for New York City
            1790 - 2003
Successive cycles of superlinear innovation reset the singularity and postpone
instability and subsequent collapse. The relative population growth rate of New
York City over time reveals periods of accelerated (super-exponential) growth.
Successive shorter periods of super exponential growth appear, separated by
brief periods of deceleration. (Inset) tc for each of these periods vs. population
at the onset of the cycle. Observations are well fit with β = 1.09 (green line).
Average “idealised, universal”
characteristics of cities of a given size
(constrained by underlying principles and
dynamics of network structures -
manifested in scaling laws)

vs.

Characteristics of specific cities as
measured by deviations from scaling
laws representing their individuality and
local environment and conditions
2003 Patenting Rankings

5
         Cornwallis, OR (1)

4
                      SanJose (4)

3
                                                  Bosto (107)
                                                       n

2
                                                                               Pho i x (155)
                                                                                  en
1
0

-1                                                                                                 Den (206)
                                                                                                      ver

-2                                                                                                                                    NewY o (272)
                                                                                                                                            rk

-3

-4                                                                                                                                                   Abilen (359)
                                                                                                                                                           e


-5
     1
          14
                27
                      40
                              53
                                   66
                                        79
                                             92
                                                  10 5
                                                         11 8
                                                                13 1
                                                                       14 4
                                                                              15 7
                                                                                     17 0
                                                                                            18 3
                                                                                                   19 6
                                                                                                          20 9
                                                                                                                 22 2
                                                                                                                        23 5
                                                                                                                               24 8
                                                                                                                                       26 1
                                                                                                                                              27 4
                                                                                                                                                     28 7
                                                                                                                                                            30 0
                                                                                                                                                                   31 3
                                                                                                                                                                          32 6
                                                                                                                                                                                 33 9
                                                                                                                                                                                        35 2
Public sculptures cast in
                              metal are being substituted
                              with cheap plastic replicas to
                              prevent them being stolen by
                              thieves feeding the
                              voracious appetite for
                              commodities in China and
                              India.


   1 September 2011

The Historic Houses
Association said this
weekend that many stately
home owners were putting
antique garden statues into
storage and displaying
plastic copies instead.
OUR “NATURAL” METABOLIC RATE ~ 90 watts

OUR SOCIAL METABOLIC RATE ~ 11,000 watts !!!

WE ARE EQUIVALENT TO A 30,000 Kg GORILLA !!!

REPRODUCTION RATE OF ~ ONE OFFSPRING PER
                             15 years
JAMES BROWN (UNM/SFI)
  BRIAN ENQUIST (U. ARIZONA)
   WOODY WOODRUFF (LANL)
    VAN SAVAGE (HARVARD)
  JAMIE GILOOLLY (U. FLORIDA)
      DREW ALLEN (UCSB)
MICHELLE GIRVAN (U. MARYLAND)
     ALEX HERMAN (UCSF)
      CHRIS KEMPES (MIT)
LUIS BETTENCOURT (LOS ALAMOS/SFI - PHYSICS)
JOSE LOBO (CORNELL/ASU - URBAN ECONOMICS)
DEBORAH STRUMSKY (HARVARD/UNC - ECONOMICS)
HYEJIN YOUN (SFI)
MARCUS HAMILTON (SFI/UNM)
NATHANIEL RODRIGUEZ (SFI)

DIRK HELBING (ETH ZURICH - TRANSPORT/PHYSICS)
ERICH RAUCH (PHYSICS/BIOLOGY - MIT/PRINCETON)

DAVID LANE (U. REGGIO - STATISTICS/ECONOMICS)
SANDER van der LEEUW (PARIS/ASU - ANTHROPOLOGY)
DENISE PUMAIN (PARIS - URBAN GEOGRAPHY)
SPYROS SKOURAS (ECONOMICS - U. ATHENS)
PATRONS:


NATIONAL SCIENCE FOUNDATION

GENE & CLARE THAW CHARITABLE TRUST

BRYAN & JUNE ZWAN FOUNDATION

ROCKEFELLER FOUNDATION

McDONNELL FOUNDATION

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Geoffrey West Predictive Science of Cities Speed February 2012Atlas iedc san_antonio_geoffrey_west_slides

