Multilevel comparison of large urban systems

9.5 MB·English

by  Denise Pumain

Multilevel comparison of large urban systems
Denise Pumain∗, Elfie Swerts, Cl´ementine Cottineau, C´eline Vacchiani-Marcuzzo, Antonio
Ignazzi, Anne Bretagnolle, Franc¸ois Delisle, Robin Cura, Liliane Lizzi and Sol`ene Baffi
G´eographie-Cit´es, CNRS-Paris 1-Paris 7 (UMR 8504), 13 rue du four, FR-75006 Paris, France
Forthefirsttimethesystemsofcitiesinsevencountriesorregionsamongthelargestintheworld
(China,India,Brazil,Europe,theFormerSovietUnion(FSU),theUnitedStatesandSouthAfrica)
aremadecomparablethroughthebuildingofspatio-temporalstandardisedstatisticaldatabases. We
firstexplaintheconceptofagenericevolutionaryurbanunit(“city”)anditsnecessaryadaptations
to the information provided by each national statistical system. Second, the hierarchical structure
5
and the urban growth process are compared at macro-scale for the seven countries with reference
1
to Zipf’s and Gibrat’s model: in agreement with an evolutionary theory of urban systems, large
0
similarities shape the hierarchical structure and growth processes in BRICS countries as well as in
2
Europe and United States, despite their positions at different stages in the urban transition that

n explain some structural peculiarities. Third, the individual trajectories of some 10,000 cities are
a mappedatmicro-scalefollowingaclusteranalysisoftheirevolutionoverthelastfiftyyears. Afew
J commonprinciplesextractedfromtheevolutionarytheoryofurbansystemscanexplainthediversity

6 ofthesetrajectories,includingaspecificpatternintheirgeographicalrepartitionintheChinesecase.
2 We conclude that the observations at macro-level when summarized as stylised facts can help in
designingsimulationmodelsofurbansystemswhereastheurbantrajectoriesidentifiedatmicro-level

] are consistent enough for constituting the basis of plausible future population projections.
h
p
-
c isons,wedefineacityasaplaceinwhichthedailyactiv-
o ities of most residents are concentrated. Its delineation
s constitutes a spatial ”envelope” that evolves through
. Urbantheoriesandmodelsaretoorarelytestedonsets
s time,generallyinexpansion. Foreachtimeperiod,wese-
c ofdatathatareproperlydefinedandstandardized. Many
lectanurbandelineationthatissuitedtothelocalregime
i contradictoryresultsandsomecontroversialpapersinur-
s ofsocio-spatialinteraction: thus,beforethe19thcentury,
y ban studies can be explained by a lack of attention paid
administrative units (communes, municipios, or places)
h to the quality and quantity of empirical data. We think
are sufficient to define ”cities without suburbs” (see [1],
p ofcrucialimportancetoestablishsolidandreplicablere-
[ sultsfromsounddatathataremadecomparablebyusing p.291) that represent a dense body of population having
acommontheoreticalbackgroundfordefininganddelin- requested and/or having been granted legal recognition
1
by the political power. The form taken on by this recog-
v eatingcities,whatevertheheterogeneityofthepublished
nition and the conditions depend on the political and
8 statistical information. We take here the opportunity of
0 several coordinated PhD works[32] for comparing urban institutional setting (for instance, request to be incorpo-
4 systemsinsevenamongthelargestcountriesintheworld: rated into the United States, or demographic threshold
6 inEurope,orpoliticaldecisionasobservedmorerecently
China, India, Brazil, Europe, the Former Soviet Union
0 in the USSR or China). With the industrial revolution,
(FSU),theUnitedStatesandSouthAfrica. Thissample
.
1 includes all the so-called BRICS countries that were for the city became a dense human group extending out-
0 side administrative boundaries, and it then requires a
awhilethemostrapidlygrowingcountriesandillustrate
5 morphological definition (i.e. urban agglomeration) so
urbansystemsinalmostallcontinentsatdifferentstages
1 as to reflect this continuity of built-up area. More re-
of the urbanization process.
