See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/232921706
Revisiting the Hierarchy of Urban Areas in the
Brazilian Amazon: A Multilevel Approach
Article in Population and Environment · May 2009
DOI: 10.1007/s11111-009-0083-3 · Source: PubMed
CITATIONS
READS
33
43
3 authors:
Gilvan Guedes
Sandra Maria Fonseca Costa
31 PUBLICATIONS 98 CITATIONS
48 PUBLICATIONS 204 CITATIONS
Federal University of Minas Gerais
SEE PROFILE
Universidade do Vale do Paraíba
SEE PROFILE
Eduardo S. Brondízio
Indiana University Bloomington
156 PUBLICATIONS 5,589 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
NSF SEES Fellows: Linking rural smallholder soil and water management practices to sea level rise
vulnerability in tropical deltas View project
Doctoral Dissertation Research: An anthropological exploration of the efficacy of conditional cash
transfers (CCTs) on food security View project
All content following this page was uploaded by Eduardo S. Brondízio on 01 January 2017.
The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document
and are linked to publications on ResearchGate, letting you access and read them immediately.
ACT Publication No. 09-08
Revisiting the Hierarchy of Urban Areas in
the Brazilian Amazon: A Multilevel
Approach
G.R. Guedes, S. Costa, and E.S. Brondizio
Reprinted from: Population and Environment 30:159-192.
_______________________________________________________________________________________
Anthropological Center for Training and Research on Global Environmental Change
Indiana University, Student Building 331, 701 E. Kirkwood Ave., 47405-7100, U.S.A.
Phone: (812) 855-6181, Fax: (812) 855-3000, Email: act@indiana.edu, internet: www.indiana.edu/~act
Popul Environ (2009) 30:159–192
DOI 10.1007/s11111-009-0083-3
Revisiting the hierarchy of urban areas
in the Brazilian Amazon: a multilevel approach
Gilvan Guedes Æ Sandra Costa Æ Eduardo Brondı́zio
Published online: 6 August 2009
Springer Science+Business Media, LLC 2009
Abstract The Legal Brazilian Amazon, while the largest rainforest in the world, is
also a region where most residents are urban. Despite close linkages between rural
and urban processes in the region, rural areas have been the predominant focus of
Amazon-based population-environment scholarship. Offering a focus on urban areas
within the Brazilian Amazon, this paper examines the emergence of urban hierarchies within the region. Using a combination of nationally representative data and
community based surveys, applied to a multivariate cluster methodology (Grade of
Membership), we observe the emergence of sub-regional urban networks characterized by economic and political inter-dependency, population movement, and
provision of services. These networks link rural areas, small towns, and medium and
G. Guedes (&)
Department of Sociology, Brown University, Maxcy Hall 112 George Street,
Providence, RI 02912, USA
e-mail: Gilvan_Guedes@brown.edu; grguedes@indiana.edu
G. Guedes S. Costa E. Brondı́zio
Anthropological Center for Training and Research on Global Environmental Change,
Indiana University, Bloomington, IN, USA
S. Costa
e-mail: smcosta@indiana.edu
E. Brondı́zio
e-mail: ebrondiz@indiana.edu
G. Guedes
Department of Demography, University of Minas Gerais, Belo Horizonte, Brazil
S. Costa
Department of Regional Planning, University of Vale do Paraı́ba,
São José dos Campos, Brazil
E. Brondı́zio
Department of Anthropology, Indiana University, Bloomington, IN, USA
123
160
Popul Environ (2009) 30:159–192
large cities. We also identify the emergence of medium-size cities as important
nodes at a sub-regional level. In all, the work provides insight on the proposed
model of ‘disarticulated urbanization’ within the Amazon by calling attention to the
increasing role of regional and sub-regional urban networks in shaping the future
expansion of land use and population distribution in the Amazon. We conclude with
a discussion of implications for increasing intra-regional connectivity and fragmentation of conservation areas and ecosystems in the region.
Keywords Brazilian Amazon Urban hierarchy Grade of membership
Disarticulated urbanization Community and urban formation
Introduction
The Legal Brazilian Amazon (LBA)1 shares the reputation of being the largest
rainforest in the world and an area of exponential urban development. It
encompasses an area of 5,217,423 sq. km, representing 61% of the Brazilian
territory and comprising, in 2007, a population of 22,303,252 (i.e., 12% of Brazilian
population), over 70% of which live in urban areas. Despite the increasing
prominence of cities in the region, population-environment research in the Amazon
has focused mainly on rural areas.
There is a growing, but still limited, literature discussing and proposing
analytical models of urbanization dynamics in the Amazon. Such models consider
the spatial-temporal dimensions of urbanization dynamics and their relation to roadriver networks, as well as their social and economic interconnections and
consequences. Such studies date back at least to the 1940s (Wagley 1953; Rocha
1968). Not until the 1980s, however, when a clear regional trend in urbanization
began to be seen, that more systematic scholarship developed on this topic (for
instance, Becker 1985; Sawyer 1987; Machado 1989, 1994, 1999; Corrêa 1987;
Browder and Godfrey 1996, 1997; Perz 2000; Browder 2002; Simmons et al. 2002;
Padoch et al. 2008).
In the midst of this increasing urbanization, heterogeneity of urban spaces and the
resulting complexity of networks, have led some authors to consider whether classical
urban hierarchical models can explain the formation of the larger Amazonian urban
system and contribute to understand the trajectories and consequences of regional
urbanization. In this paper, we use a combination of multivariate fuzzy cluster
analysis and spatial analysis to examine the level of primacy among Amazonian
cities, their relative importance and infrastructural differences, and their level of
interdependency resulting from inter-urban demographic movements. We test the
proposition by Browder and Godfrey (1997) of the inexistence of a regional urban
hierarchy (‘‘disarticulated urbanization’’) and use the methodological approach
1
The Legal Brazilian Amazon—defined for planning and administrative purposes by the federal
government in 1966 by Law No. 5173—includes 760 municipalities currently distributed across nine
states: Pará, Amazonas, Mato Grosso, Rondônia, Roraima, Amapá, Acre, Maranhão, and Tocantins. The
western side of Maranhão is included in the Legal Brazilian Amazon, while the other states are
completely included in the administrative region.
123
Popul Environ (2009) 30:159–192
161
presented by Garcia et al. (2007) to analyze this proposition. We define hierarchy as
the relative ranking of cities based on their level of primacy, complexity of functions,
and relative importance within the region, measured by such factors as available city
services, attraction of migrants, and population size. Specifically, we integrate and
examine the following variables to define the relative level of urban importance and
hierarchy within the region: date of municipality creation, urban density, presence
and area of natural reserves, presence of roads and rivers, urban infrastructure and
services, migration flows, commuting movements and health care provisions. We
hypothesize that lower levels of analysis will correspond with higher probabilities of
verifying a more symmetrical urban system, i.e., an urban hierarchy in the Amazon
exists only below the regional level.
Our model combines different databases within a spatially explicit framework:
data from the Brazilian Demographic Census (IBGE 1991, 2000), data from the
Brazilian National Hospital Information System and Brazilian National Archive of
Hospital Establishment (MS 1998, 2002a, b), and data from a socioeconomic survey
of rural communities in the municipalities of Santarém, Belterra, Placas, Ruropolis,
Monte Alegre, and Itaituba, all in the state of Pará (ACT 2004). The combination of
nationally representative surveys and community-level data allows us to test the
classical articulated urban model (e.g., Christaller 1966) at three different spatial
levels: regional, sub-regional, and local. Our analysis of regional urban articulation
complements other efforts that have called attention to intra-regional differences
(e.g., Perz 2000; Becker 2005) and attempted to stratify and qualify sub-regional
urban networks using similar methodology as presented here (e.g., Garcia et al.
2007; IPEA 2002).
This paper is organized as follows. First, we present a literature review of the
history of Amazonian urbanization, along with a discussion of the approaches used
for analyzing both multi-level regional urban hierarchies and intra-regional urban
differences. Next, we discuss the data analysis, including description of the data sets
and the Grade of Membership (GoM) model. Finally, we present our results and
discussion of urban hierarchies.
In addition to testing theoretical propositions and methodological approaches,
our analysis aims at providing subsidies to current efforts to develop regional and
state level ecological-economic zoning2 (ZEE) and to inform prognostic models of
deforestation and expansion of human occupation in different parts of the region. To
date, most prognostic models forecasting regional land use change have discussed
urban dynamics using proxies of population growth rates and road connections.
Attention to the formation of urban networks, their axis of expansion, and their
intersection with a growing but largely disconnected system of protected areas is
necessary to understand the future of population distribution, the surrounding
human landscapes around protected areas, gradients of land and resource rent value,
pollution sources and sinks, the formation of market chains, and regional variations
in patterns of economic development.
2
Economic-ecological zoning (EEZ) is a spatially explicit planning instrument, usually developed at the
state level, which includes an assessment of environmental and socioeconomic conditions and provides
directions aiming at reconciling economic activities and environmental conservation (Sombroek and
Carvalho 2000).
123
162
Popul Environ (2009) 30:159–192
Urban hierarchy and regionalization of the Amazon region
Urban networks
The city, as defined, is both an element and an economic and social system within a
world system that stimulates economic development and technical improvement
(Clark 2003; Santos 1997). Different criteria have been adopted to define
specifically what a city is and to establish the way cities are connected to each
other. However, the complexity of urban spaces, in terms of social relations,
economic activities and the range of services offered to residents, makes its
definition a difficult task.
Cities are related to ‘‘urban places’’ which are associated to local jurisdictional
institutions, such as municipalities, townships, and localities (Henderson 1997).
Cities also can be referred to as metropolitan areas, which are collections of
contiguous urban spaces within a structurally functional system. Cities are important
for offering employment, infrastructure, information, educational services, and
essential goods and services (Amorim Filho and Serra 2001). Nevertheless, given
their relative value to people, how can we classify cities, or order them in terms of
importance, within a complex urban network? Understanding the formation and
articulation of urban networks in different levels of organization is an important
component of urban and regional planning, particularly in areas experiencing rapid
urban expansion such as the Amazon.
One important discussion about urban hierarchies was proposed by Christaller
originally in 1933 (1966). Christaller, studying urban development in southern
Germany, proposed the Theory of Central Places, which defines general principles
that regulate the number, size and distribution of towns. Observing the spatial
arrangement of settlements in the mostly flat topography of southern Germany,
Chistaller noted that towns of a certain size were approximately equidistant.
Analyzing the functions of these towns and their hinterlands, he developed a model
for predicting patterns of settlement locations using geometric shapes.
Central Place Theory is based on various restrictive assumptions or principles,
with settlements (villages, towns or cities) having: (a) consumers rationally favoring
the nearest market, (b) transportation costs being equal in all directions and
proportional to distance, (c) perfect market competition, which implies no excess
economic surplus, (d) gathering of services having similar levels in the same
centers, and (e) a hierarchy of services according to their frequency of use.3 Despite
not being essential to the theory, additional assumptions are used as tools for
deriving its geometrical models, by considering settlements with (a) an isotropic
surface, (b) an evenly distributed population, (c) similar purchasing power of all
consumers, and (d) markets developing an hexagonal area of influence (Mushinski
and Weiler 2002; Shonkwiler and Harris 1996).
Because transportation cost is equal in all directions and there are no underserved or over-served market areas through the surface, for any given order,
settlements will be equidistance from each other. Generally stated, the higher the
3
We thank one of four reviewers for the suggestion of principals (d) and (e).
