GeoJournal 52: 213–221, 2001.
© 2001 Kluwer Academic Publishers. Printed in the Netherlands.
213
Using GIS for analysis of urban systems
Guoqing Du
Institute of Geoscience, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, Japan
(E-mail: [email protected])
Received 31 March 2001; accepted 5 October 2001
Key words: cartographic overlay, China, nodal structure, open policy, urban system
Abstract
It is recognized that the spatial organization of urban system can be abstracted as three main components: a set of points,
which describes the structural characteristics such as social and economic properties of individual city; a set of lines, which
indicates the patterns of interaction among the cities; a set of spheres, which indicates how and to which extent each city
affects the area around it. Even if such substance of urban system coincides with the main purpose of GIS for spatial or
geometric data types such as point, line and region, little attention has been given to this point. By an evidence analysis of
China, the application of potential analytical capabilities of GIS for urban system is attempted in this study. We find GIS acts
as an efficient tool not only for the spatial structure analysis of urban system, which including potential distribution of cities,
extraction of principal linkages among cities, and delimitation of subsystems; but also for the verification of socio-economic
attributes and dynamics of urban system.
Introduction
A large number of studies on urban systems have been carried out since the late 1960s. With the rapid development
and changes of urbanization, the studies of urban systems
are also becoming more and more complicated. First of all,
the number of cities increased and the urban scales have been
enlarged with a rapid speed, especially in developing countries. For example, China added more than 200 new cities
(shi) and nearly 10,000 towns (zhen) in the 1980s (People’s
Daily, 1989). The number of cities increased from 244 in
1982 to 640 in 1995. Furthermore, we found a remarkable
increase in urban size. In 1953, the number of cities with
a population exceeding 100,000 was 102, this number increased to 222 in 1982, doubling in 29 years. Subsequently,
an immediate sharp increase by the same indicator to 466
was seen in 1991, doubling once more in only 9 years
(Du, 1997). Secondly, as a result of the development of
convenient method of communications and transportation,
the interactions and relationships among cities became more
complicated. Suppose if we want to analyze a system of
640 cities, we have to take 640 × 640 linkages into account.
The study of urban systems has enlarged its area to include
the international or even global urban systems recently, and
number of example cities increased consequently. With such
a background, searching an efficient approach of GIS to
solve such problems is a matter of course. In this paper, we
will try to investigate the useful tools of GIS for the analysis
of urban systems.
Components and process of GIS for urban system
analysis
In its narrowest and most traditional sense, the urban system refers to the set of cities in a certain region. In other
words, the system is simply the aggregate of cities; no attempt has been made to identify relationships among them.
But when the concept is developed more fully, the urban
system account for the observed relationships among cities,
and provide a model for the analysis of spatial variations of
growth and changes in the system. Based on the urban nodes,
the urban system, in this broader sense, also includes the relationships among nodes particularly (Simmons, 1978). We
can say looking each city as a point and a relationship between each pair of cities as a linkage line is the fundamental
thought of urban system. Therefore, point and line can be
extracted as the basic spatial elements of urban system.
Figure 1 shows the components and process when we use
GIS for urban system analysis. The process begins with data
input of a set of attributes in order to describe the structural
characteristics such as size, socio-economic properties of
each city. Such characteristics are all represented with points
in GIS.
The concept of the urban system has a number of attractions, but perhaps its major one is that it links together
all studies that examine the relationship between the spatial
components (Bourne and Simmons, 1978). In the case of
linkages, we have two ways to identify these lines. One is
to input the O-D data of realistic linkages to indicate the
patterns of interaction among cities in terms of movements
of people, data, goods, or money etc; another is to identify the linkages by calculating the available point data of
214
Figure 1. Components and process of GIS for urban system analyisis.
