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Evolving core-periphery interactions in a rapidly expanding urban landscape: The case of Beijing


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We characterized and analyzed the dynamics of a rapidly expanding urban landscape of Beijing Municipality, based on the Hierarchical Regional Space (HRS) model. We focused on ecological processes such as flows of energy, materials and population between the urban core and its periphery, and how these processes co-evolved with urbanization. We treated the HRS as an alternative to the cellular automata (CA) approach to characterizing and modeling of landscape dynamics. With LANDSAT data, we showed that the urban area of Beijing expanded from 269km2 to 901km2 in the period from 1975 to 1997, an increase of 2.35 times in 22 years. Meanwhile, a number of secondary urban centers formed on areas that used to be sparsely populated around the city. These secondary centers quickly expanded and ultimately merged with each other and with the urban core. The changes in spatial pattern and organization were accompanied by evolution of urban functions and particularly the interactions between the urban core and its periphery. We demonstrated a dramatic increase in dependence of the urban core on the periphery as well as the cores influence on the periphery with a case analysis of the vegetable supply to Beijing. The tightening link between the city and its periphery reinforces the urbanization process and further drives the transformation of the regions landscape. We conclude that the HRS model is capable of characterizing the patterns and processes of complex and dynamic landscapes such as the case of Beijing, and this model has great potential for quantitative modeling of human dominated landscapes as well.
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Landscape Ecology 19: 375–388, 2004.
© 2004 Kluwer Academic Publishers. Printed in the Netherlands. 375
Evolving core-periphery interactions in a rapidly expanding urban
landscape: The case of Beijing
Ye Qi 1,, Mark Henderson2,MingXu
1, Peijun Shi1, Chunyang He1& G. Wil-
liam Skinner4
1College of Resources and Environment, Beijing Normal University, Beijing, 100875, China; 2Department of
Environmental Science, Policy and Management, University of California, Berkeley, CA 94720, USA; 3Department
of Ecology, Evolution and Natural Resources Center for Remote Sensing and Spatial Analysis, Rutgers University,
New Brunswick, NJ 08901-8551; 4Department of Anthropology, University of California, Davis, CA 95616, USA;
(Corresponding author:
Received 16 April 2002; accepted in revised form 17 April 2003
Key words: Beijing, core-periphery, hierarchical regional space, landscape dynamics, urbanization, land use change
We characterized and analyzed the dynamics of a rapidly expanding urban landscape of Beijing Municipality,
based on the Hierarchical Regional Space (HRS) model. We focused on ecological processes such as flows of
energy, materials and population between the urban core and its periphery, and how these processes co-evolved
with urbanization. We treated the HRS as an alternative to the cellular automata (CA) approach to characterizing
and modeling of landscape dynamics. With LANDSAT data, we showed that the urban area of Beijing expanded
from 269 km2to 901 km2in the period from 1975 to 1997, an increase of 2.35 times in 22 years. Meanwhile,
a number of secondary urban centers formed on areas that used to be sparsely populated around the city. These
secondary centers quickly expanded and ultimately merged with each other and with the urban core. The changes in
spatial pattern and organization were accompanied by evolution of urban functions and particularly the interactions
between the urban core and its periphery. We demonstrated a dramatic increase in dependence of the urban core on
the periphery as well as the core’s influence on the periphery with a case analysis of the vegetable supply to Beijing.
The tightening link between the city and its periphery reinforces the urbanization process and further drives the
transformation of the region’s landscape. We conclude that the HRS model is capable of characterizingthe patterns
and processes of complex and dynamic landscapes such as the case of Beijing, and this model has great potential
for quantitative modeling of human dominated landscapes as well.
Urbanization, a traditional research subject in geo-
graphy and regional economics, has received increas-
ing interest from ecologists who treat the process as
transformation of landscape patterns and functions or
as change in land use and land cover (Huang 1998;
Bessey 2002). Despite the long history of the study
of pattern and process of land use in geography, the
resurged interest in land use and land cover change in
the last decade was due primarily to its implications to
global and regional climate change (IPCC 2001). Land
use and land cover changes contribute, on average,
more than 20% to the buildup of CO2concentration in
the atmosphere (Houghton 1999), and they affect the
regional energy and water balance through the change
of the albedo and land surface processes. As a major
component of the global and regional environmental
change, land use and land cover changes have a pro-
found effect on the regional and global biodiversity
(Chapin et al. 2000).
Two major types of landscape transformation can
be identified at regional level. The first is the con-
version of natural vegetation to agricultural land, e.g.,
the agricultural development in the Amazon basin in
Brazil (Skole and Tucker 1994). The second is ex-
pansion of urban landscape, which can be found all
over the world, particularly along the coastlines of the
continents, and around the existing metropolitan areas.
Although urbanization is less extensive in area as com-
pared to agricultural development, it has intensive
impacts on the environment and ecosystem functions.
It affects more human lives through its effects on the
economy and society.
Recent efforts in modeling land use and land cover
changes have been dominated by the cellular automata
(CA) approach (e.g., Qi 1994; Wegener 1994; Qi et al.