123
Popul Environ (2009) 30:159–192
163
order of a settlement, that is, the more complex its function, the further apart it will
be from another same-order settlement and the larger the area it will service. The
order of a settlement within a hierarchy is positively associated with its area of
influence and negatively related to the number of settlements (Christaller 1966). The
distance and spatial organization can vary according to three principles: marketing,
transportation and administration. The basic difference regarding the influence of a
specific principle on settlement arrangement is the number of places under the
influence of a settlement, with market forces producing the narrower influence zone
(theoretical assumption a) and administrative function yielding a wider zone of
influence.
The concept of central place provides a heuristic starting point to define cities
(Hall 2002). Central place can be defined as a settlement that provides one or more
services for its hinterland. All cities (small, median, and large) can be considered
central places and all are endowed with relative central functions. Cities, in this
sense, are providers of goods and services to populations living in their surrounding
localities. The centrality of a town is related to the level of relative importance
within an urban network, particularly in its scope and type of functions provided to
residents, its area of influence, and its population size. As such, a ‘central place’
hierarchy expresses a systematic and cumulative hierarchical pattern organized into
a series of functions that defines the proportional distance and quantities of central
places within a given region. For example, the higher the hierarchical level of a city,
the larger its functional role in providing services to surrounding residents (e.g.,
higher order services). Low order settlements provide simple basic services (e.g.,
post-offices, churches, elementary schools, and grocery stores); while high order
settlements offer specialized services (e.g., department stores, universities, and
specialized hospitals). Importantly, as noted by Christaller, cities can change their
place within a regional hierarchy over time.
Urban networks, thus, can be considered as a set of functionally articulated,
interconnected cities. These networks are formed with vertices or ties representing
different cities, towns or villages that are endowed with urban functions, and the
flows between them are achieved by roads or other forms of transportation, as well
as systems of communication (Corrêa 1991). Urban networks therefore underlie the
articulation of different groups and economic and political systems within and
between societies.
Considering these aspects, some authors (Gohn 1999; IPEA 2002; Clark 1982,
2003) have used population size to rank cities according to their importance within
an urban network. Generally, there are five types of cities (Hall 2002): global cities
(i.e., more than 10 million inhabitants, dominating the global market and political
economic decisions), mega-cities or global metropolises (i.e., more than 10 million
inhabitants, high level of poverty and urban problems), national and regional
metropolises (i.e., more than 1 million of inhabitants), regional centers or median
cities (i.e., population between 50 and 800,000 inhabitants), and small cities (i.e.,
\50,000 inhabitants). The extent to which these criteria apply to regions such as the
Amazon, however, is questionable for two main reasons. First, the position of a city
within an urban hierarchy depends on the size of the largest urban center. In the
Legal Brazilian Amazon, the largest urban center—Manaus—has a population
123
164
Popul Environ (2009) 30:159–192
Table 1 Area, population and population density for the states of the legal Brazilian Amazon
Geographic unit
Area (km2)
Population
Population density
Acre
153,150
655,385
Pará
1,253,165
7,065,573
4.3
5.6
Amazonas
1,577,820
3,221,939
2.0
Roraima
225,116
395,725
1.8
Amapá
143,454
587,311
4.1
Tocantins
278,421
1,243,627
4.5
Mato Grosso
906,807
2,854,642
3.1
Maranhão
325,940
6,118,995
18.8
Rondônia
238,513
1,453,756
6.1
Legal amazon
5,217,423
22,303,252
4.3
Brazil
8,511,965
183,987,291
21.6
Source: IBGE (2000, 2007)
estimated for 2007 of \2 million inhabitants (IBGE 2007), far less than the
population threshold of global or mega-cites from IPEA typology. Second, with
increasing prominence of non-central functions in some parts of the Amazon, such
as cities specialized in ecotourism and agribusiness, the applicability of evolutionary theories to describe its urban systems has become progressively unrealistic and
incapable of fully describe its regional heterogeneity.
The urban hierarchy in the Amazon region
Despite the region’s reputation as a rural environment experiencing deforestation,
Amazonia has been largely urbanized since at least 1980. New urban centers have
multiplied across the landscape in previously inaccessible ‘‘terra firme’’ (upland)
forest areas, inspiring different discussions about the nature of urban networks in the
Amazon region (Becker 1985, 2005; Martine and Turchi 1988; Sawyer 1987;
Browder and Godfrey 1997; Vicentini 2006).
According to the IBGE (2007), in 2007 the Legal Brazilian Amazon had a
population density of 4.3 persons per square kilometer; five times lower than the
corresponding Brazilian population density. In general, all states of the Legal
Brazilian Amazon have low population density (Roraima and Amazonas having the
lowest with 2 hab/km2), with the very exception of Maranhão ([18 hab/km2;
Table 1).
In a sharp contrast with the low density observed at the state level, some state
capitals in the Amazon have population density reaching over 1,000 inhabitants per
square kilometer (Table 2).4 This is the case of São Luı́s and Belém, state capitals of
4
Unfortunatelly the statistics about urban area of Amazonian municipalities were not available from
online sources. For that reason, we present in Table 2 the population density of the entire municipalities
(state capitals) instead of their urban areas. This table, however, is shown illustratively only. For our GoM
analysis, we use the urban perimeter from IBGE, although the distinction between urban and rural still
carries some imprecision.
123
Popul Environ (2009) 30:159–192
165
Maranhão and Pará, respectively. On the other extreme, state capitals such as Rio
Branco and Porto Velho have density under 30 inhabitants per square kilometer, a
ratio closer to the national average. Independent of population density, all state
capitals of the Legal Brazilian Amazon have an urbanization ratio over 90%, with
some reaching almost 100%. The one exception, however, is Porto Velho, the
capital of Rondônia. According to Browder and Godfrey (1997), Porto Velho has
only an administrative relevance, while Ji-Paraná, the second biggest city of
Rondônia, is the economic center of the state. According to the Population Tally of
2007 (IBGE 2007), Ji-Paraná had an urbanization ratio of 88%, in contrast to the
40% of Porto Velho. Its population density is also approximately 50% higher than
Porto Velho’s (15.6 compared to 10.8 persons/km2, respectively).
In all, part of the regional variation in population density is due to the large
variance in municipality areas. Even so, we find exception such as Rio Branco and
Porto Velho. Both have similar population densities but very distinct municipality
areas. As suggested by one of our reviewers, this is a reflection of differences in the
actual urban density underlying the results in Table 2 and suggested by the less
variable results for urbanization ratios across state capitals.
Drawing upon the political economy literature of intersectorial articulation
(Goodman et al. 1984; Roberts 1991), capitalist penetration (Armstrong and McGee
1985; Sawyer 1984), and world systems perspectives (Brum 1988; Katzman 1976),
urbanization in the region has been interpreted as a deliberate strategy to stimulate
regional economic development and alleviate demographic pressures in other parts
of Brazil. As a result, many Amazonian cities received batches of small farmers
leaving agrarian settlements to live in urban areas, as well as groups of migrants
attracted by a tertiary sector in development and, for the most part, by public
Table 2 Selected demographic and geographic information for state capitals of the legal Brazilian
Amazon
Municipality
Rio Branco (AC)
Areaa
Populationb
Population density
Urban populationb
Urban ratio
9,877
290,639
29.4
269,505
Manaus (AM)
11,408
1,646,602
144.3
1,636,837
99.4
Macapá (AP)
6,533
344,153
52.7
328,865
95.6
São Luı́s (MA)
92.7
828
957,515
1,156.4
917,155
95.8
Cuiabá (MT)
3,971
526,830
132.7
519,015
98.5
Belém (PA)
1,065
1,408,847
1,322.5
–
34,069
369,345
10.8
148,388
40.2
Boa Vista (RR)
5,687
249,853
43.9
246,156
98.5
Palmas (TO)
2,465
178,386
72.4
175,166
98.2
Porto Velho (RO)
99.4c
Source: IPEA Data (2002), IBGE (2000, 2007)
AC Acre; AM Amazonas; AP Amapá; MA Maranhão; MT Mato Grosso; PA Pará; RO Rondônia; RR
Roraima; TO Tocantins
a
Municipality area based on information from the Brazilian Census of 2000
b
Based on information from the Brazilian Population Tally of 2007
c
Belém was not included in the population tally of 2007. This result is based on the 2000 urban ratio
123
166
Popul Environ (2009) 30:159–192
institutions (Sawyer and Carvalho 1986). This rapid growth has led to cities being
unable to offer proper services, such as water and sanitation, to their urban
populations (Lira 2008). This process, termed ‘‘over-urbanization’’ by Browder and
Godfrey (1997), occurs when urban population growth is unaccompanied by the
necessary economic growth and technological change, leading to an asymmetrical
and unorganized urbanization. Some authors have considered this process a
‘‘ruralization’’ of the urban (Martine and Turchi 1988), although Browder and
Godfrey (1997) presents empirical evidence questioning Martine and Turchi (1988)
argument by showing that the majority of Amazonian urban migrants had originated
in other urban centers (urban-urban migration). Even so, Becker (2005) affirms that
these areas maintain the identity of cities, at least in an Amazonian context, despite
their lack of infrastructure and services. The fact is that ‘‘many imperative urban
problems stay unaddressed in Amazonia, including deficient infrastructure, social
and medical services, rapid shantytown growth and pollution’’ (Browder and
Godfrey 1997: 3).
In part, because cities in the Amazon have developed with strong links to the
surrounding rural environment, different authors have proposed approaches and
theories to explain the unique process of urban development and urban networks
found within the Amazon. For example, Browder and Godfrey (1997: 11–15)
suggest a pluralistic theory of disarticulated urbanization to explain the lack of an
urban hierarchy in the Amazon and the existence of a nontraditional urban network.
According to these authors, this disarticulated nature of urbanization in the Amazon
is a result of multiple processes and factors, such as: (a) the Amazon is a
heterogeneous social space; (b) the configuration of settlement systems in Amazonia
is irregular and polymorphous, without a clear principle of spatial organization; (c)
urban growth is functionally disarticulated from agricultural development in many
parts of the region; (d) rapid urbanization is not linked to regional industrialization;
(e) urbanization in Amazonia is linked to economic forces operating at the global
level, but not necessarily subordinated to a world economic system; (f) urban
centers in Amazonia are technological crossroads that link specific activities to
global circuits of information and exchange; (g) the contemporary Amazonian urban
frontier is largely geopolitical but remains politically disarticulated from a central
state system; (h) the rural–urban dichotomy is problematic in Amazonia due to
complex and regionally heterogenous patterns of population movement and
migration; and, (i) environmental change caused by tropical deforestation, including
water contamination and resource depletion, is increasingly mediated by urbanbased interests. We add to this list of forces molding the complex urbanization
process in the Amazon, (1) the recent settling of most urban centers, (2) the new
interest in regional ecotourism, which has been affecting land prices and linkages
between urban and rural areas, and (3) initiatives from the government to demarcate
reserves and promote local governance of resource use systems (Fearnside 2003;
Becker and Léna 2002; Motta 2002).
The confluence of multiple factors has produced settlement systems in the
Amazon Region which are disarticulated from any single master principle of spatial
order, and a spatial organization ‘‘largely asymmetrical providing scant evidence of
orderly, nested hierarchies predicted by Central-Place Theory’’ (Browder and
123
Popul Environ (2009) 30:159–192
167
Godfrey 1997: 95). The influence of migration on urbanization dynamics in the
Amazon is an illustrative example of its disarticulated nature. Because some cities
in the Amazon whose economies are based on extractivist activities have been
proved to fail in maintaining a sustainable job market for a long period, a proportion
of their population tends to out-migrate towards new active extractivist or agrarian
frontiers in search of emerging opportunities. These population shifts the dynamics
of growth away from a previously urban center, creating a turning point in its
trajectory of urban development and affecting the provision of services and
infrastructure to its surrounding areas. In some cases, these cities become hollow or
ghost cities with declining local importance and economic stagnation. These
patterns are not predicted by the Central Place Theory or any other evolutionary and
linear progressive approaches, such as the Turner theory of frontier development
from agrarian to urban centers (Turner 1920).