characteristics of each city. For the first method, we can
use the data of realistic interaction directly, or we can also
take the realistic data as direct flows, calculate the indirect
flows by some models, and then take the direct and indirect
flows into account together. Using telephone calls, Nystuen
and Dacey (1961) gave us a good example of how to use
the indirect linkages and flows as well as direct. The second
method will be useful if realistic linkages data are not available. The model used in this process includes the Spatial
Interaction Model, among which the Gravity Model is the
most widely used model in geography for the calculation of
interaction. The reasons for the strong and continuing interests of Gravity Model are easy to understand and stem from
both theoretical and practical considerations (Haynes and
Fotheringham, 1984). Studies on various kinds of aggregate
flows of human interaction, such as migration, traveling,
communication, commodity shipping, traffic, and household relocation, almost invariably rely upon the Gravity
Model (Hua and Porell, 1979). While Camagni and Salone
(1993) note that in the more advanced countries, urban hierarchies have become less useful as an analytical device due
to changes in communication and transport networks, they
also accept that the derivation of traditional hierarchies and
analyses with elements of Gravity Model remain “the most
elegant, abstract but consistent representation of the hierarchy of urban centers, and the model that better interprets
the spatial behavior of many economic sectors” (Huff and
Lutz, 1995). But, regardless of which method being used,
we have to create lines representing linkages or interactions
among cities. Some GIS software systems, both raster and
vector, have the capability to develop Gravity Model between points. Many GIS software possess functions to create
lines between two points by the coordinates of these two
points. Here the program we will use for this purpose in
our following example is a quotation of ARC/INFO program
from Murayama and Ono (1993).
When we get the data of cities and linkages prepared, we
can use a model to reveal the spatial structure and mechanism of urban system. An extensively used model for this
process is Graph Theory. Based on the simple idea of points
interconnected by lines, Graph Theory combines these basic ingredients into a rich assortment of forms and endows
these forms with flexible properties, thus making the subject
a useful tool for studying many kinds of systems (Busacker
and Saaty, 1965).
In short, GIS is a useful but incomplete set of spatial
analytical tools. In many cases of urban system analysis, we
have to combine GIS tools with statistical analysis software,
input/output modeling packages designed to model systems
throughputs, mathematical modeling tools providing enhanced mathematical computations, geostatistical packages
designed for advanced spatial analysis or subsurface modeling, or even advanced macro language packages (called GIS
applications development), designed to simplify the GIS tool
kit for a particular set of tasks (DeMers, 1997).
Delimitation of nodal structure of urban system
Here we will take an example of the urban system of China
to illustrate how to use the GIS tools to analyze urban
systems.
In the 1980s and 1990s, a series of new policies have
been enforced in China. All of these policies have con-
215
tributed to China’s emergence as the most rapidly growing
economy in the world and have dramatically changed its
economic structure and pattern of regional development.
Correspondingly, significant changes also have taken place
within the urban system of China. With such a background,
attention is drawn to the mechanism of development of the
national urban system. Although a large number of studies
have been carried out in the field of urban systems in China,
some questions are still left unsolved. First of all, little is
known what the nodal structure of national urban system is
and how the nodal structure evolved in China.
Goncalves and Ulysséa-Neto, 1993) and it’s advanced adaptation Competing Opportunities Model (Tomazinis, 1962;
Murayama, 1992). In other words, we think the spatial interaction distribution from a certain origin city is governed
by the attractions exerted by the existing opportunities in
each particular destination. Therefore, we define the total
probability of the interaction from city i to city j as
n
pj
(i = j, k ≥ j )
(2)
Pij =
k
k=j
pl
From points to lines
But we find that although the Gravity Model presents the
shortcoming of disregarding the effects of the intervening
opportunities, the Intervening Opportunities Model suffers
from omission of the nonlinear effects of competing accessibilities of the alternative destinations. An extreme example
is that even each destination city changes its location if only
it keeps the same distance from the origin city, the total probability will be constant. Here I use an adaptation developed
by Fotheringham (1983) to solve this problem of concentric
circles, and emphasize the accessibility relation as the rate
of the accessibility of origin city to that of destination city.
I use the well-known population potential as the measure
of accessibility. Thus the accessibility relation Aij can be
defined as
n
n
pm
pm
Aij =
(3)
dmi
dmj
m=1
m=1
The most difficult point in the analysis of China’s urban system is the lack of data. There are only few data on cities
published in China, let alone the national O-D data between
cities. It is just the good case that we can use a model to
simulate the spatial structure with available data by GIS.
Data for statistical analysis are collected from the Urban
Statistical Yearbook of China (State Statistical Bureau, 1986,
1996). The area of study encompasses 30 provincial-level
administrative units. I selected all the cities with nonagricultural population over 100,000 as sample cities. Therefore,
there are 246 sample cities in 1985 and 488 in 1995.