1996; Landis and Zhang 1999; Jenerette and Wu 2001,
Luck et al. 2001). In the CA approach, a landscape
is divided into a number of grid cells. Each grid cell
has a finite number of states, representing the types of
land use and land cover. The change of the landscape
is treated as the overall consequence of the conversion
of the individual cells. At any point of time, each cell
has a probability of being converted to another type
of land use or land cover. The probability is a func-
tion of a number of factors such as the topography,
proximity to urban centers, proximity to transporta-
tion network, and population density. Normally, the
probability of one cell is assumed to be independent
of the probability of the neighboring cells. The prob-
ability values can be derived either from a set of rules
or based on some statistical procedures. Examples of
the former are found in Turner (1993); Qi (1994); Qi
et al. (1996); Landis and Zhang (1999); Luck et al.
2001, and the latter, the statistics-based approach, was
used in Pontius and Hall (1993); Clark et al. (1998).
The CA approach is based on a reductionist view
which assumes the whole is the sum of all consist-
ing parts, and it focuses on dealing with individual
cells. This approach often neglects the links among
the cells and the overall patterns at greater spatial
scales. The CA approach has proven to be effective
when the spatial heterogeneity dominates the pattern,
thus each location can be characterized by a unique
vector of factors. Interactions among locations are
given little or no consideration in such an approach.
Despite many sussessful case in simulations in some
cases, e.g., for mountainous regions in Southeast Asia
(Qi et al. 1996), the CA approach is likely to fail in
areas with flat terrain as a dominating feature of the
topography such as America’s Midwest and Eastern
China. In our study of landscape transformation of the
Beijing and its surrounding area, we attempted to ex-
plore a holistic approach that focuses on the overall
patterns of the landscape and on the spatial relation-
ships among the landscape components of same or
different levels in spatial scale. We did so because the
flat terrain of the region makes hard to characterize
and explain the formation and change of the urban
landscape based on the differences among locations as
in the CA approach.
We have observed in Beijing that more than 96%
of the urban expansion occurred on land with slope
less than 5 deg and elevation less than 100 m in the
study period from 1975 to 1997. In spite of the lack of
spatial heterogeneity in topography, the urbanization
is anisotropic: the northward urban expansion was sev-
eral times greater than the southward. This can hardly
be explained by the differences in the local properties
of the two directions. Thus, the CA approach which
focuses on the local differences is inadequate. It is
necessary to introduce alternative approaches that con-
sider the global properties and the interrelationships
and interactions among the localities.
We use the hierarchical regional space (HRS)
model developed by Skinner and associates (Skinner
1977, 1994; Skinner et al. 2000). HRS treats a region
as a whole in which interacting regional components
and elements are arranged in a hierarchical structure
with each component further divisible into lower level
in organization. Human settlement centers serves as
the ‘nodes’ of the hierarchy. This model recognizes
first the macroscale connections among the spatial ele-
ments. It helps to characterize the large-scale patterns.
This model was developed in study of the geographical
structure of human settlement centers and economic
activities (see also Woldenberg 1971; Wilson 1977),
but it coincides with many of the recent development
in theoretical and landscape ecology (e.g., Wu and
Locks 1995; Ahl and Allen 1996; Wu and David
The expansion of Beijing has resulted in much
greater dependence and influence of the city on its
peripheral areas. Vegetable supply is a good example.
The city used to be self-sufficient in vegetable supply
prior to 1980’s, but now more than half of the sup-
ply depends on the supply from Hebei, Shandong and
other nearby provinces.
In the HRS model, the intensively constructed
urban area is recognized as the urban core, and the
surrounding areas are treated as the core’s periphery.
Thus urban core is much larger in space than the tradi-
tional central business district (CBD). The boundaries
of the periphery is broadly defined by the limit which
the core influences can reach. We realize that defin-
ition is subject to debate, but it does not affect our
analysis in this study.
The rapid urbanization has occurred with, and as
a result of, the over-intensified interactions between
the urban core and its peripheral areas. On the one
hand, the city core acts like a socioeconomic mag-
net which absorbs the human and natural resources
from the periphery, forming a constant and significant
fluxes of material, energy and population between the
core and its periphery; on the other hand, the urban
core’s influences radiate onto the peripheral areas and
are agents for transformation in landscape patterns and
functions in those areas. These dynamic interactions
between the core and periphery are key to understand-
ing and predicting the changes of urban landscapes. In
this study, we characterize these changes of the urban
landscape of Beijing for the period of a quarter cen-
tury through applying the Hierarchical Regional Space
(HRS) model and landscape ecology theories, based
on the Landsat data and GIS. We will show that HRS is
a useful alternative to the CA model for characterizing
and modeling complex, dynamics landscapes and their
Data and method
The site of study
Beijing, the capital city of China, forms the core of
one of the largest metropolitan regions in the country
(Figure 1). The municipality is located at 3956Nand
11620E, covers an area of 16,808 km2.Ofwhich
two third are mountainous areas encircling the west-
ern, northern and eastern sides of the city. The center
is 43.71 m above sea level and the main rivers include
Yongding, Chaobai and north canal. Beijing lies in the
temperate zone. Within 50 km north and west of the
city, the Taihang and Jundu mountains, straddled by
the famed Great Wall, rise to heights of 2,300 m.