:
v cently,interactionslinkedtotheuseofcartransportand
i long-distance commuting by people living in peri-urban
X
settings but still working in the city centres led on to a
r Standardized databases for comparing urban functional description of the city (i.e. functional urban
a
systems according to an evolutionary concept of area) [6].
cities
The present development of urban databases for the
sevenstudyzonesisbasedoncommonprinciples,whileat
Harmonised databases derived from an evolving con-
thesametimeallowingforadaptationtolocalconstraints
cept of the city are a prerequisite for comparative urban
and political and administrative contexts[33]. Wherever
studies. In our perspective of spatio-temporal compar-
possible, we have used population numbers derived from
thefinestpossibleadministrativelevel(thebasicunitsin
the databases) and we performed aggregations of these
∗Correspondenceandrequestsformaterialsshouldbeaddressedto elementaryentitiesinordertofollowtheevolutionofthe
DP(Email: [email protected]). urban object in the seven selected countries over long
2
time spans (see Figure 1). Forms of urban hierarchy and growth processes
Table I sums up the final content of these databases There is a vast corpus of literature about the forms
and the method used to adapt our generic definition to taken on by urban hierarchies, and in particular the de-
the information available in each country. Few of these scriptions that refer to Zipf’s rank-size rule. Recent syn-
countriesprovidedatabasedonafunctionaldefinitionof theses [20, 26] have not reached a consensus on the uni-
the urban object, and the information is available only versal nature of this rule, nor on the factors that might
for certain dates (USA, Brazil). The database for Eu- explainitswideapplicability,orthemainreasonsforthe
rope, which covers a longer historical time span, groups variations that are observed.
entities according to a morphological definition. Certain A first obstacle to the construction of scientific knowl-
countries only give information at administrative level, edgeonthisissueistheextremeheterogeneityofthesam-
fortunately in fairly wide units that are able to evolve ples of cities that have been used to perform the tests,
(Russia: [9]). Different aggregation methods have been andsometimesthedoubtfulqualityofthedefinitionsand
implemented to harmonise these databases: morpholog- delineations used to measure city size. We do not claim
ical aggregation on the basis of aerial photographs or heretoprovideafinalsolutiontothisproblem,butwedo
satellite images (Europe, India, FSU); for South Africa, bringmorecredibleresultsbyusingdatabasesthatareas
since the morphological definition lacks relevance in the comparable as possible, applying this to a large number
case of the Apartheid city, entities that were function- of cities over fairly long time spans and for a variety of
ally linked to the city, such as the black townships, were large countries of the world.
integrated [29] ; the database for the USA uses an evolv- Another question is that of the definition of the city
ing definition of the city, considering legal entities alone systems observed. Most often, Zipf’s law is tested on a
until the change to urban agglomerations in 1870 and set of cities in a single country. We are aware that this
finally metropolitan areas (which are functional areas) use of the nation-state framework (or a quasi continen-
from 1940 [5]; For China, the principle of urban agglom- tal framework in the case of Europe, US, China, India
eration was applied, adjusting delineations of built-up andtheformerSovietUnion)isprobablynolongercom-
areas collected on satellite images on the administrative pletely suited to the delineation of city systems, since
grid of the qu, shi and xian [27]. the globalisation of exchanges brings cities to new inter-
actions, the intensity and range of which vary according
to city size. Nevertheless, urban hierarchical patterns,
precisely because of the growth processes resulting from
their interaction patterns, tend to be sustained over pe-
The urban entities included in table 1 are those with
riodsofseveraldecadesorevenseveralcenturies, andwe
more than 10,000 inhabitants at the date indicated. At
think that the hierarchies observed here have been en-
the start of the 21st century, the numbers can vary from
gaged in strong interactions for sufficiently long time for
a few hundred to several thousands according to the
the patterns to continue to show up, even if the state
country, but they remain fairly closely linked to the to-
borders enclosing them are no longer as impermeable as
tal urban population of the country, as shown in Figure
they were earlier.