As Figure 1 shows, our analysis begins with the data input for all the sample cities. Many kinds of O-D data have
been used to investigate the modal structure of urban systems. But the distribution and composition are quite different
with each kind of data. With a comprehensive result of urban
aggregation, I use nonagricultural population as attribute of
cities in this research. The nonagricultural population is not
only an important criterion for city designation, but also a
reliable indicator of urban development because of the administrative/statistical changes (Cui, 1992; Hamer, 1990;
Hsu, 1994).
Since the O-D data of linkages is absent, our next work is
to build a model to estimation the interactions among cities
(Du, 2000). The Gravity Model, which is the most widely
used model in geography for the calculation of interaction,
will be modified and employed in this research as equation
(1) shows.
Tij = Vi Pij Aij (i = j, Tii = 0, i = 1, 2, · · · , n,
(1)
j = 1, 2, · · · , n)
where Tij is the interaction between city i and j , Vi is the
scale of origin city i, Pij is the total probability of the interaction from city i to j , Aij represents the accessibility
relation of city i and j , n is the number of cities in the urban
system.
Put simply, the Vi in equation (1) can be replaced in
this study by the nonagricultural population pi . From the
standpoint of geographical study, the spatial distribution
pattern of individual cities affects the interaction greatly.
Furthermore, it is clear that the deterrence effect on interaction is not always constant. That is the meaning of the
total probability of interaction Pij in our model. Useful
ways to solve such spatial problems is the thought of intervening opportunities (Stouffer, 1940; Schneider, 1960;
l=1
m=i
m=j
where pm is the nonagricultural population of city m, dmi is
the direct distance from city m to i.
With the fusion of urban scale, total probability and
accessibility relation, our model can be written as:
n
n
n
pj
pm
pm
Tij = pi ·
·
(i = j, k ≥ j )
n
d
i
d
m
mj
m=1
m=1
k=j
pl m=i
m=j
l=1
(4)
By equation (4), we can estimate the interaction structure
of “LINE” in Figure 1. And this is the process to calculate
linkages among cities from attributes of cities.
From points and lines to nodal structure
In order to examine the interdependency of urban system,
I emphasize both to and from interactions and define the
linkage from city i to j as Lij , and
Lij = Tij + Tj i
(i = j, Lii = 0)
(5)
Further more, an indicator measuring the effect powers
of individual cities within the urban system is defined as
potential Gi .
n
Gi =
Lij
(i = j )
(6)
j =1
Trying to prove the reliability of this model, we calculate
the correlation coefficients between total interactions (calculated by proposed model) and the amount of freight traffic
216
Figure 2. Nodal structure of urban system of China.
217
Figure 3. Potential distribution and spatial structure of urban system of China.
(statistic data). The value is 0.848 for 1985 and 0.857 for
1995. Both of the two coefficients have a high value and
are significant at 1% level. This constitutes good evidence
to show the reliability of our proposed model for the urban
systems in China.
In this research, the interaction data calculated with my
proposed model are analyzed by method of Graph Theory.
First, I extract the largest linkage of each city. Then rank
the cities according to the potential of equation (6). Furthermore, connect the largest linkage from cities of lower potentials to cities of higher potentials. A city is not subordinate
to any city whose potential is lower than it.
Investigating the China’s urban system by the approach
mentioned above, we obtained the results of nodal structure
shown in Figure 2. In 1985, Beijing, Shanghai, Chongqing,
Urumqi and other 10 cities are discriminated as nodal cities.
3 nodal cities (Harbin, Shenyang and Tianjin) are subor-
dinate to Beijing, 2 nodal cities (Wuhan and Guangzhou)
to Shanghai, and 5 nodal cities (Chengdu, Guiyang, Kunming, Xi’an and Lanzhou) to Chongqing. None of these
10 nodal cities is dominated by Urumqi. In 1995, Beijing,
Shanghai, Chongqing and other 13 cities are recognized
as nodal cities. The nodal cities being subordinate to Beijing increased to 5 (Harbin, Changchun, Shenyang, Dalian
and Zhengzhou), while that to Shanghai also increased to 3
(Wuhan, Guangzhou and Qingdao). The nodal cities that are
subordinate to Chongqing remained at 5.