In other instances, historical cities linked to government-led agrarian settlement
projects have become locus for new corporativist projects. In Santarém, a major
export harbor recently has been created, using private as well as government
funding, in order to facilitate the channeling of the recent large scale soybean
production in the region and grain production coming from the center-western
region of Brazil, connected to Santarém through the highway BR-163 (Liberal
2002). In this area, two frontier systems are present: unsuccessful government-led
colonization projects and recently promoted large-scale systems of soybean export,
representing a recent penetration of large-scale capital in a previously small-scale
rural production system. Santarém, although a city of sub-regional importance for
more than a century, is now directly linked to global cities as a result of ambitious
planning by the Brazilian government to outcompete the US in soybean production
and export.
The peculiarities of the region’s urban network systems, as illustrated above, also
have led some authors to propose approaches to discuss the ‘‘regionalization’’ of the
Amazon (a typology of regional blocks) by highlighting intra-regional urban
differences (e.g., Perz 2000). Such efforts to disaggregate the Amazon into intraregional blocks are aimed at showing economic discrepancies within the region and,
ultimately, support approaches to regional development and planning that are
sensitive to social-economic differences. Perz (2000), discussing the socioenvironmental dimensions of urban areas, pointed out that Amazonian cities can
differ considerably ‘‘from one part of the region to another’’ (p. 190). He suggests
several questions and approaches to examine whether and how the quality of life in
Amazonian cities improves over time, arguing that it is possible to discriminate
between urban areas of different status and environmental quality and between
newly-formed and more established urban areas.
The Brazilian Applied Economic Research Institute (IPEA 2002) has developed
extensive research characterizing the Brazilian urban network by region, using
criteria based on urban size, urban function, functional dependency, and area of city
influence. In the Amazon, the IPEA recognizes one Metropolitan Agglomeration
(Belém), six cities as first order Regional Urban Centers (Palmas, Porto Velho, Rio
Branco, Manaus, Boa Vista, and Macapá), and four cities as second order Regional
Urban Centers (Santarém, Marabá located in the state of Pará, Araguaı́na located in
123
168
Popul Environ (2009) 30:159–192
Tocantins, and Ji-Paraná located in Rondônia). Yet, it is important to consider
Manaus as having a first level of importance in the region, in many aspects, similar
to Belém. Manaus is the backbone of the state of Amazonas and plays important
roles for the entire western part of the Amazon. It is strongly connected to national
and international markets through its industrial system. Arguably, however, Belém
continues to play its historical role as an urban area connected to and influencing the
Amazon region as a whole.
Additionally, Garcia et al. (2007) have characterized urban networks in the
Brazilian Amazon using a model that integrates levels of socioeconomic organization of municipalities and their interrelationships, as determined mainly through
the strength of migratory movements. Their proposed model of territorial
organization includes five components: (1) the hierarchy of central places (poles)
established by the concentration of urban specialized services, (2) the geographical
distance between central poles and other centers, (3) population size, (4) the level of
migratory movements among municipalities, and (5) a socioeconomic index. These
components were combined into a gravitational model, using Grade of Membership
(GoM), to produce measures and maps of municipal networks in the Brazilian
Amazon. As a result, out of 792 municipalities in the Brazilian Amazon, nine were
classified as macro-poles, 29 were classified as meso-poles and 48 as micro-poles.
The areas of influence of these poles were determined according to the three
hierarchy levels: macro (e.g., all nine state capitals), median (e.g., median
municipalities) and micro poles (e.g., 116 municipalities).
In this paper, we develop a conceptual model of urban system formation (Fig. 1),
from the level of rural properties and rural communities, to their interconnections to
small and medium cities, and to subsequent connections to large cities at subregional and regional levels. These large cities, at a regional level, comprise a wider
network, exchanging population (through migration and commuting movement),
capital, services and commodities among them. The model also considers these
large cities as having possible direct connections to small cities and communities, as
well as small towns having direct linkage with outer areas through the presence of
large-scale capital financing production systems or transportation networks (such as
harbors, airports, and credit for large-scale grain production farms), underscoring
the disarticulated nature of the Amazonian urban system, as argued by Browder and
Godfrey (1997).
The model is flexible enough to recognize the presence and persistence of
traditional urban hierarchies at smaller scales, such as local or sub-regional levels.
As argued by Browder and Godfrey (1996, 1997), the Brazilian Amazon is a quasiexperiment for urban geographers, where new areas are beginning to be populated,
and urban systems assume different forms over time. Old cities have been exposed
to different government policies and influenced by external forces, while traditional
local political elites have been challenged by new ascending social groups. As a
result, cities change their relative position within an urban network over time. For
instance, areas undergoing processes characteristic of frontier occupation (e.g.,
intensive extractivism, rapid land cover change, active land market, fast population
turnover, and disorganized forms of occupation) may, eventually, develop more
symmetric urban systems and networks as they mature. The occurrence of many
123
Popul Environ (2009) 30:159–192
169
Fig. 1 Conceptual model of local to regional urban system formation in the Amazon
such cases throughout the region, thus, adds to the already heterogeneous urban
landscape of the Brazilian Amazon.
Several conditions render the urbanization of the contemporary Legal Brazilian
Amazon different from that experienced in other parts of the world, and not least
123
170
Popul Environ (2009) 30:159–192
southern Germany in the first half of twentieth century, which gave rise to the
‘Central Place Theory’. First, the Amazon basin has an irregular surface both in
topographic and biophysical terms (e.g., soils, topography, resource distribution,
and seasonality). Upland and wetland environments differ considerably across the
region. Because of differences in occupation history, government policies,
landscape accessibility and distribution of resources, population are not evenly
distributed. The lower density of northwestern and southwestern Amazonia
contrasts with the highly populated northeastern and southeastern parts of the
region. Since different frontier systems developed in different areas, purchasing
power of potential consumers greatly varies across regions. Local dependency on
external food supply contrasts with areas of self-provisioning; fast growing
shantytowns create demands for services and new political priorities. Differences in
food supply and uneven service distribution create various forms of market
influence, within and across parts of the region. Furthermore, diverse forms of
frontier expansion incorporate external capital at different scales and intensities,
while generating market concentration in certain areas and market absence in others.
Finally, government participation in the Amazon has varied not only across space,
but also through time. Political alliances between the federal government and
national/foreign capital have brought support to some areas and total disregard to
others. Furthermore, change of political regimes in 1985, from a military to a civilbased government, dramatically affected the level and forms of political participation of government agencies and civil organizations in regional urban
development.
In all, we contribute with a conceptual framework which aims at advancing the
notion of disarticulated urbanization by addressing the urban network creation and
development at different levels and by calibrating an empirical multilevel model
with data from the Brazilian Amazon region at regional, sub-regional, and local
levels of data aggregation. This is particularly important in the Amazonian context
because of the increasing presence of node cities, as we will discuss later in the text,
which provide services and infrastructure to their surrounding areas, such as in more
traditional spatial organizations.
Data acquisition and model development
Data acquisition
As described above, we used several data sources to study and analyze urban
growth, development, and hierarchy at three different spatial levels of the Legal
Brazilian Amazon—regional, sub-regional and local (Fig. 1).
The Legal Brazilian Amazon is composed of a total of 760 municipalities, 334 of
which were created after the Brazilian Constitution of 1988 (IBGE 2007). At the time
of the 2000 Brazilian Demographic Census, 747 municipalities were found within
the region. Therefore, our analytical sample includes 747 observations at the level
of municipalities, derived from micro level data collected by the Brazilian
123
Popul Environ (2009) 30:159–192
171
Demographic Census (IBGE 1991, 2000).5 Because some variables, such as
infrastructure and household income, were only available for private and permanent
households, we discarded other types of households in the analyses (i.e., private but
temporary households and collective households6). The discarded household
observations correspond to 1.92% of the population in 2000. Thus, our final sample
includes 530,188 households and 2,368,515 individuals. Nevertheless, for analysis of
commuting movements within the region, we preserved a sample of 2,404,083
individuals living in 541,573 households in order to avoid sample selection bias.
We also used databases from the Brazilian National Council of Municipalities
(CNM 2007), the Brazilian Hospital Information System (MS 1998, 2002a, b), the
Brazilian Population Tally (IBGE 2007) and the Brazilian National Archive of
Health Establishments (MS 2002a, b) for analysis of the regional and sub-regional
urban levels.
For the local level, we used ethnographic field data, community surveys, and
archival research collected by members of the Anthropological Center for Training
and Research on Environmental Global Change at Indiana University of 181
communities located in the municipalities of Santarém, Belterra, and Monte Alegre
(ACT 2004). Our community sample, surveyed in 2004, ranges, in terms of
population size, from 10 to over 5,000 individuals. These communities are formally
recognized by municipalities (i.e., listed in public records for health and school
services) and by the regional Catholic diocese. These communities vary in their
distances to local towns and urban centers, and also vary in their date of creation,
from \5 years old up to the colonial era (i.e., 200 years old). Table 4 presents a list
and descriptions of all variables used for analyses.
The profiles generated from the local level present a limitation: the absence of a
variable representing connections between rural communities and between rural
communities and urban centers (e.g., commuting, frequency of visits). In order to
overcome the absence of a proxy for demographic and functional linkage, we
selected the municipality of Santarém to analyze commuting movement from urban
areas of neighboring municipalities included as part of our community-level sample.
As shown below, Santarém appears in our regional level as an urban area with a
sub-regional first level importance and includes the communities with the better
relative position at the local level.
The grade of membership model
Model description
The Grade of Membership (GoM) model is a statistical methodology used to
delineate clusters of elements (or ‘profiles’ of a group of elements based on their
characteristics) within a heterogeneous and multidimensional dataset (Woodbury
et al. 1978; Manton et al. 1994; Lamb 1996; Portrait et al. 1999, 2001; Cassady et al.
2001). The main difference between GoM and the majority of other clustering
5
For analysis of trends in urbanization, we used the Brazilian Demographic censuses from 1970 to 2000.
6
In the Portuguese, ‘‘domicı́lios coletivos’’.
123
172
Popul Environ (2009) 30:159–192
techniques is that the former does not consider individuals and objects to be
organized in well-defined (i.e., ‘crispy’) sets. The GoM model is classified as a
fuzzy cluster technique because the same individual is allowed to have a certain
level of pertinence to multiple sets. This is an important aspect of GoM because
individual heterogeneity at the category level can be estimated, producing a finer
description of the sample heterogeneity (Machado 1997).
As discussed by Garcia et al. (2007), in addition to population size and density,
other factors that are relevant when defining an urban hierarchy include geographical
isolation, migration, and social development, especially provision of services and
infrastructure. We argue that elements such as the date of municipality formation,
and natural characteristics, such as proximity to rivers and/or roads and areas of
natural reserves and parks, contribute to or constrain position of the municipality in
the urban hierarchy and its trajectory over time. As most of the transportation
networks in the Legal Brazilian Amazon is still incipient and precarious, roads and
rivers networks along with federal regulations regarding access to natural reserves
represent specific geographical, anthropogenic and institutional constrains, affecting
the probability of traditional hierarchical formation.