2, where a simple linear regression adequately fits three
quartersofthevariationsofthesetwovalues. Eachfigure
isforeseeableiftheotherisknown,whichmeansthatthe
Macro-level analysis of urban hierarchies explained
degree of concentration of the urban population varies
by territorial history
little in the present-day world (a very approximate mea-
sure of the present degree of concentration in these large
Our main results widely confirm the results reported
countries can be obtained by calculating an average size
byMoriconi-Ebrard[18]whousedtheGeopolisdatabase
per city, about 100,000 inhabitants, which has of course
tocomparestatesacrosstheworld. Theyalsogoagainst
no real geographical meaning) and this reflects a certain
theideaofahistoricalconvergencetowardsaregularZipf
coherenceinurbanisationprocessesoverall,whateverthe
model with a slope of -1 as suggested in a recent article
country considered. The United States appear as an ex-
by Berry and Okulicz-Kozaryn [4].
ception, with far less urban units than expected from
their total urban population. That huge concentration Indeed,slopevaluesforrank-sizeadjustedonthesedis-
of the urban system can be explained by the historical tributions vary, clearly differentiating countries and con-
process of settlement in the “New world” as well as by tinents according to how long-standing their settlement
the very large size of the elementary spatial units aggre- is. The value of the rank-size slope is an index of size
gated in SMA’s (the counties). Even if we had included inequalities of cities. The variations of this index of ur-
in the database the urban agglomerations instead of the ban size amplitude in a given territory can be fairly well
SMA’s,theexceptionwouldremain,sincethereare1380 explained by differences in the speed of transport sys-
“urbanized areas and urban clusters” over 10 000 inhab- tems enabling exchanges among cities, at the time when
itants for a total of 237 million urban population (US the urban networks become established: the lowest val-
Census Bureau, 2014)[34]. ues (absolute value, i.e. without sign) are observed in
3
S
R
N
Brazil és C
China cit
e-
Europe* hi
p
Former Soviet Union gra
o
India é
G
South Africa MR
U
United States y -
Cit
er
v
Di
o
e
G
1300 1800 1850 1900 1950 2000 ©
* European continent, Turkey and the former Soviet Union excepted
FIG. 1: Historical range of the urban data bases.
Database N (initial date) N (final date) D Method
0 F
Brazil (a) 531 (1872) 2615 (2010) 11 Administrative (municipios + metropolitan areas)
Former Soviet Union Morphological (municipalities at three administrative
91 (1840) 1929 (2010) 11
(DARIUS) (b) levels)
India (IndiaCities)
503 (1901) 5841 (2011) 12 Combined morphological, and functional
(c)
Combined administrative (xian, qu, xianjishi (district
China (ChinaCities)
605 (1964) 9294 (2000) 4 level) and zhen, xiang (subdistrict level)) morphological
(d)
and functional
South Africa Combined morphological and functional (white cities +
14 (1911) 220 (2001) 10
(DYSTURB) (e) black townships)
Europe (PARIS-
Bairoch-Geopolis) 3619 (1950) 4413 (2010) 14 Morphological (municipalities then agglomerations)
(f)
Combined administrative then morphological and
United States
5 (1790) 909 (2010) 23 functional (places, cities, retropolated micropolitan and
(Harmonie-cit´es) (g)
metropolitan areas)
TABLE I: Harmonised urban databases for international comparisons (urban units > 10,000 inhab.). N :
0
number of cities at (initial date) ; N : Number of cities at (final date) ; D : number of dates considered in the database.
F
Sources : (a) IBGE, Instituto Brasileiro de Geografia e Estatistica. Statistical tables for the Brazilian Empire and the Federal
RepublicofBrazil;(b)StatisticaltablesfortheRussianEmpire,RussianEmpirecensuses,USSRcensuses(archives),National
censuses;(c)CensusofIndia;(d)ChinaDataCenterandChineseNationalCensusBureau;(e)StatisticsSouthAfrica,South
AfricaninstituteofRacerelations,Urbanfoundation,CensusStatisticsSA,DYSTURB(see[15]);(f)[1,19];(g)USAcensus
data: Census of Population and Housing.
countries that have been populated for a long time (In- the politico-administrative organisation of the territories
dia, China, Europe) and the highest values for countries concerned (see Figure 3). In countries that have been
that were settled more recently, higher transportation run under socialist regimes aiming at restraining urban
speed enabling a wider spacing between settlements as growth, there is a levelling-off of the curves (in the FSU
well as larger urban concentrations emerging on sparser aroundonemillioninhabitants,inChinaaround100000
spatial distributions of rural population (South Africa, inhabitants, corresponding to city sizes for which tar-
the United States). The fairly high values for the Soviet geted investments have been made [8, 18]. Conversely
Union could be explained by its relatively late industri- thecountrieswiththemostmarkedmacrocephaly(South
alisation compared to Europe, and a more recent urban Africa, India, Brazil) are those that have allowed their
developmentofitsEasternareasintheAsiaticpart,while metropolises the greatest latitude.