Beijing’s history as a capital city dates as far back
as China’s Warring States period (484-221 B.C.); most
significantly, it was the center of the Mongols’ east
Asian empire at the time of Marco Polo (c. 1285)
and was the capital of the Ming and Qing dynasties
(1368–1643 and 1644–1911). At the establishment
of the People’s Republic of China in 1949, the city
was again made the national capital. Beijing has seen
more constant expansion over the past five decades
than any other Chinese city. By 1975, the city has
a population of 9 millions. Since then, the city has
experienced rapid economic boom and urbanization.
The Qing city walls were replaced with ring roads,
multistory housing blocks rose over the alleys of the
old city, and surrounding villages became suburbs in a
conurbation with some 6.5 million residents by 2000.
Beijing municipality, encompassing eight districts and
ten counties as well as the central city area, reports
a population of 14 million. Since the beginning of
economic reforms in 1978, development of the sur-
rounding countryside has been especially brisk. The
plains east and south of the city are now a check-
erboard of high-intensity agricultural lands and new
urban areas, while nearby mountainous areas, though
targets of reforestation since the 1960s, also show the
effects of economic expansion.
We used Landsat data to detect the change in spatial
patterns of the land use and land cover from 1975 to
1997. The coverage of our landsat data includes 11 of
a total of 18 districts or counties of which the Beijing
Municipality consists. The 11 districts or counties
contain the urban core of Beijing and part of peri-
pheral counties. The covered area (enclosed within
393552–40220 N and 1155005–1165909
and about 4500 km2) has experienced the most dra-
matic urbanization in the period of study. Seven
counties that are left out of the study all distribute in
the outskirt of the municipality. The four periods of
Landsat data are all taken for path 123 and row 32.
Except the first period (May 6, 1975) for which MSS
data was used with a spatial resolution of 180 ×180 m,
TM data are used for other periods (October 2, 1984,
May 6 1991 and May 16, 1997) and the spatial res-
olution is 30 ×30 m. The four scenes were selected
based on their data quality, cloud cover, and time of
the year. All data were processed at the Institute of
Resource Science of Beijing Normal University. Shi
et al. (2001) provided details on data processing.
Land use and land cover classification were based
on the system used by Anderson (1976). The seven
types that are distinguished on the images include:
intensive urban area, extensive urban area, water
(including fish ponds), farmland, orchards, shrubs,
and forest. Kappa statistics for classification are 0.71
(1975), 0.76 (1984), 0.80 (1991) and 0.82 (1997).
The hierarchical regional space model
HRS draws on some of the fundamental elements of
modern geographical thought, including regional sys-
Figure 1. The area of study, with surrounding urban centers. The urban core is indicated as central city and the surrounding districts. Different
levels of urban centers are also laid out.
tems theory (in the tradition of von Thünen), central
place theory (following Christaller 1933), and dif-
fusion theory (as introduced by Hägerstrand 1965).
Foreshadowing regional systems theory, von Thünen
(1966 [1826]) described how zones of high- to low-
value economic activity fill the regions around cities.
Seen as core-periphery structures, von Thünen’s zones
can be characterized in terms of agricultural intens-
ity and transport efficiency. Regional systems theory
subsequently conceptualized these regions as local or
regional, social and economic systems, centered at
urban nodes and nested in a more or less integrated
hierarchy. This led in 1925 to E. W. Burgess’s con-
centric growth model that modeled city expansion in
terms of concentric circles of high- to low-value urban
In the 1930s and1940s geographer Walter Christaller
and economist August Lösch put forth versions of
central place theory that became fundamental texts of
urban geography. For agrarian societies, Christaller’s
(1933) central place theory predicts the emergence
of a hierarchy of settlements, with each level of the
hierarchy providing distinctive services and attain-
ing corresponding levels of development. Individuals
on the landscape orient their economic activities to
specific central places at each hierarchical level in
accordance with the services provided there: to a
nearby market town for items of daily use, to central
towns for cooking utensils, to cities for fashionable
clothes, and to metropolises for specialized services
such as higher education. It follows that the hierarch-
ical levels providing less common services require a
correspondingly wider hinterland, and that for reas-
ons of transportation efficiency the hinterlands at each
level become nested. Economic activities in this hier-
archy develop hand in hand with a web of social
networks, making a central place analysis a useful
starting point for an investigation of social patterns.
Anthropologist G. William Skinner first applied
a central place analysis to understanding the spa-
tial structure of rural Chinese society at the local
level, finding evidence for the ‘traffic’ and ‘market’
variants of Christaller’s central place theory (Skin-
ner 1964–65, Crissman 1976, p.204). In subsequent
work, Skinner traced China’s central place hierarchy
up to its highest levels, proposing that the agrarian
portion of China (excluding the pastoral regions of
Tibet and Inner Asia) could best be analyzed in terms
of nine ‘macroregions’ (Skinner 1976). Each of these
macroregions makes up a more or less integrated eco-
nomic system within China’s national economy, much
as the comparably populous nations of France, Ger-
many, and others function as more or less integrated
economic systems within Europe’s continental eco-
nomy. Skinner’s regionalization of China has been
highly influential in studies of Chinese history and
society (Lavely 1989; Cartier 2002) but has not been
widely appreciated in fields such as economics or
ecology, which have tended to rely on much more
simplistic spatial characterizations (such as bifurcat-
ing China into ‘north’ and ‘south’ or ‘coastal’ and ‘in-
terior’; see Batty 1994) or emphasizing physiographic
regions rather than social regions despite the dominant
role of human activity on the Chinese landscape.