In the GoM model, we generated profiles of urban areas, which differed from the
ones proposed by Garcia et al. (2007) in four different ways: (a) our unit of analysis
was the urban area of each municipality,7 not the entire municipality; (b) our sample
included only the municipalities within the Legal Brazilian Amazon; (c) we
included geographical characteristics of the municipalities, such as the area of
natural reserves and parks and the presence of roads/rivers within the municipality;
(d) we included the date of municipality creation for each city in our sample. We
believe that the inclusion of the last two factors and the addition of local level data
will greatly improve upon other empirical models of urban hierarchy that have not
included these factors.
The empirical application of GoM implies that:
1.
2.
3.
Two or more well-defined profiles, called extreme or reference profiles, are
identified from the non-observed association among the categories of variables
in the model;
Reference profiles correspond to crispy sets with same mathematical properties;
Each individual possesses degrees of pertinence to extreme, or reference,
profiles. For example, if an individual displays all the characteristics of one of
the reference profiles, his/her degree of pertinence to that profile will be 100%
and, consequentially, 0% to the other. Thus, the closer the person is to one
reference profile, the higher his/her degree of pertinence is to that profile and
lower to the others.
7
The urban area is based on the classification adopted by IBGE in the Brazilian Census, according to the
municipality law from September 1, 1991 (IBGE 1991, 2000). Therefore, for all the variables used from
the Demographic Census, we selected the observations classified as in the urban sector, which includes:
(a) urbanized area (city); (b) not urbanized area (distrital seats), and (c) isolated urban area. We recognize
that the classification of observations into urban sector is somewhat imprecise and may include more than
one settlement, but is the closest we can get using the micro data from IBGE. For the geographical
variables, we considered the urban perimeter also provided by IBGE. We explicitly recognize that some
bias still persists due to likely variation in measurement from one census to the other.
123
Popul Environ (2009) 30:159–192
4.
5.
6.
7.
8.
173
It is common for individuals to have no predominant characteristics of any
specific reference profile (i.e., individuals who are equidistant to all extreme, or
reference, profiles).
An individual’s degree of pertinence to the reference groups constitutes a fuzzy
set. Therefore, a larger number of variables will improve the definition of the
fuzzy set;
Non-observed heterogeneity is not considered a problem for the GoM because
the elements of the sets are individual attributes;
Parameters are estimated through iterative processes. This implies that smaller
sample sizes will require shorter time for the likelihood function to converge to
its maximum value.
The previous assertions attest that GoM has the desirable property of analyzing
categorical data for small samples with a large number of variables and of
dealing with endogeneity.
In the GoM method, an estimate of the degree of pertinence for each individual
relative to all the sets is created, resulting in a fuzzy set or partition for each
individual. This fuzzy partition of each individual is used to delineate the extreme,
or reference, profiles. For each element in a fuzzy set, there is a score of the degree
of pertinence, gik, which represents the degree to which that element ‘‘I’’ belongs to
the reference group, k. These scores vary from 0 to 1. Zero indicates that the
element does not belong to the set and one means that it entirely belongs to the set.
The value gik represents the proportion or intensity of pertinence to each of the
extreme profiles. Thus, the following constraints to each parameter (or score for
each individual) apply:
gik 0
for each i and j
k
P
gik ¼ 1 for each i
k¼1
The following assumptions apply for the model specification and the estimation
of the parameters (scores):
1.
2.
3.
4.
The random variables represented by Yijl where ‘‘i’’ refers to the individual, ‘‘j’’
to the variable and ‘‘l’’ to each category of the variable are independent across
‘‘i’’. That is, the answers given by each individual are independent;
The gik (k = 1, 2,…, k) are moments of the random vector fi = (fil,…, fik) with
distribution function H(x) = P (fi B x). Thus, GoM scores are the result of
random variables when an individual is selected in the population under
analysis. The distribution of the samples of realization (the scores in the
sample) gives the estimates of the distributional function H(x);
If the degree of pertinence, gik, is known, the answers to the questions Yijl by
individual ‘‘i’’ are independent across categories for the same variable;
The probability to answer ‘‘l’’, for the jth question, for the individual with the
kth extreme profile, is kkjl. By assumption, there is at least one individual who is
a well-defined (‘crispy’) member of the kth profile. This assumption gives the
probability that this individual has to answer each category for each question.
This assumption can be represented as:
123
174
Popul Environ (2009) 30:159–192
kkjl 0
for each k; j and l
k
P
kkjl ¼ 1 for each k and j
k¼1
5.
The probability of an answer at level ‘‘l’’, of the jth question, by individual ‘‘i’’,
conditional to the score gik is given by:
k
X
P Yijl ¼ 1 ¼
gik kkjl ¼ 1
k¼1
The probability model, based on a random sample, is the multiplication of the
multinomial model by the probability for each cell, given by:
k
X
E Yijl ¼
gik kkjl
k¼1
where gik is, by assumption, known and equal to or bigger than zero. The maximum
likelihood model is, then, described as:
!yijl
L
k
J Y
I Y
Y
X
LðyÞ ¼
gik kkjl
i1 j¼1 l¼1
k¼1
Defining the extreme profiles
The number of extreme profiles can be established according to two criteria: (1)
by means of a theoretical orientation (Sawyer et al. 2002), or (2) by a technical
criterion, as suggested by Manton et al. (1994). According to the authors, a model
with k ? 1 profiles can be compared to a model with k profiles using the values of
the Akaike criterion (AIC) for each extreme profile as the test statistics. A
generalization of the estimated AIC of the maximum likelihood function allows the
selection of the model with the smallest distance from the data, even in cases where
the structural model is unknown. In our analysis, we based our criterion on the
number of hierarchical regional levels proposed by Browder and Godfrey (1996,
1997).8 Thus, we fixed three reference groups, using a theoretical orientation, which
are the three hierarchical levels proposed by the authors.
Besides defining the number of reference groups, the next step is to define who
they are. To identify them, there are three alternatives: (1) by random selection, (2)
via external restriction using an attribute or (3) fixing the degree of pertinence of a
subgroup in order to delineate the final profiles (i.e., estimation in two stages, as
8
Garcia et al. (2007) use a slightly different strategy. They define two extreme profiles representing the
ends of the ranking and calibrate the observations (municipalities) by means of the degree of pertinence,
gik. This implies a continuous hierarchy, differing from our calibration of three extreme profiles. The
additional profile in our calibration is a response to empirical findings from Costa and Brondizio (n.d.) of
important node cities representing municipalities with average population size. Even when we tried
different calibration, such as the deffuzification with k = 2, as proposed by the authors, our general
results did not change significantly.
123
Popul Environ (2009) 30:159–192
175
applied by Seplaki et al. 2004; Sawyer et al. 2002). In this paper, we used random
selection to freely describe the heterogeneity arisen from our sample. Random
selection is an attractive method for defining reference groups because it allows all
attributes to have equal weight. The other methods are more appealing only when
there is a consensus about the most important variable in delimiting the nature of a
reference group.
We selected seven groups of variables to include in the GoM model showed in
Table 4. The physical infrastructure variables, originally at the household level,
were transformed into proportions of households with the selected infrastructure
feature by municipality, creating a cumulative distribution that better fit the purpose
of defining hierarchy. All these variables were categorized as quantiles (quintiles
and deciles) along the cumulative distribution.9 The health care provision variables
were analyzed as a proportion of 1,000 habitants and were also categorized in
quintiles.
The estimated values of kkjl were generated by the GoM model. These values
represent the probability of a category of a variable to be part of each extreme
profile. This value was divided by the percentage of observations in the
correspondent category of the same variable in the whole sample. This ratio is
known as the Lambda-Marginal Frequency Ratio (LMFR). Operationally, each
lambda value (predicted probability) was divided by the relative marginal frequency
for each variable used in the analysis. Every time the LMFR C 1.2 for one category
of a variable, this category was considered to be dominant in that extreme profile.
Using a higher LMFR increases the likelihood of a given variable to not be selected
as part of a given profile (see Machado 1997). The threshold is arbitrary and
depends on the degree of heterogeneity one wants to capture in the sample (Sawyer
et al. 2002). The three extreme profiles thus were described according to the
categories of each variable with the LMFR C 1.2 (Table 1).
The algorithm for the mixed profiles
Mixed profiles are considered the core of GoM results. In the previous section, we
defined the extreme (pure) profiles. A profile is considered pure when its elements
have degrees of pertinence equal to one. Therefore, we can represent the pure types
(or extreme profiles) for the regional level as:
EPin ) gin ¼ 1 with n ¼ 1; 2; 3 and i ¼ 1; . . .; 747
The reference groups are, in general, profiles that might contain unique or rare
characteristics in a population. A precise definition of the mixed profiles is relevant
insofar as the majority of individuals in a population differ, with some degree, from
the pure types. Dissimilarity arises from the heterogeneity in the sample. In our
case, the three pure types represented 32.1% of the municipalities in the Legal
Brazilian Amazon, meaning that almost 70% of the Amazonian cities differ,
somehow and with a certain degree, from the reference groups.
9
For instance, if a selected urban section of the municipality had 23% of its urban households served
with garbage collection service, it was classified as belonging to the third decile of the garbage collection
service distribution (from 20 to 29%).
123
176
Popul Environ (2009) 30:159–192
By definition, the bigger the difference of attributes of any given element from
the attributes of a given extreme profile, the smaller the preponderance of that pure
type in its characterization. Because of that, the criterion of individuals clustered by
the preponderance of a specific pure type seemed more appropriate. Based on
predominance criteria, we established an algorithm that was able to define three
types of mixed profiles, combining different degrees of pertinence to the three
extreme profiles previously created:
(a)
Profiles of high preponderance (PHP):
PHPn ) 0:7 [ gin [ 1 with n ¼ 1; 2; 3 and i ¼ 1; . . .; 747
(b)
Profiles of relatively high preponderance (PRHP)
PRHPnm ) ð0:5 gin 0:7Þ \ ð0:1 gin 0:4Þ
with i ¼ 1; . . .; 747 and n; m ¼ 1; 2; 3jm 6¼ n
(c)
Profiles with relative pairwise predominance (PRPP)
PRPPnm ¼ gin þ gim 0:6666 \ ½ðgin \1:5gim Þ [ ðgim \1:5gin Þ
with
i ¼ 1; . . .; 747 and n; m ¼ 1; 2; 3jm 6¼ n
Interpreting multi-level urban hierarchies in the Amazon
The GoM fuzzy cluster model used seven groups of urban characteristics to generate
mixed profiles, characterizing Amazonian urban areas: demographic configuration
and dynamics, population movement, spatial dimensions, foundation date, physical
infrastructure, social development indicators, and geographical landmarks
(Table 4).
Table 3 summarizes the distribution of Amazonian municipalities by profile
(both extreme and mixed), according to the GoM analysis. The profile ‘‘Recent
Small Cities’’ clusters the poorest cities, generally only recently created, with those
presenting the worst urban infrastructure, the smallest average urban population
size, and also representing areas of out-migration. These cities are predominately
located close to roads. The profile ‘‘Historical Growing Small Cities’’ incorporates
intermediate urban areas, in terms of income, infrastructure, urban population size
and migration. Interestingly, these are municipalities that contain the largest area of
natural reserves and parks. This group also includes some of the oldest
municipalities in the Amazon. The profile ‘‘Medium Cities and Urban Agglomerations’’ represents the other extreme: the richest urban centers in relative regional
terms, concentrated at the upper levels of infrastructure and urban population size
distribution. These areas attract the largest amount of temporary workers and
students with their home residency in a different municipality and permanent
immigrants within the region, and generally include a relatively high level of health
care provision. These municipalities were founded mostly between 1950 and 1970
and have considerably smaller areas of natural reserves and parks.