Brazil escapes the general pattern with a moderate de- Compared to the United States and Europe, the
gree of urban concentration (see Table II). BRICScountriesstandoutfortheparticularshapeofthe
The qualitative variations in shape of the size dis- upper part of their urban hierarchies: Russia is the only
tribution are explained above all by the diversity of BRICScountrytopresentacaseofurbanprimacy,where
4
Country N a P /P M P (×106)
1 2 tot
Brazil 2615 0.88 2 2 161
FSU 1929 1.10 3 0 173.5
India 5121 0.95 1.1 3 427
China 9294 0.80 1.3 0 481
South Africa 220 1.15 2 4 25
Europe 4413 0.96 1.2 2 291
United States 909 1.23 1.5 0 287
TABLEII:Comparing urban hierarchies: city size distributions around 2010. Weconsidertheurbanagglomerations
larger than 10000 inhabitants. Rank size slope a is estimated from equation log(P) = K −alog(R) with P the population
of the city and R its rank, using OLS method. P /P : primacy index; M: number of macrocephalic cities; P : total urban
1 2 tot
population.
10000 NRS 100000 NRS
8000 CFEBourharrimzonipaleer Soviet Union graphie-cités C 10000 CBhrainzail Géographie-cités C
er of cities 6000 IUSnonduiitatehd A Sftraictaes DiverCity - UMR Géo n (thousands) 1000 FormerU SSnooivtuieetdhtE USuAItrnnfaoridtiopceineaas © GeoDiverCity - UMR
b eo o
m 4000 © G ati
u ul
N p
o
P
100
2000
0
10
0 100 200 300 400 500
1 10 100 1000 10000 100000
Total urban population (millions) Rank
FIG. 2: Total urban population and number of cities FIG. 3: City size distributions in seven countries
per country. around 2010.
Moscow is three times the size of St Petersburg (see Ta- also ranks first for the number of cities of over a million
bleII)whilealltheothersexceptChinaarecharacterised inhabitants, 66 in all. In contrast, India, ranking sec-
by macrocephaly comprising two to four cities that are ond for the weight of its total urban population (around
clearly discontinuous with the rest of the distribution. 400 million) ranks only 4th for the number of cities with
Thus in India, the three cities of Delhi, Mumbai and more than a million inhabitants – with 44 of these, it is
Kolkata, with around 16 million inhabitants each, stand outranked by the USA which has 51 for an urban popu-
out from the other cities in India, as do Durban, Cape- lation of only 287 million, which is therefore much more
town and Johannesburg agglomeration in South Africa, concentrated spatially. In contrast again, Europe where
whileinBrazilthediscontinuityislessmarked, withSao the urban population is more or less the same than in
Paulo (20 million inhabitants) and Rio de Janeiro (12 the USA (291 million) has only 39 cities of over a mil-
million)somewayaheadofBeloHorizontewithjustover lioninhabitants,andthedegreeofconcentrationisfairly
5 million. similar to that of the former Soviet Union (173.5 million
The largest cities are remarkable by their size, which city-dwellers and 28 cities of over a million), or Brazil
obviously depends on the mass of the urban population (161 and 23 respectively). While Europe remains the
in the country (see Figure 2), but this top part of the continentofsmalltomediumcitiesandtowns,andwhile
urban hierarchy is not sufficient to characterise the de- India stillhas many small cities, the urbanprocesses un-
gree of concentration of an urban population, and it is derway in India suggest that many of these cities are set
relevant to look at the other parts of the distribution. to expand in the coming decades [28], and in China, al-
Thus China, which ranks first for its mass, with a to- though population concentration is still moderate, the
tal urban population of around 500 million inhabitants, massive size of the country and the closeness of certain
5
cities one to the other suggests that several large conur- monfeaturesdespitemajordifferencesintheirhistoryas
bations or megalopolises are likely to develop with 30 wellasterritorialandpoliticalorganization. Thatiswhy
to 40 million inhabitants each, around the Pearl River Zipf’s law and Gibrat’s model albeit purely statistical
Delta,intheregionsroundShanghai,orbetweenBeijing models remain rather good standard references enabling
and Tianjin. international comparisons for a synthetic description of
empirical urban hierarchies and urban growth processes,
even though they do not directly provide an explanation
Urban hierarchies and urban growth at micro-level: for the underlying generative geographical processes (we
testing Gibrat’s model suggestfurthertheoreticalcomplementaryinvestigations
inthisdirection, forinstancebydevelopingthefamilyof
Simpop models, [10, 22]. This conclusion is consolidated
The universal shape of urban hierarchies that can be
by the consistency of deviations from the models that
summarized by Zipf’s rank-size rule as above or by a
can be rather easily related to different families of the
lognormal statistical distribution is explained as a first
historicaldevelopmentofurbansettlementsandpolitico-
approximation by a stochastic repartition of the growth
administrativeorganisationoftheterritoriestheybelong
rates of individual cities in an urban system [21, 24].