The most significant characteristic of the spatial structure of the urban system of China is that the national urban
system dispersed into 3 regional systems in both 1985 and
1995. Even in 1995, there is not any city powerful enough
to integrate all of the regional subsystems into a complete
system. In general, there is a lack of a fully integrated urban
system at the national level. The socio-economic reasons
218
Figure 4. Distribution and changes in potential of nodal cities.
for such spatial structure can be found in other studies on
China’s urban systems (Murphey, 1974; Skinner, 1977; Pannell, 1984; Xu, 1986; Chen, 1987; Yan, 1995; Du, 2000).
The most significant change is that Urumqi became one of
the nodal cities of Chongqing.
Create graph from map
Using GIS, we can investigate the spatial structure of urban system with maps as mentioned above. But there is
not any map that can tell us all the information we want.
It means we have to create other expression method to get
more information to reveal the mechanism of urban system.
For example, in Figure 2 only the nodal structure of urban
system is shown by the method of map. But as everybody
knows, the attributes of cities also have relation with the
nodal structure. Thus we hope to create a graph to express
such relationship in detail. The modification from map to
graph is also a modification from spatial expression to aspatial or half-spatial expression. Therefore, we can also say
it is a process to combine the geographical characteristics
(such as coordinates of x and y) and attributes of cities
(for example, population, potential, income, . . . ) together
to excavate their relationship. It is one useful way to show
the hierarchical structure in the analysis of urban system,
and GIS can provide us possibilities to finish this task with
high accuracy and efficiency. To some degree we can say,
an essential feature of spatial database systems is that they
cover an extremely wide and diverse range of applications
(Schneider, 1997).
Figure 3 shows such a modification result with the addition of potential of each city. Here we use the city of
Shanghai, which obtains the highest potential both in 1985
and 1995, as the datum of X-axis, distance from shanghai as
the X-coordinate, and potential as the Y-coordinate.
This Figure reveals that the national urban system of
China can be discriminated into three systems: Beijing ur-
ban system, Shanghai urban system and Chongqing urban
system, and each urban system possesses its characteristics.
Holding extremely higher potential, the terminal city stands
on the position of prime center and dominates a large number of cities directly in Shanghai urban system. Between
the terminal city Shanghai and the other nodal cities, there
exist great gaps of potential. All the nodal cities are only
subordinate to the terminal city Shanghai.
In the case of Beijing urban system, the differences of potentials between the terminal city Beijing and the other nodal
cities are not so great. There exist hierarchical structure and
linkages among nodal cities. The linkages from the terminal
city Beijing to the high-leveled nodal city, and then to the
low-leveled nodal city build up a significant developing axis.
In the Chongqing urban system, there exist a lot of
nodal cities, but their potentials are all not so high. Furthermore, the differences of potential between the terminal city
Chongqing and the other nodal cities are also not so great.
But there exist a hierarchical structure among nodal cities.
All the characteristics of urban systems mentioned above
are the features that we cannot get from maps such as Figure 2. Therefore, the graph is a necessary tool for urban
system analysis. From this example we can understand that
GIS provides us more possibilities to combine different data
and methods together to get new discoveries. In the analysis
of urban system, even for the same points, there are always two kinds of data. One is the geometric (or commonly
called spatial) data, including the X and Y coordinates; the
other is the set of socio-economic attributes. Only by the
analytical applications of GIS, these two kinds of data are
preserved in the same database, from which we can get more
opportunities to create new analytical methods.
Cartographic overlay for urban system analysis
One of the most powerful features of GIS is the ability to
place the cartographic representation of thematic informa-
219
Figure 5. Changes in nodal structure and the distribution of open cities in China.
tion of a selected theme over that of another. This process,
commonly called overlay, is so intuitive that its application
long preceded the advent of GIS. With the method of cartographic overlay, the spatial correspondence could be directly
related to cause and effect. GIS can provide easily available
map overlay procedures that may result in the development
of new hypotheses, new theories, or even new laws about
these pattern similarities (DeMers, 1997). It can also increase our ability to knowingly lie with maps well beyond
what was possible before GIS could be used to compare
spatial phenomena (Monmonier, 1991).