123
Popul Environ (2009) 30:159–192
177
Table 3 Regional level: characterization of the pure types and marginal frequencies (absolute and
relative) of the mixed profiles—municipalities of the Legal Brazilian Amazon in 2000
Descripton of the extreme profiles
Profiles with
preponderance
Marginal
frequency
Absolute Relative
Recent small cities: Low income, low overall
percentage of urban households with fair
infrastructure, low supply of health services,
near roads, between 10 and 30% of the area
with natural reserve or national/state parks,
emancipated very recently (after 1990), small
number of habitants, low urban ratio and
relative small number of the in-migrants in the
Legal Brazilian Amazon
Recent small cities
(EP1)
92
12.3
2,291
Small cities with poor
infrastructure (PL1)
53
7.1
3,485
Small cities with fair
infrastructure (MP12)
37
5.0
4,528
Highly forested small
cities (MP13)
25
3.3
5,261
207
27.7
3,355
68
9.1
9,156
Growing small cities
with fair
infrastructure (PL2)
103
13.8
9,953
Highly growth small
cities (MP21)
22
2.9
6,032
Growing medium cities
(MP23)
31
4.1
12,645
Subtotal
Historical growing small cities: Average
income, intermediate overall percentage of
urban households with fair infrastructure,
average supply of health services, near rivers,
at last 60% of the area as natural reserve or
national/state park, small number of habitants,
average urban ratio, with high demographic
growth rate, emancipated before 1970 and
average relative number of in-migrants in the
Legal Brazilian Amazon
Historical growing
small cities (EP2)
Subtotal
Medium cities and urban agglomerations: High
income, high overall percentage of urban
households with fair infrastructure, high
supply of health services, \5% of the area
with natural reserve or national/state parks,
emancipated mainly between 1950 and 1970,
from small to large number of habitants, high
urban ratio, average demographic growth, and
relative large number of the in-migrants in the
Legal Brazilian Amazon
Average
size
(Pop.)
224
30.0
9,699
Medium/urban
agglomeration (EP3)
80
10.7
77,562
Medium cities with
good infrastructure
(PL3)
59
7.9
56,810
3
0.4
27,879
23
3.1
11,801
Subtotal
165
22.1
60,072
Relative pairwise
predominance
151
20.2
6,294
Total
747
100.0
18,379
Urbanized recent small
cities (MP31)
Small cities with
average
characteristics
(MP32)
Data sources: Brazilian Demographic Census (IBGE 1991, 2000), Brazilian population tally (2007),
Brazilian hospital information system and national archive of hospital establishments (MS 1998, 2002a,
b), Brazilian national council of municipalities (CMN 2007), Cartographic Database (IBGE 2007)
Figure 2 shows the profiles generated using the GoM model (profile characteristics are described in Table 3). The figure suggests regional differences in terms of
settlement ranking (large, medium, small) and spatial pattern (distance between
urban centers). The state of Amazonas, for instance, has one central city (Manaus,
123
178
Popul Environ (2009) 30:159–192
the state capital), followed predominantly by small, mostly distant, ones. The virtual
absence of medium-size cities in the state, in itself, departs from more ‘‘traditional’’
urban networks systems based on the articulation of large, medium, and small cities.
The state of Pará has cities of different sizes and levels of importance, and its most
important city, Belém, is located far from most of the others cities in the state. This
spatial pattern gives medium-size cities a significant function in relation to lower
level ones. In this sense, the disarticulated nature of the Amazon’s urban system
arises from the lack of evidence of a hierarchical order and spatial pattern of central
cities positioned within their surroundings. This confirms Browder and Godfrey’s
(1997) hypothesis of disarticulated urbanization in the region. We also found a
pattern of regionalization of cities with similar characteristics. These clusters of
‘‘similar’’ cities do not perfectly coincide with political boundaries, however. Thus,
we can roughly categorize the Legal Brazilian Amazon into four main groups: (a)
Acre, Amazonas and the Northwest of Pará; (b) Roraima; (c) Rondônia, Mato
Grosso, Amapá, and the center-west of Pará, and (d) Eastern Pará, Maranhão, and
Tocantins. These four areas approximately correspond to the ones proposed by Perz
(2000).
Defining the regional and sub-regional levels
Based on the previous discussion and findings from the first stage of GoM profiles,
we reclassified the Amazonian cities into seven different levels of urban hierarchy
Fig. 2 Amazonian cities by profiles of socio-demographic and geographical dimensions (profiles
explained in Table 1)
123
Popul Environ (2009) 30:159–192
179
Fig. 3 Urban hierarchy based on the GoM profiles for the Legal Brazilian Amazon
(Fig. 3): (a) regional first level (Belém, Manaus, Cuiabá, Porto Velho, Macapá, and
Boa Vista); (b) regional second level (São Luı́s, Rio Branco, and Palmas); (c) subregional first level (the cities of Imperatriz, Araguaı́na, Ji-Paraná, Marabá,
Santarém, Altamira and Rondonópolis, plus 68 other cities); (d) sub-regional
second level (211 cities); (e) local first level (224 cities); (f) local second level (56
cities), and (g) local third level (173 cities).10
The disarticulated nature of urbanization in the Legal Brazilian Amazon
suggested by Browder and Godfrey (1997) can be further visualized in Fig. 3. Some
small urban areas of limited importance offer the only urban alternative for some
populations in hundreds of square kilometers. Similarly, important sub-regional
cities are ‘‘disconnected’’ from regional urban centers, creating a pattern that does
not correspond to Chirstaller’s theory (1966). This disarticulation also creates
unexpected linkages within and between cities and between different states, calling
attention to the limits of using municipal area boundaries as units of analysis. The
state of Amazonas, as mentioned before, is a good example of this uneven order of
distribution of settlements: 90% of cities have less then 50,000 inhabitants and only
one city is considered as a regional first level city.
10
The first regional level was based on the main capitals included in the extreme profile, ‘‘Medium Cities
and Urban Agglomerations’’, plus Belém. The second regional level incorporated the other capital cities
in the same extreme profile. The first sub-regional level was based on the remaining cities classified as a
member of the previous extreme profile. The second sub-regional level included the cities classified in the
mixed profile, ‘‘Medium cities with good infrastructure’’. The first local level includes cities with the
highest urban population growth rates. The second local level includes cities with average urban
population growth rates. The third local level represents cities with the lowest population growth rate.
123
180
Popul Environ (2009) 30:159–192
Despite the lack of evidence of a traditional hierarchy at the regional level, it is
possible to see the rise of sub-regional hierarchies centered on node service cities in
different parts of the region (Costa and Brondizio n.d.). For example, Santarém is a city
with sub-regional urban relevance that is surrounded by cities with a lower level urban
hierarchy, such as Belterra, Aveiros, Monte Alegre, Rurópolis, and Placas (Fig. 4).
The emergence of these sub-regional node cities is the result of a growing
physical and functional connectivity and the consequence of deficient services and
economic conditions for a large group of small cities. These deficiencies increase
the level of inter-dependency between cities and between rural and urban areas,
which reinforce the emergence of these node service cities and consolidate their
importance over time. Because they are surrounded by towns, rural areas and
villages with deprived infrastructure, these sub-regional node cities inevitably
attract an influx of immigrants, both permanently and seasonally, from these areas,
as can be seen in Fig. 4. The majority of people going to Santarém do so in order to
work or study, often from small nearby cities in other municipalities, such as
Belterra, Prainha, and Alenquer.
Defining the local level
In a 1978 study of rural communities (povoados) along the Belém-Brası́lia highway,
Becker (1978) proposed a methodology to differentiate levels of hierarchy and
importance among villages established between 1953 and 1963 along this corridor.
Her indicators ranged from basic functions such as schools, churches, and
Fig. 4 Commuting movement to Santarém, state of Pará, 2000
123
Popul Environ (2009) 30:159–192
181
cemeteries, to distinct activities such as the availability of specialized stores (e.g.,
veterinary products) and processing plants (e.g., dairy). Population size and density
also were used as an indicator of hierarchy.
Our study of local level hierarchy among rural communities uses comparable
variables to those proposed by Becker. Similar to our above analyses, we apply the
grade of membership (GoM) model using a wide array of variables, including
infrastructure and services (see Table 4).
Ranking communities by population size, Mojuı́ dos Campos stands as the most
populated community in the region. Other communities, such as Secretária, Vista
Alegre and Limão Grande, also appear prominently in this analysis, when ranked in
terms of population size and density. Our GoM analysis (Fig. 5), on the other hand,
allowed us to test the role of infrastructure and service variables on the delineation
of a local level hierarchy. The model found differences not only in terms of
population size, but also as a function of the availability of services such as health
centers, post office, banks, gas station, and commerce. Community age was
generally associated with a higher level of services and population size at the local
level. However, communities created in the same period showed different levels of
services and population size depending on the municipality in which they were
located. For example, old communities located in the municipality of Santarém
provided better services, such as the presence of a bank post office and health
centers, than old communities located in Belterra and Monte Alegre.
This finding suggests that the overall position of a settlement in a hierarchy
depends on its entire zone of influence, including which sub-areas it affects (lower
order settlements) and which supra-area it depends on for more complex service
provision (higher order settlements). This result may be taken in perspective,
because our local level survey comprises a specific area in the Amazon, considered
by Browder and Godfrey (1997) as part of the populist frontier. On the other hand,
the recent participation of foreign capital in the region (Becker and Léna 2002;
Liberal 2002), with the creation of a new harbor in Santarém and its connection with
the epicenter of soybean production in Brazil through the BR-163 highway, makes
the region an hybrid case of populist and corporativist frontier.11
Discussion
Methodological remarks
In our study, we combined datasets from different sources and scales using a
multilevel approach in order to understand the process of urbanization and hierarchy
11
According to Browder and Godfrey (1997), the main difference between a populist and a corporativist
frontier is the degree of capital penetration and the scale of production, although the authors consider other
elements which encompass such distinction. While Santarém is part of the government project of small-scale
agricultural colonization scheme back to the 1970s, the recent introduction of large scale soybean production
and export is redefining its orientation towards the national and global economy. This recent penetration of
agribusiness has developed in tandem with surviving smallholders who contribute significantly to regional
food production but whose production is mostly based on limited access to technologies.
123
182
Popul Environ (2009) 30:159–192
Fig. 5 Settlement hierarchy at the local level
of urban areas in the Brazilian Amazon. Our findings suggest that finer scale data
adds constructively to the understanding of urban process in the region, as in the
emergence of node cities at the sub-regional level and in the prominence of
communities in-between urban centers with a micro-hierarchical position in the
provisioning of services and social activities at the local level. The findings are also
suggestive of sensitivity of city position along the urban ranking to unit boundaries.
We used a fuzzy cluster analysis to generate profiles of urban areas along an
urban hierarchy (according to variables described below in Table 4). As most
variables considered important determinants of urban hierarchy are endogenous to
the hierarchical position and dynamics of a city or community hierarchy over time,
we decided to apply a non-parametric cluster analysis. In addition, the option for a
fuzzy technique was considered in order to test the hypothesis of a disarticulated
urban hierarchy. As each category of each variable for a single urban area is the
element of clustering, the same urban area can have part of its attributes allocated to
one group of extreme urban areas while other categories of the same urban area’s
attribute can be allocated to a different extreme profile. The partition of its attributes
results in different degrees of pertinence for the same urban area to different
extreme profiles. As a result, small cities with high level of infrastructure can be
clustered close to large, main cities in an urban hierarchical typology. For example,
as described above, the municipality of Altamira, a remarkably large municipality
with a small urban area and with most of its territory covered by reserves, had its
123
Popul Environ (2009) 30:159–192
183
urban perimeter classified in a similar relative position in terms of urban hierarchy
by the GoM model as the urban perimeter of Belém, although Belém is the main and
biggest municipality in the State of Pará, and also the only metropolitan area of the
Legal Brazilian Amazon.