to.
Gibrat’s law [14] predicts the statistical form of urban
hierarchies and their persistence over time. This model
assumesthatcitiesgrowinamannerthatisproportional
Macro and micro-dynamics: urban transitions and
to their size. It is a growth model of the exponential
cities trajectories
type, but with growth rates (or relative population vari-
ations)thatvaryinthecourseoftime. Accordingtothis
As we have seen before, understanding city size dis-
model, despite considerable fluctuations in growth rates
tribution and moreover urban growth processes cannot
fromonecitytoanotherovershorttimespans,long-term
be done without referring to the history of each ur-
growth averages out at the same level for all cities in a
ban system and at least, even in a parsimonious ab-
given system.
stract approach, to its stage in the seemingly univer-
WetestedthehypothesesofGibrat’slawfortheinter-
sal urban transition. This process named by W. Zelin-
vals between these two dates for which city population
sky [31] using an analogy with the demographic tran-
data is known in our bases. The hypothesis for generat-
sition describes the universal change from a dispersed
ingalognormaldistributionstipulatesthatthevariations
andhomogeneousspatialrepartitionofpopulationinru-
in growth rate at each time interval do not depend on
ral habitat towards much more concentrated diversified
city size, and are distributed randomly from one period
and hierarchized forms in urban settlements. The pro-
to another. Overall, the process observed in the BRICS
cess which generally accompanies economic development
complies with the model, which thus, at least in first ap-
started roughly at the beginning of 19th century in first
proximation, remains a relevant reference for analysing
industrialized countries and around 1950 in the less de-
the process of growth distribution in the city systems.
veloped ones.
Everywhere, the correlation between city size and
growth is low or absent. In South Africa, in India and
China, whether at national level or for the main regions,
Macro-level trajectories of urban systems
thehypothesesoftheGibratmodelareverified,andfrom
the start of the 20th century [28, 29]. There is a discrep-
Different stages in urban transition clearly appear al-
ancy with the model, particularly in periods of vigorous
readyfromthecondensedinformationinTableIIIwhere
growth, for the USA throughout the 19th century and
countries that experimented earlier urban transitions
in Europe after 1950, where there is a positive tempo-
(Europe, Former Soviet Union, USA, Brazil) have much
ral autocorrelation of the growth rates. In Russia the
lower average urban growth rates during the last forty
process also appears in the course of the second phase
yearsthancountriesthatarestillintheexponentialstage
of industrialisation in the 1930s and in the two decades
of the growing curve of their urbanization rate.
during which there was a trend towards metropolisation
China appears clearly as the country in which recent
of the largest cities and a cumulative decline of certain
urban development is the most rapid, with an average
specialisedcities[9]. Wehavedemonstratedinaprevious
urban population growth rate of over 5% per year over
paper [13] how such deviations from a purely stochastic
40years. TheIndianurbangrowthrateishalfofthis,but
growthmodelcouldbeexplainedbyintegratinginanur-
nevertheless vigorous during the period, with an average
ban growth model the interaction processes that convey
rate of 2% per year, while South Africa, with 3.2%, still
innovationwavesintheurbanhierarchies. Aconsequence
reflects the fast urban growth rates across the African
is that inequalities in city sizes are growing faster in real
continent. In the USA and in Brazil growth is still over
urban systems than according to a pure Gibrat’s rule.