After a decades-long lapse, in 1980s and 1990s the
Chinese government began compiling and releasing a
wide range of social, economic, and environmental
statistics. Skinner and associates made use of these
statistics and newly available GIS technologies to
construct a still more detailed spatial framework for
China dubbed the Hierarchical Regional Space model.
(Parallel efforts using historical data have led to the
construction of HRS models for Japan and France.)
The HRS model aims to make explicit the spatial
relations among regions defined around human settle-
ments at multiple levels in the central place hierarchy.
Phenomena at a given location in the social-economic
landscape must be understood in terms of that loc-
ation’s position amidst the core-periphery structures
operating at different spatial scales at each level in
the hierarchy. Implemented in a geographic inform-
ation system, the HRS model has been shown to be
highly predictive of socioeconomic phenomena such
as fertility, education, and occupational stratification
by gender (Skinner 1994; Henderson et al. 1999; Skin-
ner et al. 2000; Henderson and Ladenson 2000). In
this paper we return HRS to the intellectual roots of
Thünenesque regional systems theory by applying it
to questions of urbanization and land use/land cover
As applied to China, HRS theory is operationalized
as a multilevel hierarchical framework for analyzing
data for regional systems. Below the top hierarch-
ical level, China’s nine macroregions may be divided
into central metropolitan subsystems, each oriented
around one or two major metropolises. Four such sub-
systems – Beijing-Tianjin, Shijiazhuang, Zhengzhou,
and Ji’nan-Qingdao – are found in the North China
macroregion. (Figure 2 outlines the nine macrore-
gions of China and the four subsystems of North
China, including the region around Beijing that is
the focus of this paper.) At this hierarchical level
Figure 2. China’s macroregional systems in relation to provinces, showing metropolitan cities, 1990. Within the North China macroregion,
four subregions are delineated with heavy dashed lines (from Skinner et al. 2000).
we can discern the broad core-periphery pattern of
socio-economic development through an analysis of
county-level statistics and household census returns.
For the Beijing region these are grouped into seven
core-periphery zones (see figure 3), representing the
structural distance from the core to settlements within
each zone, taking into account socioeconomic vari-
ables and transportation costs. (Skinner et al. 2000,
provides a detailed description of the analysis rep-
resented by these zones. The core-periphery concept
itself is widely used in urban studies; see for example
Wallerstein 1991; Krugman 1991; or Chase-Dunn and
Hall 1991.)
In implementing the HRS model for China, Skin-
ner et al. (2000) has continued below the level of
regional cities to assign some 12,000 smaller cities and
towns to levels in the central place hierarchy. These
assignments are not simply a matter of classifying set-
tlements by population size; as countless applications
of the rank-size rule have shown, we can expect no
hierarchical discontinuities within an ordered list of
city populations below the level of the primate city,
and China is no exception (Zipf 1949; Skinner 1976;
Mann 1984; Marshall 1989; Reed 2002). Instead,
central places are classified by the urban functions
they perform for their surrounding hinterlands. In the
case of China, published statistics on urban functions
were used for cities in the upper levels of the hierarchy
to guide the analysis; population figures play a role in
the lower levels, but the breakpoints between levels
necessarily vary with each local urban system. Central
place analysis expects settlements at each level within
the hierarchy to show a high degree of primacy with
respect to the lower-level central places in their hinter-
lands. Thus the delineation of regional systems and the
assignment of the central places therein to hierarchical
levels follows a top-down approach as advocated by
Marshall (1989), identifying the primate settlement at
each level and its dependent nodes at the next lower
level, ultimately revealing the urban hierarchy from
metropolis to market town.
Figure 3 depicts the urban hierarchy in the imme-
diate vicinity of our Beijing study area and outlines the
boundaries of regional city systems, a middle hierarch-
ical level in the HRS model. Each regional city (there
are some 272 in all of China; Skinner et al. 2000,
p.619) serves as the economic and social hub of these
systems. And, with patterns analogous to those seen at
the macroregional level, regional city systems are ex-
pected to exhibit their own core-periphery structures.
These assignments, along with administrative ranks
assigned by the Chinese government, have been used
to characterize every location in China along an urban-
rural continuum; this dimension of the HRS model
aims to approximate the core-periphery structures of
lower-order urban centers and their immediate hinter-
lands. Thus the HRS model allows us to contextualize
any settlement in agrarian China by its position within
both high-level and meso-level core-periphery struc-
tures: by its core-periphery zone and by its urban-rural
continuum category.
For this study of urban expansion, the space
between settlements is modeled by interpolating the
core-periphery zone assignments between mapped set-
tlements, and by extending the urban-rural continuum
categories outward along transportation routes using
the distance-decay function common to gravity mod-
els. The key model variables, then, account for the
structural distance from a given point to the urban
nodes at different levels in the urban hierarchy, as well
as the transportation distance to the nearest settlement
of any level.
Results and analyses
The results of classification of land use and land cover
of 1975, 1984, 1991, and 1997 are mapped in Figure 4.