  • 1. IS A QUANTITATIVE, PREDICTIVE, SCIENCE OF CITIES CONCEIVEABLE? WHAT CAN WE LEARN FROM PHYSICS & BIOLOGY? GEOFFREY WEST SANTA FE INSTITUTE SAID BUSINESS SCHOOL, OXFORD UNIVERSITY
  • 2. WE LIVE IN AN EXPONENTIALLY EXPANDING SOCIO-ECONOMIC (URBANISED) UNIVERSE!! 1800 < 4% OF THE US POPULATION WAS URBAN 2011 > 80% 2006 > 50% WORLD’S POPULATION URBANISED 2050 > 75% EVERY WEEK FROM NOW TILL 2050 OVER ONE MILLION PEOPLE ARE BEING ADDED TO OUR CITIES
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. NEED A SCIENCE OF CITIES “GRAND UNIFIED THEORY OF SUSTAINABILITY” ARE THERE UNIVERSAL, QUANTIFIABLE PRINCIPLES? COMPLEMENT TO TRADITIONAL (QUALITATIVE) THEORIES AND MODELS
  • 14. ARE CITIES AND COMPANIES JUST VERY LARGE ORGANISMS SATISFYING THE LAWS OF BIOLOGY? WHY DO ALL COMPANIES DIE WHEREAS ALMOST ALL CITIES SURVIVE?
  • 15. METABOLISM • GROWTH • AGING/DEATH • EVOLUTION • SLEEP/REPAIR • DISEASE/CANCER
  • 16. THE SEARCH FOR UNDERLYING LAWS AND PRINCIPLES LEADING TO A QUANTITATIVE PREDICTIVE (COARSE-GRAINED) CONCEPTUAL FRAMEWORK [ENERGY & RESOURCES (METABOLISM) vs. INFORMATION (GENOMICS)]
  • 17.
  • 18.
  • 19.
  • 20.
  • 21. INTERSPECIFIC SIZE DISTRIBUTION All species in a Malaysian Rainforest 3 1947 2.5 N = log number of trunks 62 D- 1981 2.07 2 N = 55 D-1.95 1.5 1 0.5 1.5 0.5 2.5 1 2 3 log trunk diameter (cm)
  • 22.
  • 24. Mammals vary in size by 8 orders of magnitude Shrew Elephant 2g 2,000,000g Blue Whale 200,000,000g
  • 25.
  • 26. 300 240 Weight (g) 180 120 60 0 0 60 120 180 240 300 360 Age (day)
  • 27.
  • 28.
  • 29. SLOPE = ¾ < 1 SUB-LINEAR
  • 30. EXTRAORDINARY SYSTEMATIC ECONOMY OF SCALE (THE BIGGER YOU ARE, THE LESS NEEDED PER “CAPITA”) SIMILAR SCALING HOLDS TRUE FOR ALL PHYSIOLOGICAL PROCESSES AND LIFE HISTORY EVENTS OVER THE ENTIRE SPECTRUM OF LIFE
  • 31. Metabolic rate sets the pace of life small animals live fast and die young heart rate scales as -1/4 power of body mass
  • 33.
  • 34. Relation between number and size of branches within a tree
  • 35.
  • 36.
  • 37.
  • 38. INCOMING METABOLISED ENERGY  MAINTENANCE (of existing cells) + GROWTH (of new cells)
  • 39.
  • 40.
  • 41.
  • 42.
  • 43. BIOLOGY (LIFE) a) DOMINATED BY NON-LINEAR (UNIVERSAL) 1/4 - POWER SCALING b) EXTRAORDINARY ECONOMIES OF SCALE (THE BIGGER YOU ARE, THE LESS YOU NEED PER “CAPITA”) c) PACE OF LIFE SYSTEMATICALLY SLOWS WITH INCREASING SIZE; d) GROWTH IS SIGMOIDAL REACHING A STABLE SIZE AT MATURITY e) NETWORKS
  • 44. ARE CITIES (AND COMPANIES) SCALED VERSIONS OF EACH OTHER? DO THEY MANIFEST “UNIVERSALITY”?
  • 45.
  • 46.
  • 47. Kuhnert, Helbing & West, Physica A363, 96-103 (2003)
  • 48. Example of scaling relationships a) Total WAGES per MSA in 2004 for the USA vs. metropolitan population. b) SUPERCREATIVE employment per MSA in 2003, for the USA vs. metropolitan population. SUPER-LINEAR SCALING
  • 50. TOTAL CRIME (JAPAN) Slope = 1.21 [1.08, 1.35]
  • 51.
  • 53. THE GOOD, THE BAD & THE UGLY DOUBLING THE SIZE OF A CITY SYSTEMATICALLY INCREASES INCOME, WEALTH, NUMBER OF PATENTS, NUMBER OF COLLEGES, NUMBER OF CREATIVE PEOPLE, NUMBER OF POLICE, CRIME RATE, NUMBER OF AIDS & FLU CASES, AMOUNT OF WASTE,……….. ALL BY APPROXIMATELY 15% REGARDLESS OF CITY
  • 54. NETWORK DYNAMICS DETERMINES THE PACE OF LIFE IF THE SLOPE IS < 1 PACE OF LIFE SLOWS DOWN IF THE SLOPE IS > 1 PACE OF LIFE SPEEDS UP
  • 55. Pace of biological life vs. Pace of social life Heart Rate vs. Walking Speed vs. Body Size
  • 56.
  • 57. I. FINANCIAL MARKETS, ECONOMIES, GLOBAL WARMING, ENVIRONMENT, URBANISATION, HEALTH, CRIME, POLLUTION,………. ARE NOT INDEPENDENT THEY ARE ALL HIGHLY COUPLED, INTER- RELATED COMPLEX ADAPTIVE SYSTEMS
  • 58. UNIVERSALITY OF SOCIAL NETWORKS (CLUSTERING HIERARCHIES)