1%, while in Europe and Russia it is just 1% or much
To conclude, the urban hierarchies and growth pro-
lower.
cesses in a variety of large urban systems all over the
world,includingtheBRICScountries,sharegenericcom- These trends are partly determined by the stage
6
Country Average growth rate (%/year) Period
Brazil 1.11 1960-2010
China 5.20 1964-2000
India 2.10 1961-2001
Former Soviet Union 0.78 1959-2010
South Africa 3.15 1960-2001
Europe 1.01 1960-2010
USA 1.52 1960-2010
TABLE III: Average annual growth rates of population for cities of over 10.000 inhabitants during second half
of the 20th century.
reachedbythedifferentcountriesintheurbantransition, 100
S
buttheyarealsoinfluencedbytheirparticularhistoryof NR
C
urbanisation: inChinapoliticalaction,inparticularthat és
apffloescitoinngomf uigrrbaatnioisnast,iofonr[a7l,o1n7g],tifmarepmuotrebrmakaerskoednltyhteheaxn- %) phie-cit
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internal migrations from the former Bantustans, and on of op erCit
tm”hnieegwroa”tnhcteosru[1nb1ter,cie2as9u]is.neBtoerframiztissloaafntwtdraatvchetesivoUefnSseeAststbletemoinweganrtadmhsaodfnoagreftioghrne- ation ban p © GeoDiv
ulur
mer urbanisation rate systematically higher than in ”old pd
world”countriesinEuropeandAsia[25]. Theslowingin Porl 1
o
urbangrowthrateshasbeenmoremarkedinRussiasince
w
the1990s,asaresultofdecreasesinthetotalpopulation,
especiallyintheNorthernpartsoftheterritoryresulting
Brazil China Europe
from the dismantling of the Soviet Union [12, 16]. It has
even been negative on average over the last two censuses Former Soviet Union India
(since 1989).
South Africa United States
0.1
As a result, the weights of urban population of these
1950 1980 2000
countriesinthetotalurbanpopulationoftheworldhave
beenrelativelyincreasingasshowninFigure4thatcom- Date
pares their evolution over almost half a century.
These contrasted evolutions explain the very rapid
turnover in rankings among the mega-cities (above 10 FIG.4: Comparing the evolution of countries share of
millions inhabitants), which are always the subject of world urban population (%).
controversy because the delineations chosen to measure
these large urban areas have a considerable impact on
their rank[35]. We can consider here the subset of (14),andRio(12). TheEuropeancitiesdonotexceed10
these seven large countries included in the harmonised million, and they are joined in this megapolis group by
database to demonstrate the upheavals caused by un- Guanzhou, Shenzen, and probably a few other Chinese
equal demographic growth over the last four decades. In cities if the migrant populations with more or less illegal
the1960s,thethreelargestcitiesbysizewerelocatedout- status are taken into account.
sidetheBRICS,withpopulationsof10.6millionforNew
York, 8.9 for Greater London, and 7.2 for Paris. These
were followed on by Moscow (7.2), Shanghai (6.4), Delhi Differentiating urban trajectories at micro-level
(5.9),LosAngeles(6),Kolkata(5.3),Mumbai(4.9),Bei-
jing (4), Sao Paulo (with 3.8 having just overtaken Rio Too often however in urban studies the focus is only
at 3.3) and Guanzhou (2). By the end of 20th century, directed on these few “global cities” or “world cities”
this ranking had been completely overturned, with Sao whosesizeandexpansion islinkedwith thedevelopment
Paulo joining or overtaking New York with some 20 mil- of long distance exchange networks but not necessarily
lion inhabitants, and 4 cities in the BRICS taking top reflectotherpossibletypesofurbandynamics. Wethink
places among the others: Delhi (17 million), Shanghai necessary to develop knowledge about perhaps less pres-
(16), Kolkata (16), Mumbai (15), Moscow (14), Beijing tigiousnamesinurbanhierarchiesthatneverthelesspro-
7
videwaysoflivingforamajorityofpopulationsandalso tions–certainStatecapitalsinIndia, provincialcapitals
participate in a decisive manner to the maintenance and in China regional capitals in Russia, and medium-sized
renewal of urban systems. cities in South Africa.