The evolution of the urban system in fact is a result
of the socio-economic development. Urban systems change
through time in a variety of ways as the social, economic, technological and geographical conditions around
them evolve (Huff and Lutz, 1995). Any change in the spatial structure and socio-economy of urban systems can alter
other factors, and thus create new patterns of spatial structure
and socio-economy. With such a dynamic interdependence
and mechanism, urban systems develop and evolve. Therefore, method of cartographic overlay is a powerful way to
investigate the variety developmental factors of urban system. In this part, we will look closely at our options for
overlay operations for urban system analysis.
We select the 12 cities that have been nodal cities in
both 1985 and 1995 as samples to investigate the changes
in spatial structure of urban system of China. Distribution
and changes of potential of these nodal cities are shown in
Figure 4. Among the 12 nodal cities, Guangzhou shows the
highest change rate of 280.2%, and Chongqing shows the
second (268.4%). It indicates a significant regional characteristic. The three nodal cities of Shanghai urban system,
Shanghai, Guangzhou and Wuhan, all surpass the average
of potential change rate. The three nodal cities of Beijing
urban system (Beijing, Shenyang and Harbin) show a pattern of lower development with lower change rate values. In
the Chongqing urban system, four nodal cities (Chongqing,
Chengdu, Kunming and Xi’an) present a pattern of great
development while the other two nodal cities (Lanzhou and
Urumqi) are under the average level. But we cannot find out
what socio-economic factors caused such changes if we only
look over the nodal structure of 1985 and 1995 in Figure 2.
The most important occurrence since the 1980s in China
is the enforcement of the Open Policy, which was aimed
to achieve the goals of economic development through the
introduction of foreign investment and advance technology.
Until 1985 China extended the outward-oriented economic
strategy to the entire coastal belt, including four Special
220
Economic Zones and 14 Coastal Open Cities. These actions
established the bases for an open economy in China. As evidence, in the last five years of the decade (1985–1989), the
level of total foreign investment was 10 times greater than
that of the preceding five years (Chen, 1991). The effect is
so great that we can say that the year of 1985 has become
a historical turning point. That is the main reason why we
think the Open Policy may also affected the spatial structure
of urban system of China.
Figure 5 presents us an application of overlay with three
layers: (1) changes in potential of nodal cities, (2) changes
in the largest linkages of nodal cities, and (3) the distribution
of the Special Economic Zones and Coastal Open Cities.
Combining the layer of the largest linkages with that of
potential, we find that the remarkable development only happened in the linkages between the terminal cities and their
directly connected nodal cities. That is to say, from 1985 to
1995 the interactions between the terminal cities and their
directly dominated nodal cities have been intensified, but
the interactions between low-level nodal cities have not developed yet in Beijing urban system and Chongqing urban
system. Such features can be considered as an important
structural characteristic in national urban system of China.
Furthermore, we overlay the layer of the Special Economic Zones and Coastal Open Cities to the two layers
above in order to find the development mechanism of urban system. We know that there are also three Coastal Open
Cities (Dalian, Qinhuangdao and Tianjin) distributing within
the sphere of Beijing urban system, but why this area present
a lower development pattern compared with Shanghai urban
system? In the case of Shanghai urban system, the nodal
cities Shanghai and Guangzhou are all Coastal Open Cities;
however there isn’t any nodal city obtains the qualification
of open cities in the Beijing urban system. This phenomenon
can be thought as an answer for the question above, and it
also shows the interdependence between the spatial structure
of urban system and its socio-economy. It also tells us that
with the enforcement of the Open Policy, foreign investment
became a major power for the development of China’s urban
system. Good evidence for this is the extensive development
of Shanghai and Guangzhou, especially Guangzhou.
So far we have outlined how to use GIS for the analysis of
urban system. My intent is to see the analytical capabilities
of the GIS for urban system and to watch how they operate.
The result of our experiments clearly shows that the aspect
of analyzing geographical data is one of the main purposes
of a GIS, and such purposes can also be applied to urban system. To some degree, it is up to the urban system researchers
to recognize the potential capabilities of GIS, because such
awareness will provide us with the conceptual framework
to operate on the largest ‘superset’ of geographic analysis
capabilities we have at hand. The study of urban system is a
broad research field abounding in various theories and applications. Anyway, improvements can be obtained by applying
of GIS to this field. We believe that with rapid development
GIS should be able to automate most of the urban system
analyses more specifically.
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