Part of the explanation for this case, and an issue of methodological relevance for
Amazonian population-environment studies, arises from the unit of analysis applied:
the spatial distribution of infrastructure and population in Belém is shared with other
municipalities of the metropolitan areas, creating a singular functional entity.
However, in our model, we used the urban area of Amazonian municipalities instead
of metropolitan areas as unit of analysis. This is why some cities appear in the same
hierarchical level in our analysis as Manaus and Belém, while in other studies they
are allocated to different levels of hierarchy (IPEA 2002). The sensitivity of a
typology to the unit of analysis is a relevant dimension of our results. Garcia et al.
(2007), for example, generalize the urban reality when using the entire municipality
as the unit of analysis. Changing the unit modifies the relative position of some
cities in the urban hierarchy when compared to the classification proposed by the
previous authors, as it will be discussed in the next section. This difference in the
spatial unit is fundamental when considering an urban hierarchical typology, insofar
as discussed above, most Amazonian municipalities comprise a large territory but
have a very small urban area. This heterogeneity must be explicitly modeled in any
effort when creating an urban hierarchical typology.
This work also contributes to the study of urbanization in the Amazon by
combining datasets representing different levels of analysis and using diverse
techniques of data collection and processing. Drawing on the methodological
tradition of multi-method approaches for social science research (Axinn and Pearce
2006; Ragin 1987; Pearce 2002; Brondizio 2005, 2006), we argue that combining
large-scale secondary data with local survey data based on a combination of
structured methods with ethnographic approaches for data collection (such as direct
observation, semi-structured interviews and participatory rural appraisal) can
considerably add to the understanding of multi-level and multi-causal phenomena.
First, a local scale mixed-method research design allows deeper understanding of
local reality for a fixed budget and time constraint; though it requires direct
involvement of the researcher with his/her object of analysis. Second, it adds more
flexibility to local specificities, which is less likely in more standardized large-scale
surveys. Third, it is better suited for dealing with multiple causation, partially
because of the less structured nature of the research design (from sampling design to
data analysis), including direct participation and physical presence in the research
site. Third, the combination of different approaches and levels of analysis strengthen
the final analysis by balancing the weakness of one method or approach with the
strength of the other.
More specifically, incipient sub-urban systems and intra-urban variations are not
captured in large-scale surveys, such as demographic census and administrative
records. The use of our ethnographic based survey allows us to observe the
organization of incipient and more established communities within municipalities,
shedding light on the principles underlying local level settlement systems and
adding to the understanding of urbanization dynamics. Local scale surveys also may
123
184
Popul Environ (2009) 30:159–192
contribute to future efforts of modeling urban systems formation or fragmentation in
the Amazon. Moreover, local scale surveys might be an important future source for
local development program evaluation regarding forest resource management and
the role of the new Amazonian commodities, such as the Açaı́ berry and soybeans,
on rural sustainability, environmental consequences and urban dynamics in the
forthcoming decades (Brondizio 2008).
Urban hierarchies and implications for urban development and environment
Our findings suggest that three main factors influence urbanization at the level of
communities: population size, quality of infrastructure, and availability of services.
While comparison of population size alone is limited for differentiating hierarchical
levels, it does correlate, although not linearly, to level of functional complexity, as
the results shown in Fig. 5 suggest. Our data show that a threshold of 900
inhabitants indicates the presence of specialized services and, thus, a higher position
in the hierarchy. Communities with houses built from durable material, access to
some form of treated water and sanitation, and services such as a health post, appear
higher in the urban hierarchy. On the other extreme, there are several young, small,
and precarious communities with limited resources to its residents, implying that the
availability of even small additional services is enough to elevate a community’s
position in the hierarchy.
No clear hierarchical pattern seems to emerge at the local level, as there are
similar numbers of communities within each hierarchical category. This is not
totally surprising given that many communities receive similar levels of support
from municipalities and can access services in urban centers such as Santarém.
There are, however, large and important communities located away or in-between
regional urban centers. These communities provide important services to other
surrounding communities and farms. Becker’s (1978) study of rural community
hierarchy along the Belém-Brası́lia highway, for example, demonstrated that village
hierarchy was inversely related to proximity to towns and cities. In other words, the
closer a village is located to a larger town, the lower its hierarchy is in relation to
other villages. Villages at longer distances from towns increase their local
importance and their role in providing services to their surroundings. In a region
where distances and travel conditions are significant constraints, communities inbetween urban centers assume a micro-hierarchical position in the provisioning of
services and social activities.
Our model, both theoretical and empirical, emphasizes the heterogeneity of the
local level, not only at the level of communities, but also at the level of small cities.
As discussed above, most of Amazonian cities are classified as small cities, and
many are located in areas where accessibility is precarious. Nonetheless, as also
observed among rural communities, some of these cities, because of their proximity
to sub-regional urban areas, stand out as relatively important within the local level.
This within-local level relevance (micro-hierarchical position) pushes these small
cities up in the hierarchical urban scale, increasing the heterogeneity of this local
level, neglected in classifications using only population size or density.
123
Popul Environ (2009) 30:159–192
185
If we turn our attention to cities at the sub-regional level, our results suggest a
more traditional hierarchy. At this level, node cities, as called by Costa and
Brondizio (n.d.), appear in our model as a reference for a set of small surrounding
cities. They function as central places to provide services, such as job opportunities,
schools and health services, and to attract temporary workers and students who
reside in different municipalities. They also have an important position as receptors
of migrants coming from the surrounding areas. The relative isolation of some small
cities to larger urban centers reinforces their dependence on these sub-regional node
cities. These node cities, therefore, play a central role within the Amazonian region,
as they often represent the only connection to an urban reality for rural residents and
those living in impoverished villages and towns.
At the regional level, different from the macro-poles suggested by Garcia et al.
(2007), the capitals of the Amazonian States were, in this article, divided in two
distinct urban hierarchical sub-levels: Belém, Manaus, Cuiabá, Porto Velho,
Macapá, and Boa Vista are at the top of urban hierarchy in the region, followed by
São Luı́s, Palmas, and Rio Branco as the second most important hierarchical urban
cities. From a methodological viewpoint, the inclusion of the entire municipality in
the study referred above as the unit of analysis does not allow the unobserved
heterogeneity to emerge not just at the local, but also at the regional level, as our
typology suggests when separating the capitals in two different levels or regional
importance.
The urban hierarchy proposed in our analysis supports Browder and Godfrey’s
theory of disarticulated urbanization as it applies to the level of the Brazilian
Amazon. However, we find it is insufficient to explain the emergence of subregional urban hierarchies that increasingly define the region in terms of population
distribution and economic connections to national and global markets. Our
empirical model highlighted some medium cities, such as Altamira and Marabá
in Pará, Barra dos Garças in Mato Grosso, Araguaı́na in Tocantins, Ji-Paraná in
Rondônia, Santana in Amapá, Humaitá in Amazonas and Cruzeiro do Sul in Acre,
which are not considerable in terms of population size, but are representative in
terms of other urban dimensions, such as infrastructure and services and connections
to national and international markets.
Our results also emphasize the singularity of urban context in the Amazon region,
as noted by Corrêa (1987) and Browder and Godfrey (1997), and complement the
interpretation of Garcia et al. (2007) of regional and sub-regional urban hierarchies.
Nevertheless, we call attention to the importance of other determinants of urban
hierarchies such as date of municipality creation and types of access, such as rivers
and roads. Our study is also distinct in using only the urban area and not entire
municipalities as the unit of analysis. In addition, the inclusion of a local level
dataset allowed us to offer insights about the processes leading to the initial
formation of urban centers in areas with high rates of migration, land turnover, and
close rural–urban movements and connections.
Regionalization models, on the other hand, while contributing to the differentiation of intra-regional historical and social particularities within continental
Amazonia, are very sensitive to the (inclusive) spatial configuration of municipalities, as units of analysis, and thus can lose empirical value. The inclusiveness
123
186
Popul Environ (2009) 30:159–192
(historical and cultural, environmental and geographical, social, political, and
economic) of each regional block defies its own categorical meaning. In this sense,
the aggregation of cities into wider categories of urban regional blocks masks their
differential local importance. Our findings, thus, suggest that even small variations
in terms of urban hierarchy must be taken into consideration and seen in relative
perspective, because of its impact for the population living in these cities and in
their surroundings.
Finally, we would like to call attention to the environmental implications of
these processes. Along with the exponential growth of urban areas since the late
1980s, the region has seen a similar growth of both reserves and conservation
areas and agro-pastoral activities. The confluence of these systems, their spatial
overlaps and adjacencies, and their respective institutional arrangements are
defining the future axis of occupation and connectivity within the region. The
growing network of urban areas in the region is contributing to a situation of high
level of connectivity between land use systems and social groups within and
between ecosystems and watersheds (Brondizio et al. 2009). As a result, the
region is starting to witness blocks of protected areas surrounded by urban and
agrarian systems, thus creating ‘island conservation’ effects and fragmentation of
habitats. The propagation of impacts within such situations also increases,
particularly given the distribution and size of important watersheds in the region.
The case of the Indigenous Park of Xingu provides an illustrative example. Today,
the watershed of the Xingu River (which comprises around 51 million ha) cuts
across 35 municipalities (with a population close to half a million) and
encompasses 27 indigenous groups (with a population over 10,000). While
indigenous groups within the park have developed strong institutions to monitor
its border successfully, high rates of deforestation and a complex network of roads
and cities around the park are undermining the park’s environment with water
pollution, soil erosion, and forest fires. This is a situation which speaks to many
other areas of the Amazon, and which is likely to increase.
Intra-regional connectivity and urban network complexity create new challenges
for conservation and regional planning. Projected scenarios of climate change in the
region raise further concerns about the impact of urban network expansion in the
region and vice versa, the impact of climate change on urban populations. Urban
populations are affected by the spread of accidental fire and the lack of water during
extended droughts such as those in 1997/1998 and in 2005. These droughts have
directly affected the level of pollution in cities such as Manaus and Belém, largely
from smoke from nearby burning areas, resulting in respiratory problems for
residents, as well as decreased water quality, an already important problem.
Understanding the forms and fronts of urban network expansion and their
intersection with conservation areas and expanding land use systems is an important
component of any program aimed at improving the quality of life for regional
populations and finding sustainable solutions for reconciling conservation and
development in the Amazon.
123
Popul Environ (2009) 30:159–192
187
Concluding remarks
Our findings suggest that the proposed disarticulated urbanization holds for the
Brazilian Amazon at the regional level, but is insufficient to explain urban
hierarchies emerging at sub-regional levels. Our urban ranking identifies subregional urban centers important for their provisioning of services and infrastructure
to their adjacencies, despite their relatively modest population size. These cities
function as node cities, linking the local realities to national and, in some instances,
global economies. Some of these node cites provide specialized functions to their
neighboring areas, rendering them important to surrounding cities of different sizes.
Micro-hierarchical specialized functions were also identified at the local level
among communities in-between urban centers.
Understanding the trajectories of Amazonian cities and their position and
dynamics within a regional urban hierarchy offers an instrument to predict new axes
of expansion and the rise of inter-urban networks. Furthermore, the absence of an
articulated regional urban hierarchy calls attention to the importance of analyzing
these processes at sub-regional and local levels. The importance of sub-regional
node cities is increasing in different parts of the Amazonian region as they supply
services and assume a position of political and economic relevance for surrounding
rural areas and cities and may be a reflective process initiated at the micro-level, as
illustrated by node communities.