To differentiate city trajectories in each country we
developed a method that compares population evolution
profilesbywayofacorrespondencefactoranalysisanda In Brazil (Figure 5) almost all the large metropolitan
hierarchicalascendingclassificationusingtheχ2 distance areas, which are the capitals of the federal States, have
(seeAppendice1). Theimplicitreferencemodelisthere- stronglyascendingtrajectories. Therecentdynamicthus
forethatofproportionaldistributedgrowthproposedby hastheeffectofaccentuatingthehierarchicalinequalities
Gibrat [14], and the trajectories show the most system- in the country. India too exhibits this process of rein-
atic discrepancies in relation to mean growth trend. The forcement at the top of the urban hierarchy, with three
types of profile derived from the classification are shown quarters of the largest cities exhibiting ascending or sta-
on the left-hand graph (see Figures 5, 6, 7, 8, and 9) by bletrajectories. Incontrast, intheothercountries, what
the trajectory of the mean population for each type, and canbeseenisaformof”catchingup”bythesmallercities
to the right for a trajectory showing the relative evolu- and peripheral areas. The markedly ascending trajecto-
tion of the mean weight of this type of city in the urban riestendtobecharacteristicofafewsmallercities,often
systemunderconsideration(semi-logarithmicgraphsen- in the vicinity of the large metropolises, in India (Fig-
ablingontheonehandacomparisonofgrowthintensities ure 7) or South Africa (Figure 9). In the former Soviet
representedbytheslopes,andontheotherimmediatevi- Union (Figure 6), it is most of the cities located on the
sualisationofanydifferencesinevolution(theyarefairly peripheries of Russia to the South (Central Asia, Azer-
frequent) associated with positions in the urban hierar- baijan) and West (Ukraine, Belarus), and some Russian
chy). cities near deposits of mineral resources, that gain weigh
Naturally, the shape of city trajectories in relation to relativetotheothers. InChina(Figure8),twoprocesses
that of the system to which they belong can vary with are seen, where the large cities in the East (Shenzen, Xi-
the period considered. We chose the period 1960-2010 amen) are gaining weight in the system while medium-
for reasons of comparability between countries, and also sized cities in Xinjian province and Inner Mongolia are
to restrict the number of major historical turning points developing fast, illustrating the catching-up by periph-
which if too numerous would make it more difficult to eral regions.
efficiently anticipate possible evolutions in the 21st cen-
tury. By construction, these classifications show classes
of cities where the ”absolute” evolutions are often all in- There is a long-term trend in most city systems
creasing (rarely including an inflexion), but grouped ac- whereby it is mostly the small urban entities that show
cording to their relative growth, which may be faster or relative decline. Indeed, all other things being equal, the
slower than that of the country as a whole. To compare smallercitiesaremorelikelytobedistantfromthemain
the degree of heterogeneity of these trajectories across waves of innovation, or else to be highly specialised in
countries, we measured the share of variance remaining declining sectors of activity, so that they lose their influ-
between classes. The order of the countries remains the ence on local markets as a result of acceleration in the
same, whether the partition is into two, three or five speed and capacity of transport systems. The only ex-
classes. In the case of five classes, the Former Soviet ception appears in China (see Figure 8) where classes
Union shows the greatest diversity in trajectories (76% of cities with an ascending profile are made up mainly of
interclass variance) while in Brazil the trajectories ap- smallcities,athirdofwhichinSpecialEconomicZonesin
pear less differentiated (63%), the other countries falling whichinnovatingactivitieshavebeensetupandtowhich
between these two (70-73%). populations migrate. The classic process of hierarchical
Depending on the form of the classification tree, there diffusionofinnovationispartiallydisconnectedherefrom
aretwoclearlydistincttypesoftrajectoryinChina,while the previous structure of the city system. However cities
three families of trajectory can be observed in the other with a ”winning” trajectory are mainly located in the
countries. To obtain more detail, and in relation to lev- immediate vicinity (roughly less than 200 km) of large
els of heterogeneity, for mapping purposes (Figures 5,6, metropolitanareas,forinstanceGuangzhouorShanghai.