Six types of land use/ land cover are identified in the
map, with intensive and extensive urban area lumped
together. Maps of changes in urbanization are shown
in Figure 5. In this section, we will discuss the changes
in land use and land cover, in spatial patterns, and in
urban functions.
Changes in land use and land cover
Table 1 summarizes the change in land use and land
cover in the study area of a total of 4500 km2.The
values in each row represent the percentages of each of
the seven land use/cover types in the study area for one
point in time. The table shows that five types of land
increased in their areas while two types decreased.
Among the gaining side, urban expansion is the most
significant. Putting together both urban land types, we
see that 14% (or 630 km2) of the land in the study area
was converted to urban use in the 22-year period from
1975 to 1997. As a result, the urban area more than
doubled during the period. Water surface, orchards,
and forest cover also had significant growth. On the
losing side, farmland lost about one third of its area
in 1975. The lost land, about 945 km2, was mostly
Figure 3. Core-periphery structure of the North China macroregion, showing high-order cities and major transportation network, 1990. The
level of shade (gray) indicate the gradient from core to periphery. See 2.3. for description how the gradient is defined and delineated.
Figure 4. Land use and land cover classified from Landsat MSS and TM data, (a). 1975; (b) 1984; (c) 1991; and (d) 1997. Intensive and
extensive urban areas are combined.
used for urban expansion and aquaculture. Shrubs and
grassland had a significant loss to reforestation, mostly
in the mountainous areas where the natural vegetation
prior to timber harvest was temperate forest.
Changes in spatial patterns
Three major changes in spatial pattern have been ob-
served: (1) the expansion of the urban core, (2) the
formation of secondary urban centers, and (3) the
fragmentation of the landscape.
First, the expansion of the urban is obvious from
the maps from 1975 through 1997 in Figures 4 and 5.
Most of the expanded urban areas took place around
the edges of the existing urban area, mostly through
‘nibbling’ the farmland around the city. The core
expansion was accompanied by the mergence of sec-
ondary urban clusters. For example, on the map of
1975, a dumb bell-like band of urban area in the west
and southwest of the city was well recognizable. The
west cluster, labeled with circle and number 1 in Fig-
ure 4, was where a major state-owned steel plant, the
Capital Steel, was located; and the southwest cluster is
a suburban town called Fengtai which was a major hub
of freight trains. Over the years, these clusters grew
fast enough, and quickly merged with the main urban
core. By 1991, the farmland between the dumb-bell
band and the urban core was hardly recognizable and
continued to grow through 1997.
The second feature is the formation of second-
ary urban centers, or secondary central places in
Christaller’s (1933) terminology. There are largely two
types of secondary urban clusters around the core of
Beijing: the capital towns of the counties and the in-
dustrial buildups of large, and usually state-owned,
enterprises. In addition to the two clusters mentioned
Table 1. The fraction of land use/cover types in four points in time.
Intensive Extensive Water Crops Orchards Shrubs/ Forest
Urban Urban Grass
1975 1.29 4.68 1.19 66.66 6.03 16.04 4.10
1984 1.53 10.86 2.28 60.29 7.09 12.31 5.64
1991 2.19 12.45 4.44 54.41 7.56 12.06 6.90
1997 6.44 13.54 7.39 45.66 8.68 10.13 8.16
in the last paragraph, we marked eight other second-
ary urban clusters which could be barely recognized
in the 1975 map. These clusters were ‘seeds’ to grow.
By 1984, all marked clusters had significant growth
in area, and clusters 1 and 2 merged with each other.
By 1991, clusters 1, 2 and 3 were essentially in-
tegrated with the urban core due to their respective
expansion. Other marked clusters grew to become sig-
nificant urban centers. The formation and growth of
the secondary urban centers markedly changed the
pattern of the landscape.
The third feature of the landscape change is char-
acterized by the increased fragmentation. Shi et al.
(2001) calculated both landscape diversity index and
fragmentation index. The diversity index increased
from 0.49 in 1975 to 0.70 in 1997 for the study area.
Meanwhile, the fragmentation index increased from
0.71 to 0.81. Important differences in fragmentation
exist between the flat areas (about 80% of the total
area of study) and the mountainous area (20%). For
the plains, the diversity index increased from 0.38 to
0.56, while the fragmentation index from 0.73 to 0.90.
For the mountainous area, the diversity index varied
in the range from 0.52 to 0.56, but the fragmentation
index decreased from 0.93 to 0.56.
The change in fragmentation indicates the effect
of land use and land cover change on the overall
pattern of the landscape. The plains were severely
impacted by urban expansion which resulted in in-
crease in fragmentation, while the mountains were
reforested with young trees, leading to decrease in
fragmentation of the landscape. This agrees to gen-
eral observation that urban expansion and intensified
transportation network tend to increase the landscape
heterogeneity, while the reforestation homogenizes the
vegetation cover of the mountains. In fact, 99% of the
urban expansion took place on areas with slope less
than 5 deg, and more 96% of the urbanization on areas
with elevation less than 100 m (Shi et al. 2001). The
fact that most urbanization took place on relatively
homogeneous areas makes it difficult for the CA ap-
proach using topography as a major driver the change
in land use / land cover.