Our analysis is an effort to motivate research and new studies focused on the
emergence of regional, sub-regional, and local inter-urban networks, including how
communities and incipient urban centers emerge and change over time. These
dynamics underlie the future direction of urban expansion and its consequences for
population and environment in the region during the coming decades.
Acknowledgments We thank Anthony Cak, Scott Hettrick, Álvaro D’Antona, and Andrea Siqueira for
logistical support in the field and laboratory, and constructive reviews of this article. We thank four
anonymous reviewers and the editor for their valuable comments and suggestions. The larger project of
which this is a part benefits from the experience and input of these investigators, and funding from the
National Institutes of Health (grant no. HD35811-04/07, Phase II),National Aeronautics and Space
Administration (NASA)/LBA (Grant no. NNG06GD86A, Phase III) and National Science Foundation/
USD (grant no. BCS-05-27578, Estuary). We finally thank Coordenação de Aperfeiçoamento de Pessoal
de Nı́vel Superior (CAPES), the Department of Anthropology, the Antrhopological Center for Training
and Research on Global Environmental Change (ACT) at Indiana University, the Centro de
Desenvolvimento e Planejamento Regional e Urbano (CEDEPLAR/UFMG), and Departamento de
Planejamento Regional (UNIVAP), institutions which the authors belong or collaborate.
Appendix
See Table 4.
123
188
Table 4 Variables used in the
fuzzy cluster models in order to
establish an urban hierarchy in
the Legal Brazilian Amazon
Popul Environ (2009) 30:159–192
I. Demographic configuration and dynamics
Urban population (2000)a
Community population (2004)f
Urban population density (2000)a
Community population density (2004)f
Urban ratio (2000)a
Change in urban ratio (1991/2000/2007)*a,b
Average urban population growth (1991/2000/2007)*a,b
II. Population linkages
In-migrants in urban areas of municipalities (2000)a
Out-migrants from urban areas of municipalities (2000)a
Commuting movement [from urban/rural to urban] (2000)a
III. Spatial dimensions
Countryside/metropolitan area (2000)a
Urban perimeter (2000)a
Total area of the municipality (2000)a
Total area of the community (2004)f
Municipality of community location (2000)a
IV. History of creation
Year of municipality creation (2007)c
Year of community creation (2004)f
V. Social development
Total urban household income (2000)a
Hospital beds per 1,000 habitants (2002)d
Change in hospital beds for 1000 habitants (1998/2002)d
Emergency rooms per 100,000 habitants (2002)d
Change in emergency rooms per 100,000 habitants (1998/2002)d
Type of health establishment (2002)e
Level of health attention [primary, secondary or terciary] (2002)e
Outpatient facilities per 1,000 habitants (2002)e
Number of hospitals in the community (2004)f
Number of health centers in the community (2004)f
Number of health posts in the community (2004)f
Number of school establishments in the community (2004)f
Number of shops in the community (2004)f
Number of churches in the community (2004)f
Number of soccer fields in the community (2004)f
Number of public squares in the community (2004)f
Number of post offices in the community (2004)f
Number of lottery stores in the community (2004)f
Presence of banks in the community (2004)f
Presence of gas station in the community (2004)f
Number of neighborhood associations (2004)f
Number of bakeries in the community (2004)f
123
Popul Environ (2009) 30:159–192
Table 4 Variables used in the
fuzzy cluster models in order to
establish an urban hierarchy in
the Legal Brazilian Amazon
189
VI. Physical infrastructure
Type of urban household [permanent, temporary or collective]
(2000)a
Material used in community household construction (2004)f
Type of water supply system in urban households (2000)a
Type of water supply system in community households (2004)f
Availability of piped water in urban households (2000)a
* The minimum area of
comparison was respected when
analyzing municipalities over
time
Source: a IBGE Demographic
census (IBGE 1991, 2000)
b
IBGE population tally (IBGE
2007)
c
National council of
municipalities (CMN 2007)
d
MS hospital information
system (MS 1998, 2002b)
e
MS national archive of health
establishments (MS 2002a)
f
ACT santarém community
survey (ACT 2004)
Type of water treatment in community households (2004)f
Type of sewage system in urban households (2000)a
Type of sewage system in community households (2004)f
Type of garbage collection in urban households (2000)a
Type of garbage collection in community households (2004)f
Existence of electric power in urban households (2000)a
Existence of electric power in community households (2004)f
Existence of public light posts for urban households (2000)a
Existence of public light posts for community households (2004)f
Degree of paved streets for urban households (2000)a
VII. Geophysical landmarks
Presence of river and/or road in the municipality (2007)c
Area of municipality under indigenous reserves and national/state
parks (2007)c
References
Amorim Filho, O., & Serra, R. V. (2001). Evolução e perspectivas do papel das cidades médias no
planejamento urbano e regional. In T. A. Andrade & R. V. Serra (Eds.), Cidades me´dias brasileiras
(pp. 107–123). Rio de Janeiro: IPEA.
Anthropological Center for Training and Research on Global Environmental Change (ACT). (2004).
NIH-NICHD project.
Armstrong, W., & McGee, T. G. (1985). Theatres of accumulation: Studies in Asian and latin American
urbanization. London and New York: Methuen.
Axinn, W. G., & Pearce, L. D. (2006). Mixed method data collection strategies. New York: Cambridge
University Press.
Becker, B. K. (1978). Uma hipótese sobre a origem do fenômeno urbano numa fronteira de recursos do
Brasil. Revista Brasileira de Geografia, 40(1), 111–122.
Becker, B. K. (1985). Fronteira e urbanização repensadas. Revista Brasileira de Geografia, 47(3/4),
357–371.
Becker, B. K. (2005). Geopolı́tica da Amazônia. Estudos Avançados, 19(53), 71–86.
Becker, B. K., Léna, P., Pequenos Empreendimentos Alternativos na Amazônia. (2002). Rede de Sistemas
Produtivos e Inovativos Locais. UFRJ, 2002. Available at:http://www.unifap.br/ppgbio/doc/
Bertha%20Becker.pdf. Accessed 13 Oct 2008.
Brondizio, E. (2005). Intraregional analysis of land use change in the Amazon. In E. Moran & E. Ostrom
(Eds.), Seeing the forest and the trees: Human-environment Interactions in forest ecosystems
(pp. 223–252). Cambridge: MIT Press.
Brondizio, E. S. (2006). Landscapes of the past, footprints of the future: Historical ecology and the
analysis of land use change in the Amazon. In W. Balée & C. Erikson (Eds.), Time and complexity in
historical ecology: Studies in the neotropical lowlands (pp. 365–405). New York: Columbia U.
Press.
Brondizio, E. S. (2008). The Amazonian caboclo and the Açaı´palm: Forest farmers in the global market.
New York: New York Botanical Garden Press.
123
190
Popul Environ (2009) 30:159–192
Brondizio, E. S., Ostrom, E., Young, O. (2009). Connectivity and the governance of multilevel socioecological systems: The role of social capital. Annual Review of Environment and Resources, 34.
Avaiable at: http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.environ.020708.100707
Browder, J. D. (2002). The urban-rural interface: Urbanization and tropical forest cover change. Urban
Ecosystem, 6, 21–41.
Browder, J. D., & Godfrey, B. J. (1996). Disarticulated urbanization in the Brazilian Amazon.
Geographical Review, 86(3), 441–445.
Browder, J. D., & Godfrey, B. J. (1997). Rainforest cities: Urbanization, development and globalization
of the Brazilian Amazon. New York: Columbia University Press.
Brum, A. J. (1988). Modernizacao da agricultura: Trigo e Soja. Petropolis: Editora Vozes.
Cassady, F., Pieper, C. F., & Carrol, B. J. (2001). Subtypes of mania determined by grade of membership
analysis. Neuropsychopharmacology, 25(3), 373–383.
Christaller, W. (1966). Central places in southern Germany. New Jersey: Prentice Hall.
Clark, D. (1982). Urban geography. Baltimore: Johns Hopkins Press.
Clark, D. (2003). Urban world/global city. London: Taylor and Francis Group.
Confederação Nacional dos Municı́pios (CMN). (2007). Histórico dos Municı´pios. Available via
http://www.cnm.org.br/municipio/info_observacao.asp?iIdInfo=2. Cited 12 Dec 2007.
Corrêa, R. L. (1987). A periodização da rede urbana da Amazônia. Revista Brasileira de Geografia, 4(3),
39–68.
Corrêa, R. L. (1991). Região e Organização Espacial. São Paulo: Ática.
Costa, S. M., & Brondizio, E. S. (n.d.). Floodplain cities of the Brazilian Amazon. In M. Pinedo-Vasquez,
M. Ruffino, E. S. Brondizio, & C. Padoch (Eds.), Development and conservation of the Amazonian
floodplains: The decade past and the decade ahead. Berlin/New York: Springer/The New York
Botanical Garden Press.
Fearnside, P. M. (2003). Conservation policy in Brazilian Amazonia: Understanding the dilemmas. World
Development, 31(5), 757–779.
Garcia, R. A., Soares-Filho, B. S., & Sawyer, D. O. (2007). Socioeconomic dimensions, migration, and
deforestation: An integrated model of territorial organization for the Brazilian Amazon. Ecological
Indicators, 7(3), 719–730.
Gohn, M. G. (1999). O futuro das cidades. Available via http://www.lite.fae.unicamp.br/revista/
gohn.html. Cited 4 Jan 2008.
Goodman, D., Sorj, B., & Wilkinson, J. (1984). Agro-industry, state policy, and rural social structures:
Recent analyses of proletarianisation in Brazilian agriculture. In B. Munslow & H. Finch (Eds.),
Proletarianisation in the third world: Studies in the creation of a labour force under dependent
capitalism. London: Croom Helm.
Hall, P. (2002). Christaller for a global age: Redrawing the urban hierarchy. In A. Mayr, M. Meurer, & J.
Vogt (Eds.), Stadt und region: Dynamik von Lebenswelten, Tagungsbericht und wissenschaftliche
Abhandlungen, 53. Deutsche Gesellschaft für Geographie, pp. 110–128. Available via. http://
www.uniduisburg.de/FB6/geographie/Studium/Lehrveranstaltungen/SS2003/Schmitt/3_Leseprobe_
14.5.pdf.
Henderson, V. (1997). Medium size cities. Regional Science and Urban Economics, 27, 583–612.
Instituto Brasileiro de Geografia e Estatı́stica (IBGE). (1991). Censo Demográfico, Rio de Janeiro (micro
data).
Instituto Brasileiro de Geografia e Estatı́stica (IBGE). (2000). Censo Demográfico, Rio de Janeiro (micro
data).
Instituto Brasileiro de Geografia e Estatı́stica (IBGE). (2007). Contagem populacional, Rio de Janeiro
(available in CD-ROM).
Instituto de Pesquisa Econômica Aplicada (IPEA). (2002). Caracterização e tendeˆncias da rede urbana
do Brasil—Redes urbanas regionais: Norte, Nordeste e Centro-Oeste. Brası́lia: IPEA.
Katzman, M. T. (1976). Paradoxes of Amazonian development in a ‘‘resource-starved’’ world. Journal of
Developing Areas, 10, 445–459.
Lamb, V. L. (1996). A cross-national comparison of quality of life factors associated with partners of
elderly disablement. Social Science and Medicine, 42(3), 363–367.
Liberal, O. (2002). Novo porto vai mudar a cara de Santare´m (PA). Jornal O Liberal. Published in 04 Nov
2002.
Lira, S. R. B. (2008). A Crise do Estado Brasileiro e o Financiamento do Desenvolvimento da Amazônia.
Revista Econômica do Nordeste, 39(1), 7–24.