  • 59. II. ACCELERATION OF THREATS TO SUSTAINABILITY HAS ORIGIN IN SUPER - EXPONENTIAL URBANISATION
  • 60. III. NEED A NEW PARADIGM, A NEW INTEGRATED CONCEPTUAL FRAMEWORK: SYSTEMIC, HOLISTIC, QUANTITATIVE, PREDICTIVE
  • 61. CITIES AND URBAN LIFE a) SUPER-LINEAR DOMINATED BY INNOVATION & WEALTH CREATION THE BIGGER YOU ARE, THE MORE YOU GET PER CAPITA OF EVERYTHING FROM INCOME AND INNOVATION TO CRIME, POLLUTION AND DISEASE - ALL TO THE SAME DEGREE (~15% FOR EVERY DOUBLING OF SIZE) b) SYSTEMATIC INCREASE OF PACE OF LIFE c) UNBOUNDED GROWTH vs COLLAPSE
  • 63. N(t) N(0) t b<1 (SUB-LINEAR) BOUNDED GROWTH
  • 64. b >1 (SUPER-LINEAR) SUPER-EXPONENTIAL UNBOUNDED GROWTH COLLAPSE
  • 65. UNBOUNDED GROWTH N(t) REQUIRES ACCELERATING CYCLES OF INNOVATION TO AVOID COLLAPSE N(t) N3(0) . N2(0) . N(t) N1(0) . N(t) N(0) t t tc
  • 66. UNBOUNDED GROWTH LEADING TO “FINITE-TIME SINGULARITY” & COLLAPSE UNLESS INNOVATIONS (SYSTEMATICALLY) OCCCUR FASTER AND FASTER CONTINUOUS TENSION BETWEEN: INNOVATION & WEALTH CREATION vs ECONOMIES OF SCALE
  • 67. Population growth for New York City 1790 - 2003
  • 68. Successive cycles of superlinear innovation reset the singularity and postpone instability and subsequent collapse. The relative population growth rate of New York City over time reveals periods of accelerated (super-exponential) growth. Successive shorter periods of super exponential growth appear, separated by brief periods of deceleration. (Inset) tc for each of these periods vs. population at the onset of the cycle. Observations are well fit with β = 1.09 (green line).
  • 69. Average “idealised, universal” characteristics of cities of a given size (constrained by underlying principles and dynamics of network structures - manifested in scaling laws) vs. Characteristics of specific cities as measured by deviations from scaling laws representing their individuality and local environment and conditions
  • 70. 2003 Patenting Rankings 5 Cornwallis, OR (1) 4 SanJose (4) 3 Bosto (107) n 2 Pho i x (155) en 1 0 -1 Den (206) ver -2 NewY o (272) rk -3 -4 Abilen (359) e -5 1 14 27 40 53 66 79 92 10 5 11 8 13 1 14 4 15 7 17 0 18 3 19 6 20 9 22 2 23 5 24 8 26 1 27 4 28 7 30 0 31 3 32 6 33 9 35 2
  • 71.
  • 72.
  • 73.
  • 74. Public sculptures cast in metal are being substituted with cheap plastic replicas to prevent them being stolen by thieves feeding the voracious appetite for commodities in China and India. 1 September 2011 The Historic Houses Association said this weekend that many stately home owners were putting antique garden statues into storage and displaying plastic copies instead.
  • 75. OUR “NATURAL” METABOLIC RATE ~ 90 watts OUR SOCIAL METABOLIC RATE ~ 11,000 watts !!! WE ARE EQUIVALENT TO A 30,000 Kg GORILLA !!! REPRODUCTION RATE OF ~ ONE OFFSPRING PER 15 years
  • 76. JAMES BROWN (UNM/SFI) BRIAN ENQUIST (U. ARIZONA) WOODY WOODRUFF (LANL) VAN SAVAGE (HARVARD) JAMIE GILOOLLY (U. FLORIDA) DREW ALLEN (UCSB) MICHELLE GIRVAN (U. MARYLAND) ALEX HERMAN (UCSF) CHRIS KEMPES (MIT)
  • 77. LUIS BETTENCOURT (LOS ALAMOS/SFI - PHYSICS) JOSE LOBO (CORNELL/ASU - URBAN ECONOMICS) DEBORAH STRUMSKY (HARVARD/UNC - ECONOMICS) HYEJIN YOUN (SFI) MARCUS HAMILTON (SFI/UNM) NATHANIEL RODRIGUEZ (SFI) DIRK HELBING (ETH ZURICH - TRANSPORT/PHYSICS) ERICH RAUCH (PHYSICS/BIOLOGY - MIT/PRINCETON) DAVID LANE (U. REGGIO - STATISTICS/ECONOMICS) SANDER van der LEEUW (PARIS/ASU - ANTHROPOLOGY) DENISE PUMAIN (PARIS - URBAN GEOGRAPHY) SPYROS SKOURAS (ECONOMICS - U. ATHENS)
  • 78. PATRONS: NATIONAL SCIENCE FOUNDATION GENE & CLARE THAW CHARITABLE TRUST BRYAN & JUNE ZWAN FOUNDATION ROCKEFELLER FOUNDATION McDONNELL FOUNDATION

Hinweis der Redaktion

  1. Emphasis on tot inc rate