7, 8, 9) we retained four classes of city for China (Figure Theimportanceofpolicyinurbandynamicscanthusbe
8),India(Figure7),SouthAfrica(Figure9)andthefor- seen in the creation of new cities, at the same time pre-
mer Soviet Union (Figure 6), and five for Brazil (Figure serving a degree of spatial and historical coherence with
5). The classes can be grouped according to the orien- theearliertrendsinthecitysystem. Inaterritorywhere
tation of their trajectories in relation to the city system urbanisation is long-standing, like China, these new ur-
to which they belong, generally in two types, ”winners” ban developments fit themselves into the previous urban
and ”losers”, but a stable type also appears in India, spatial pattern, while in ”new” countries like Brazil, the
China,theformerSovietUnionandSouthAfrica. These USA and South Africa, and also the Eastern part of the
cities that maintain their relative weight in the system Russian Empire, urban creations ran alongside the set-
are often long-standing cities with administrative func- tlement of new territories.
8
Conclusion evolutions are disseminated in all parts of each territory
with few remarkable spatial concentrations.
Whendealingwithcomplexsystems,itisimportantto
relatetheconfigurationsofurbanhierarchiesobservedon
macro-geographical scale of States to the trajectories of
Appendice 1. TrajPop software (author:Robin Cura)
the urban entities they comprise on micro-geographical
level. This paper thus demonstrates the usefulness of
constructingaharmoniseddatabaseenablingthedescrip- TheseanalysesareperformedusingtheTrajPopscript,
tion of the evolution of urban entities and their spatial developed in the ERC GeoDiverCity project. This tool,
extension over time. based on the free statistical environment R, performs a
We provide for the first time a comparable overview CorrespondenceAnalysisonatemporalpopulationtable.
of the systems of cities in the five BRICS countries. Us- The coordinates of cities on the orthogonal components
ing Zipf’s distribution of city sizes and Gibrat’s urban then make it possible, after re-entering the weights of
growth models as benchmarks for the comparison, we the cities, to generate a matrix for population discrep-
have demonstrated that the dynamic urban processes in ancies among cities (measured using a khi2 distance);
BRICS during the last fifty years were rather similar to this matrix is applied a Hierarchical Cluster Analy-
to those observed for instance in Europe or the United sis (using the Ward method, which tends to minimise
States. There is nothing resembling a specific urban dy- intra-classvarianceandtomaximiseinter-classvariance).
namicinBRICSwhetherweconsidertheshapeofurban From the tree generated by this clustering, the number
hierarchies,citysizedistribution,ordistributionofurban of clusters is chosen so that it sufficiently distinguishes
growth among individual cities. the trajectories while at the same time enables them to
Of course differences do exist, but they relate to the be mapped. It is then possible to analyse trajectory
specific developmental pathway of these countries, in- classes using the TrajPop graphic and numerical print-
cluding the relative delay in the urban transition com- outs,forinstancebystudyingtheevolutionoftherelative
pared to more developed countries, which explains their weights of the classes in the system in the course of time
very high mean urban growth rates – the case of Rus- (http://trajpop.parisgeo.cnrs.fr).
sia being excepted. History matters too for differentiat-
ing the evolution of urbanisation rates, which registered
higher values earlier in Russia and Brazil compared to
Acknowledgements
South Africa, China and India.
Whenshiftingfromthemacro-scaleofcountriestothe
micro-scale of individual cities, the most striking fact The authors acknowledge funding from the European
is the diversity of urban trajectories that exhibit con- Research Council through project GeoDiverCity (ad-
trasted patterns of booming growth or relative decline vanced grant programme, number 269826, PI Denise
everywhere. Moreover,thesequalitativelydivergentlocal Pumain).
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10
TRAJECTORIES OF 1800 CITIES FROM 1960 TO 2010 IN BRAZIL
Population in 2010 (millions)
20
1
0 500 Km
Sources
IBGE, Censos Demogràficos 1872-2010
Made with Philcarto http://philcarto.free.fr
© Cosmo Antonio Ignazzi, Géographie-cités - GeoDiverCity, 2013
CLUSTERING TREE AND FACTORIAL MAP F1 (x) : 79%
Continuous trend F2 (y) : 12%
Classes
A : Fast growing cities (138)
E
Slow growth Fast growth
B : Growing cities (297) D B A
C
C : Slow growing cities (758)
D : Declining cities (451)
E : Fast declining cities (156)
ABSOLUTE EVOLUTION RELATIVE EVOLUTION
s) 1000 an 0.5
d b
an ur 0.4
us al %)
on (tho 100 on / toton (in 0.3
ati atiati 0.2
ul ulul
p pp
po popo 0.1
n n
Mea 10 Mea 0
1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010
FIG. 5: Urban trajectories in Brazil.