Increased core-periphery interactions: The example
of vegetable supply
The changes of urban functions are closely related
to the transformation of the landscape patterns as a
consequence of urbanization. The urbanization of the
Beijing area showed that the patterns and functions
co-evolve with each other. These changes together res-
ulted in functional integration of the core and the sur-
rounding areas. More profoundly, the changes helped
to convert the areas surrounding the municipality to
functionally integral parts of the urban hierarchy. As
a result, the urban core of Beijing has increased its
influence over the expanded periphery. The surround-
ing areas which otherwise used to be little impacted
by the municipality have now become integrated in
economic, social and ecological functions with the
municipality. On the other hand, the dependence of
the urban core on the newly expanded periphery has
increased. This dynamics of evolving core-periphery
interactions is important in understanding and mod-
eling the change of landscape patterns. We use the
vegetable supply as an example to demonstrate this
important feature in urban function dynamics.
Fresh vegetable has become perhaps the largest
foodstuff consumed by the urban population. This has
to do with the diet structure of the Chinese people
who, in general, consume much less meat as com-
pared to the people in the Western countries. Cities
themselves do not produce vegetables, and vegetable
supply to the cities depends largely on the nearby rural
areas. However, municipalities which include rural
areas surrounding the urban cores had great capacity
for vegetable supply (Skinner 1978). In 1975, Beijing
achieved self-sufficiency in vegetable supply, with
about 90% of its consumption of vegetables produced
in the suburban and rural areas of the municipality.
But self-sufficiency declined to less than 60% by 1996
(Qiao et al. 1998).
As the city increasingly expands to occupy the
land that previously used for vegetable production, ve-
getable supply relies more on the land farther away
from the urban core. For example, a sharp decline
in land area for vegetable production in the immedi-
ate proximity of the city occurred from 11,300 ha in
1984 to 6700 ha in 1996, a 41% decline in 12 years.
However, in the rural area farther away from the city,
the vegetable land increased from 8700 ha in 1984
to 43,000 ha in 1996, a three-times increase in a 14
year period (Qiao et al. 1998). As the frontiers of ve-
getable land are pushed further away from the urban
center, the production efficiency also declines. First,
the land taken by urban expansion is more fertile than
the land farther away from the city, because it has been
improved over years and decades for vegetable pro-
duction. Night soil from the city was a major source
of fertilizer (Skinner 1978). Second, many skilled
vegetable farmers are likely to abandon their old pro-
fession and seek employment in the city, now that their
land and themselves are urbanized. Third, production
and transportation cost are usually higher in the near
When the municipality reaches its capacity for ve-
getable production, the deficit in vegetable supply has
to be supplemented by regions beyond the boundary
of the municipality. Qiao et al. (1998) cited that more
than 40% of the vegetable was supplied by the nearby
provinces, mostly Hebei and Shandong (see Figures 2
and 3). Assuming a per capita consumption of 500 g of
fresh vegetable per day, we calculated the annual de-
mand of vegetable by the Beijing municipality. Using
these values and the statistics of vegetable produc-
tion from Beijing Statistics Bureau, we found that the
self-sufficiency in vegetable supply by the municip-
ality was between 10% and 18% in year 2000. In
other words, more than 80% of vegetables had to be
supplied by other provinces. In fact, by mid-1980’s
it became a daily (or nightly, to be accurate) routine
that truckloads of vegetables were transported to the
major markets in Beijing from the provinces of Hebei
and Shandong. In these adjacent provinceswhere agri-
cultural production has historically focused on grains,
major shift in land use quietly, but swiftly, took place
toward more and more production in vegetable and
fruits. Vegetable production began to replace grain
production to be the main source of income and the
first place in land use. By late 1980s, major feature
Figure 5. Land use and land cover change in three periods, showing
the expansion of urban core.
in the rural landscape in these regions was plastic
covered greenhouses surrounding the rural villages.
The rapid urban expansion in Beijing area was closely
coupled with the widespread reallocation of land from
grain production to vegetable and fruit production in
the neighboring Hebei and Shandong Provinces.
The regional expansion of vegetable supply de-
veloped with formation and growth of specialized
vegetable production zones. In Shandong Province
alone, several counties developed their specialized
zones for vegetable production.Counties in Liaocheng
Prefecture, 250 km from Beijing, became special-
ized in vegetable production by late 1980s. Similarly,
Shouguang County has become the largest vegetable
production base in China. This county alone sup-
plied 20% of all vegetable consumption of the city of
Beijing in 1990’s.
Lowered cost of transportation has been an im-
portant factor. For example, Suning county of Hebei
Province is 220 km south of Beijing. It used to take
6 h to travel to Beijing by bus ten years ago. Now the
travel time is cut by half due to the improvement of
roads and means of transportation. In this relatively
small county where vegetable production used to be
for households’ self-consumption, 25% of the arable
land is now allocated for vegetable production, mainly
to be transported Beijing (Suning Statistics Summary
2000). In effect, at least part of each of the provinces
surrounding Beijing has become well integrated in
function with the Beijing metropolis.
Land classification and measures of landscape
We were able to identify seven types of land use and
land cover. It is possible to make more detailed clas-
sification, particularly using LANDSAT TM data, but
the seven types are adequate for our purpose in this
study. Rather than accurately quantifying each land
type, we focused on characterizing the change of the
spatial patterns and functions of the urban landscape
of Beijing and on uncovering the evolving interac-
tions between the urban core and its periphery that
accompanied the changes of landscape patterns.