123
Popul Environ (2009) 30:159–192
191
Machado, L. O. (1989). The intermittent control of the Amazonian territory (1616–1960). International
Journal of Urban and Regional Research, 13(4), 652–665.
Machado, L. O. (1994). A Fronteira Agrı́cola na Amazônia Brasileira. Revista Brasileira de Geografia,
54(2), 27–55.
Machado, C. J. (1997). Perfis de morbi-mortalidade infantil no Estado de São Paulo, 1994: uma
aplicação de grade of membership a análise de causas multiplas de morte. University of Minas
Gerais, Belo Horizonte (M.Sc. thesis). Available via. http://www.bibliotecadigital.ufmg.br/
dspace/bitstream/1843/MCCR-76LLWD/1/carla_machado_dissert_pop_1997.pdf.
Machado, L. O. (1999). Urbanização e mercado de trabalho na Amazônia Brasileira. Cadernos do
IPPUR/UFRJ, 13(1), 110–137.
Manton, K. G., Woodbury, M. A., & Tolley, H. D. (1994). Statistical application using fuzzy sets. Nova
York: Wiley.
Martine, G., & Turchi, L. (1988). A urbanização da Amazônia: realidade e significa. In Encontro
Nacional da ABEP, 6. Olinda, Anais, Belo Horizonte: ABEP, 2, 161–189.
Ministério da Saúde (MS). (1998). Sistema de Informação Hospitalar. Rio de Janeiro (available in CDROM).
Ministério da Saúde (MS). (2002a). Arquivo Nacional dos Estabelecimentos Hospitalares. Rio de Janeiro
(available in CD-ROM).
Ministério da Saúde (MS). (2002b). Sistema de Informação Hospitalar. Rio de Janeiro: 2002 (available in
CD-ROM).
Motta, R. S. (2002). Estimativa do Custo Econômico do Desmatamento na Amazônia. IPEA Working
Paper 910.
Mushinski, D., & Weiler, S. (2002). A note on the geographic interdependencies of retail market areas.
Journal of Regional Science, 42(1), 75–86.
Padoch, C., Brondizio, E. S., Costa, S., Pinedo-Vasquez, M., Sears, R., Siqueira, A. (2008). Urban forest
and rural cities: Multi-sited households, consumption patterns, and forest resources in Amazonia.
Ecology and Society, 13(2): 2 [online]. URL: http://www.ecologyandsociety.org/vol13/iss2/art2/.
Pearce, L. D. (2002). Integrating survey and ethnographic methods for systematic anomalous case
analysis. Sociological Methodology, 32(1), 103–132.
Perz, S. G. (2000). The quality of urban environments in the Brazilian Amazon. Social Indicators, 49,
181–212.
Portrait, F., Lindeboom, M., & Deeg, D. (1999). Health and mortality of the elderly: The grade of
membership method, classification and determination. Health Economics, 8(5), 441–457.
Portrait, F., Lindeboom, M., & Deeg, D. (2001). Life expectancies in specific health states: Results from a
joint model of health status and mortality of older persons. Demography, 38(4), 525–536.
Ragin, C. C. (1987). The comparative method. Berkeley and Los Angeles: University of California Press.
Roberts, J. T. (1991). Forging development, fragmenting labor: Subcontracting and local response in an
Amazon boomtown. Ph.D. dissertation, Johns Hopkins University.
Rocha, A. P. (1968). Bele´m do Pará (estudo da geografia urbana) (Vol. 1). Belém: Universidade Federal
do Pará. (Coleção Amazônia, Série José Verı́ssimo).
Santos, M. (1997). Metamorfoses do espaço habitado. São Paulo: HUCITEC.
Sawyer, D. (1984). Frontier expansion and retraction in Brazil. In M. Schmink & C. Wood (Eds.),
Frontier expansion in Amazônia (pp. 180–203). Gainesville: University of Florida Press.
Sawyer, D. (1987). Urbanização da fronteira agrı́cola no Brasil. In L. Lavinas (Ed.), A Urbanização da
fronteira (Vol. 1, pp. 43–55). Rio de Janeiro: Edições Publipur.
Sawyer, D., & Carvalho, J. (1986). Os Migrantes em Rio Branco, Acre: Uma Análise a Partir de Dados
Primários. In L. Aragón & L. Mougeot (Eds.), Migrações Internas na Amazônia: Contribuições
Teóricas e Metodológicas (pp. 112–147). Belém: UFPA, NAEA, CNPq.
Sawyer, D. O., Leite, I. C., & Alexandrino, R. (2002). Perfis de utilização de serviços de saúde no Brasil.
Cieˆncia & Saúde Coletiva, 7(4), 757–776.
Seplaki, C. L., Goldman, N., Weinstein, M., & Lin, Y. (2004). How are biomarkers related to physical
and mental well-being? Journal of Gerontology: Biological Sciences, 59A(3), 201–217.
Shonkwiler, J. S., & Harris, T. R. (1996). Rural retail business thresholds and interdependencies. Journal
of Regional Sciences, 36(4), 617–630.
Simmons, C., Sorrensen, C., & Walker, R. (2002). Urban rural linkages and environmental change:
Addressing the human dynamics of urban ecologies. Urban Ecosystems, 16(1–2), 5–8.
Sombroek, W., & Carvalho, A. S. (2000). Macro- and micro ecological-economic zoning in the Amazon
region history, first results, lessons learnt and research needs. German-Brazilian Workshop on
123
192
Popul Environ (2009) 30:159–192
Neotropical Ecosystems—Achievements and Prospects of Cooperative Research, Hamburg,
Germany.
Turner, F. J. (1920). The frontier in American history. New York: Holt.
Vicentini, Y. (2006). Cidade e história na Amazônia. Curitiba: Editora da Universidade Federal do
Paraná.
Wagley, C. (1953). Amazon town: A study of man in the tropics. London: Oxford University Press.
Woodbury, M. A., Clive, J., & Garson, A., Jr. (1978). Mathematical typology: A grade of membership
technique for obtaining disease definition. Computers and Biomedical Research, 11, 277–298.
123
ACT Publications 2008
No. 08-01
D’Antona, A. de O., A.D. Cak and L. VanWey. Collecting Sketch Maps to Understand Property Land Use and Land
Cover in Large Surveys. Field Methods 20:66-84.
No. 08-02
Lu, D., M. Batistella, E.F. Moran and E.E. de Miranda. A Comparative Study of Landsat TM and SPOT HRG Images
for Vegetation Classification in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing 74(3):311321.
No. 08-03
Brondizio, E.S. and E.F. Moran. Human Dimensions of Climate Change: The Vulnerability of Small Farmers in the
Amazon. Philosophical Transactions of the Royal Society B 363(1498):1803-1809.
No.08-04
Lu, D., M. Batistella and E.F. Moran. Integration of Landsat TM and SPOT HRG Images for Vegetation Change
Detection in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing 74(4):421-430.
No.08-05
D’Antona, A. and L.K. VanWey. Estratégia para Amostragem da População e da Paisagem em Pesquisas Sobre
Uso e Cobertura da Terra. Revista Brasileira de Estudos de População 24(2):263-275.
No.08-06
Sherbinin, A. de, L.K. VanWey, K. McSweeney, R. Aggarwal, A. Barbieri, S. Henry, L.M. Hunter, W. Twine and R.
Walker. Rural Household Demographics, Livelihoods and the Environment. Global Environmental Change 18:28-53.
No.08-07
Rindfuss, R.R., B. Entwisle, S.J. Walsh, L. An, N. Badenock, D.G. Brown, P. Deadman, T.P. Evans, J. Fox, J.
Geoghegan, M. Gutmann, M. Kelly, M. Linderman, J. Liu, G.P. Malanson, C.F. Mena, J.P. Messina, E.F. Moran,
D.C. Parker, W. Parton, P. Prasartkul, D.T. Robinson, Y. Sawangdee, L.K. VanWey and P.H. Verburg. Land Use
Change: Complexity and Comparisons. Journal of Land Use Science 3(1):1-10.
No.08-08
Parker, D.C., B. Entwisle, R.R. Rindfuss, L.K. VanWey, S.M. Manson, E. Moran, L. An, P. Deadman, T.P. Evans, M.
Linderman, S. Mussavi Rizi and G. Malanson. Case Studies, Cross-Site Comparisons, and the Challenge of
Generalization: Comparing Agent-Based Models of Land Use Change in Frontier Regions. Journal of Land Use
Science 3(1):41-72.
No.08-09
D’Antona, A. de O., A.D. Cak and T. Tartalha. Integrando Desenhos e Imagens de Satélite no Estudo de Mudanças
no Uso e Cobertura da Terra. Ambiente e Sociedade XI(1):99-116.
No. 08-10
Adams, R.T. Large-Scale Mechanized Soybean Farmers in Amazonia: New Ways of Experiencing Land. Culture
and Agriculture 30(1,2):32-37.
No.08-11
Lu. D, G. Hongli, S. He, A. Xu, G. Zhou and H. Du. Pixel-based Minnaert Correction Method for Reducing
Topographic Effects on a Landsat 7 ETM+ Image. Photographic Engineering and Remote Sensing 74(11):13431350.
No. 08-12
Mattos, L. and A. Cau. Efetividade do Mecanismo de Desenvolvimento Limpo (MDL) no Contexto das Atividades
Agroflorestais no Brasil: Uma Análise Crítica. In Alternativa Agroflorestal na Amazônia em Transformação. R. Porro,
ed. Belem: ICRAF. Pp. 381-410.
No. 08-13
Donnelly, S. and T.P. Evans. Characterizing Spatial Patterns of Land Ownership at the Parcel Level in SouthCentral Indiana, 1928-1997. Landscape and Urban Planning 84:230-240.
No. 08-14
Evans, T.P. and H. Kelley. Assessing the Transition from Deforestation to Forest Regrowth with an Agent-based
Model of Land Cover Change for South-Central Indiana (USA). Geoforum 39:819-832.
No. 08-15
Messina, J.P., T.P. Evans, S.M. Manson, A.M. Shortridge, P.J. Deadman and P.H. Verburg. Complex Systems
Models and the Management of Error and Uncertainty. Journal of Land Use Science 3(1):11-25.
No. 08-16
Muehlenbein, M.P. Adaptive Variation in Testosterone Levels in Response to Immune Activation: Empirical and
Theoretical Perspectives. Social Biology 53(1-2):13-23.
No. 08-17
Muehlenbein, M.P., L.A. Martinez, A.A. Lemke, L. Ambu, S. Nathan, S. Alsisto, P. Andau and R. Sakong. Perceived
Vaccination Status in Ecotourists and Risks of Anthropozoonoses. EcoHealth 5:371-378.
No. 08-18
Parker, D.C., B. Entwisle, R.R. Rindfuss, L.K. Vanwey, S.M. Manson, E. Moran, L. An, P. Deadman, T.P. Evans, M.
Linderman, S.M.M. Rizi, and G. Malanson. Case Studies, Cross-site Comparisons, and the Challenge of
Generalization: Comparing Agent-based Models of Land-use Change in Frontier Regions. Journal of Land Use
Science 3(1):41-72.
No. 08-19
Rindfuss, R.R., B. Entwisle, S.J. Walsh, L. An, N. Badenoch, D.G. Brown, P. Deadman, T.P. Evans, J. Fox, J.
Geoghegan, M. Gutmann, M. Kelly, M. Linderman, J. Liu, G.P. Malanson, C.F. Mena, J.P. Messina, E.F. Moran,
D.C. Parker, W. Parton, P. Prasartkul, D.T. Robinson, Y. Sawangdee, L.K. Vanwey, and P.H. Verburg. Land Use
Change: Complexity and Comparisons. Journal of Land Use Science 3(1):1-10.
View publication stats