Due to the lack of TM data for 1975, we used MSS
data instead. The difference in spatial resolution (30 m
for TM vs. 180 m for MSS) would inevitably introduce
errors in both land classification and in calculation of
measures of landscape structure (Qi and Wu 1996; Wu
et al. 2002). It is likely that the resulted errors do not
obscure the overall trend in landscape structures from
1975 to 1984. This was a period when great changes
in landscape patterns took place.
The diversity index is generally not a good choice
to indicate the fragmentation of landscapes. We used
it in combination with fragmentation index calculated
in Shi et al. 2001.
The core-periphery interactions and transformation
of urban landscape
More than two decades of urban development not only
has transformed the landscape of the municipality of
Beijing, but also changed the overall landscape in
the peripheral regions. The extent of the periphery
has expanded and the functional links between the
urban core and its periphery have been tightened. The
perspective of viewing the dynamics of the urbaniz-
ing landscape as an evolving process of interactions
between the urban core and periphery has import-
ant implications for modeling the landscape changes.
The urbanization process in Beijing region can be
viewed largely as process of core-periphery interac-
tions, while the characteristics of specific locations has
played minor role in affecting land use and land cover
change. The changes of spatial patterns at large scale
are closely linked to the spatial interactions between
the different levels in the urban-rural continuum in
the core-periphery zone. For this particular case of
Beijing, topography, which is usually an important
driver for land use and land cover change, has doubt-
lessly played a minor role, because of its lack of
heterogeneity in the region. No other single or mul-
tiple factors, physical or socio-economic variables,
seemed to have major influenceon the landscape trans-
formation in the region during the period under study.
Therefore, as an alternative, the HRS approach serves
as a complement to the CA approach.
The use of the HRS model
We noted that both HRS model and its predecessor,
the central place theory, were developed for under-
standing the regional structure of agrarian societies.
In our case, the Beijing municipality and the sur-
rounding region are one of the most industrialized
regions in China. Yet HRS still works as a qualitat-
ive theory, as demonstrated in Skinner et al. (2000),
in explaining the social-economic structures of the
lower Yangtze River basin. This has potential to be-
come a quantitative model of landscape dynamics of
the region. However, this model will have to include
Tianjin, another major metropolitan entity of the same
macroregion (see Figure 2). An interesting observation
is that the rate of urban expansion of Tianjin has been
much slower than that of Beijing. Some even argue
that it is the development of Beijing that has drained
the resources and market that are otherwise shared by
Tianjin. If this is the case, it provides an example of
core-periphery interactions of different type. But this
much more complicated case is beyond the scope of
this study.
The urban expansion in Beijing is a result of
reallocation of energy, material, information, capital,
labor and market. These elements distribute, flow and
dissipate with the spatial structure and make the struc-
ture itself to evolve (Costanza 1977). HRS model
provides a macroscopic view of the overall system,
and a theoretical framework, supplementing the CA
model, for building the model of landscape dynamics.
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This study investigates the drivers of water use efficiency (WUE), a key metric of water resources management, and its changes over eight regions across China from 1982 to 2015 based on gross primary production (GPP) and actual evapotranspiration (AET) datasets. The order of seasonal change of WUE from large to small is autumn, summer, spring and winter. The drivers include seven variables, air temperature, specific humidity, precipitation, short-wave radiation, Normalized Difference Vegetation Index (NDVI), soil moisture and CO2. Our analysis suggests that the sensitivity of annual average NDVI to WUE changes was high nationwide, but there were some differences in seasonal scales. The annual average contribution of air temperature and CO2 affecting WUE change was relatively high in China's largest area (SW, SE, E, NP). Other influencing factors were only relatively high in the local area. Seasonally, NDVI is the driving factor with the highest contribution rate in summer and autumn for NC and NW region. The seasonal contribution rates of driving factors in other regions are significantly different. For the study period (1982–2015), the shrubland ecosystem had the highest annual WUE followed by forest and cropland. The WUE of the farmland ecosystem was higher than that of the grassland ecosystem in most areas.
... Persistent decline in cultivation in the present periurban area is a reflection of farming becoming a less attractive occupation due to quantum jump in land prices offered by private builders/government agencies, secured supply of food grains and quality perishable fruits/vegetables/milk/milk products at subsidised/ reasonable prices by government agencies, fragmentation of land holdings, stagnation of yields and emerging mindset of higher social status of for non-farm employment among youth (Fazal 2000;Qi et al., 2004;Ahmad and Haseen 2012;Sarkar et al., 2016). ...
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Land use and land cover changes over 1973–2017 period in peripheral Delhi were mapped based on digital classification of satellite data and their driving forces ascertained. Urban area expanded and agricultural area diminished at annual rates of 38.6% and 2.1%, respectively, during the 1973–2017 period. Urban expansion occurred more in scrub and sparse vegetation areas than in cultivated lands or ponds. Loss of cultivated land happened mostly due to abandonment of cropping and tree planting in farmhouses developed by the urban elites. Improvement in the state of forests in terms of their expansion as well as densification offsets their loss due to urbanisation, encroachment and logging. The increment in the green cover was due to strict enforcement of compensatory afforestation/forest conservation law, growing demand of ecotourism, emergence of tree-clad farmhouses and increased environmental awareness and surveillance. This research will help in comprehending policies favouring sustainable urban development.
This study focuses on the trends and the causes of variation in actual evapotranspiration (AET) around the warming hiatus over China by a comprehensive analysis applying various temporal‐spatial methods. It is observed that the annual AET showed a different trend around 2000 for China as a whole. By employing segmented regression analysis for detecting warming hiatus points, high temporal inconsistency can be found in eight climatic regions of China. The impacts of meteorological variables on AET were further identified by affecting the intensity and relative change of meteorological factors. AET was highly correlated (P<0.01) with solar radiation in the southeast (R=0.80) and air specific humidity in the northwest areas (R=0.83). AET changes presented the highest sensitivity to specific humidity in Northwest before 2006 and in North Central China after 2003, with sensitivity coefficients of 1.48 and 1.74, respectively. Three variables, including air specific humidity (with an average contribution rate of ~17% in the Northwest), short‐wave radiation, air temperature can be the main factors that lead to the changes in AET. The specific meteorological factors varied from region to region: the changes in AET can be ascribed to the increased wind and short‐wave radiation in North Central China and East China, the decreased air temperature in Tibetan Plateau, and the increased specific humidity in Southeast China during warming hiatus, etc. After the warming hiatus occurred, the dominant factor of AET trends changed from air specific humidity to short‐wave radiation and other factors. Generally, air specific humidity and air temperature have played leading roles in AET trends during the past 30 years. This article is protected by copyright. All rights reserved.
Landscapes change in space and time creating a complex mosaic of patches, which are the result of a different history of disturbances. These disturbances largely depend on a multitude of concurrent factors that may be unrelated. Land mosaic is the result of opportunities, events, and novelties that operate at different spatial and temporal scales and that contribute to the environmental variability observed in a landscape. In addition, disturbances originated by human activity (e.g., agriculture intensification and abandonment, fire suppression, deforestation, livestock grazing, and development) concur to create the observed patchiness across landscapes.
Key features of reproductive behavior in China vary systematically through space and time. In this article we present an analysis of fertility change in regional space, using a 1% household sample from China’s 1990 population census. Elsewhere,we use the same data to analyze reproductive strategizing, but here we pursue the big picture with a straightforward analysis that takes reported births as an uncomplicated indicator of fertility.The article has three objectives: first, to introduce a novel, multilevel spatial model of regional structure constructed using a geographic information system (GIS); second, to demonstrate the potential for longitudinal data derived from onetime censuses to contribute to historical demography in conjunction with regional analysis; and third, to document the manner in which China’s fertility transition has unfolded in regional space.
Since in geographical research it is often necessary to handle large amounts of information, data-processing techniques have many advantages. Many problems can be solved more quickly and more cheaply, and in addition, exercises can be carried out which have so far been beyond the reach of conventional techniques. More specifically, the computer can undertake descriptive mapping, spatial analysis and the running of process models. A necessary condition for the adoption on a larger scale of automatic data-processing in geography is that census information should be made available with due regard to the spatial element. The tradition of providing data only for political areas gives a quite inadequate degree of geographical precision and flexibility. In Sweden, experiments have been carried out with the aim of developing a national data-bank system possessing a high degree of geographical precision. The system is now under Government consideration with a view to implementing it in time for the 1970 census. Public land-registration forms the general base. The principle is to select a 'central point' (normally the main building) inside each cadastral unit. This point is taken to represent the location of the entire unit, and its x-y co-ordinates in the national grid are entered in the land-register to the nearest 10 metres. Since all census data are referred to the cadastral units, the land-register tape will provide a translation device for purposes of mapping. A set of examples is shown to demonstrate descriptive mapping and computerized spatial analysis. It is often convenient and useful to aggregate data over square cells, but this procedure is by no means a necessary feature of the system. Distance zones from a point or line and completely irregular regions are equally possible, as well as straightforward plotting of dots. Samples can be drawn with full regard to ecological conditions. Data-banks storing geographical information, together with the use of computers, would greatly improve our ability to tackle complex regional situations quantitatively.
Acknowledgements Introduction: The lessons of the 1980s Part I. Geopolitics, Post-America: 1. North Atlanticism in decline 2. The Reagan non-revolution, or the limited choices of the US 3. Japan and the future trajectory of the world-system: lessons from history 4. European unity and its implications for the interstate system 5. 1968, revolution in the world-system 6. Marx, Marxism-Leninism, and socialist experiences in the modern world-system 7. The Brandt report 8. Typology of crises in the world-system 9. The capitalist world-economy: middle-run prospects Part II. Geoculture, The Underside Of Geopolitics: 10. National and world identities 11. Culture as the ideological battleground of the modern world-system 12. The national and the universal: can there be such a thing as world culture 13. What can one mean by southern culture 14. The modern world-system as a civilization 15. The renewed concern with civilization(s)? Index.
An energy model of exchange is formulated that shows energy relations to economics. Computer simulations were used to study effects of external energy resources on exchange, and the effect on spatial organization of landscapes.