Computers, Environment and Urban Systems
29 (2005) 595–616
www.elsevier.com/locate/compenvurbsys
Changes in urban built-up surface and
population distribution patterns during
1986–1999: A case study of Cairo, Egypt
Zhi-Yong Yin a,c,*, Dona J. Stewart b,
Stevan Bullard b, Jared T. MacLachlan
b
a
Department of Marine Science and Environmental Studies, University of San Diego,
San Diego, CA 92110, USA
b
Department of Anthropology and Geography, Georgia State University, Atlanta, GA 30303, USA
c
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing,
Wuhan University, Wuhan, 400071, PR China
Abstract
Landsat TM and ETM+ images were used to examine urban growth for the Greater Cairo
area, Egypt, between 1986 and 1999, assisted by fieldwork and high spatial resolution images
such as IRS, CORONA, and IKONOS. During the study period, urban land (collectively classified as built-up surface) increased significantly. Most of the increases in urban land came
from the conversion of desert land and farmland on the Nile Delta. When changes in builtup surface were analyzed against census data of 1986 and 1996, it was found that population
per unit area of built-up surface may serve as a good indicator of urbanization. This metric
offers a different measure from the traditional calculation of population density and provides
a measure of urban living environment by reflecting the dynamics of both population and
urban land use growth. For the entire study area, population density increased by 26.8% from
7158 persons/km2 to 9074 persons/km2 during 1986–1999. In the mean time, the total built-up
area increased by 33.7% from 344.4 km2 to 460.4 km2, while population per unit of urban land
decreased from 27,188 persons/km2 to 25,799 persons/km2, indicating that urban land use
*
Corresponding author. Address: Department of Marine Science and Environmental Studies,
University of San Diego, San Diego, CA 92110, USA. Tel.: +1 619 260 8864; fax: +1 916 260 6874.
E-mail address: [email protected] (Z.-Y. Yin).
0198-9715/$ - see front matter 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compenvurbsys.2005.01.008
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Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
growth in terms of construction and infrastructure may have outpaced population growth in
Cairo overall. However, such changes were not evenly distributed. While most increases in
built-up surface were found in the outer margin of the greater Cairo area, population per unit
built-up area increased most significantly in the suburbs and to the south of the city. Such
changes reflected the nature of housing demand and the mechanisms that governed the urbanization process in the Greater Cairo area.
2005 Elsevier Ltd. All rights reserved.
Keywords: Urbanization; Remote sensing; Land use/land cover; Cairo, Egypt
1. Introduction
Cairo is the largest city on the African continent and dominates the urban system
in Egypt (Fig. 1). The estimated population of 9.65 million in 1995 (World Bank,
Fig. 1. Study area—the Greater Cairo area, ground truth points examined during May 2001, and 39
reference points used in evaluation of the Internal Average Relative Reflectance (IARR) correction. The
image background is Band 4 (NIR) of the Landsat 7 ETM+ of December 11, 1999.
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
597
1997), was nearly double its population of 5.074 million in 1976 (Sobhi, 1987). Indeed, during the mid-1980s, the cityÕs population increased at a rapid rate of 2.6%
a year (Denis, 1997). A densely built-up core, parts of which were developed as early
as the 10th century AD, characterizes the city. Much of the city, especially to the east
and the west, is surrounded by non-arable, desert land. Past growth of the city has
typically been to the north, onto prime agricultural land in the Nile Delta or, to a
lesser extent, to the south along the Nile River.
Until the late 1980s, the Cairo urban agglomeration was tightly bound, with little
decentralization and relatively little sprawl in light of its rapidly growing population.
The spatial expansion of the city was constricted by government ownership of the
surrounding desert land and intentional urban planning policies designed to channel
population growth from Cairo to new, distant urban centers (Stewart, 1996). In the
past decade, however, EgyptÕs economy has been liberalized, many of the centrallycontrolled urban planning policies have been replaced by market-driven plans,
including the sale of land to private real estate developers and thus changing the
political-economic conditions for urban growth (Stewart, 1999). This political economic change provides the backdrop for a much-needed study on urban land use
changes during the critical period from late 1980s to the present. Most previous
studies on Cairo (e.g., Harris & Wahba, 2002) used population data for the entire
city or at the qism level (similar to a US census block group), while in the current
study we used a data set at a finer spatial resolution to examine the population
distribution pattern of the city.
Remote sensing technology has greatly facilitated investigation of land use/cover
changes. For example, Sultan et al. (1999) used the Landsat Multispectral Scanner
(MSS, 79 m resolution) and TM images to study the urbanization process on the
Nile Delta during the 70s and 80s. They found that urban areas increased 58% in
18 years since 1972. If such a trend continued, Egypt could lose 12% of its agricultural area to urbanization by 2010. They also found that the growth around small
villages was as significant as the growth around major cities. Another study found
accelerated urban growth at the expense of agricultural land during 1987–1995 as
compared to the period 1950–1987 (Fahim et al., 1999). There has long been an urgent need for land use management policies based on an accurate understanding of
current land use conditions to ensure future sustainable growth (Hefny, 1983; Biswas, 1993).
This study used image processing and analysis integrated in a geographic information system (GIS) environment to assess spatial change in the greater Cairo region
between 1986 and 1999. Specifically, we sought to assess changes in the spatial extent
of metropolitan Cairo and changes in population distribution patterns within the
urban conglomeration. Spatial patterns of urban land use and population distribution were compared quantitatively. Such analyses would shed light on the overall impact of political-economic environment and policy changes on urbanization
processes for Cairo, a large city of a developing country, which can provide insight
into the urban growth patterns under similar conditions. The information of land
use/cover conditions and changes over time obtained through this study can be used
in formulating management policies of the national and municipal governments.
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2. Data and methods
Since the beginning of the Landsat program in the early 1970s, it has become one
of the most widely used and available sources of remote sensing imagery. Thematic
Mapper (TM) images, with a 30 m ground resolution (for visible, NIR and MIR
bands, 60 m for TIR band) and seven spectral bands, have proven to be a very effective tool in regional-scale urban studies, especially for areas where accurate maps
and past surveys are not available, as is true in many developing countries (e.g.,
Chen, Zeng, & Xie, 2000; Gupta & Munshi, 1985; Hashiba, Takasaki, Kameda, &
Sugimura, 1998; Kwarteng & Chavez, 1998; Masek, Lindsay, & Goward, 2000).
The Landsat 7 Enhanced Thematic Mapper (ETM+) images have a higher spatial
resolution for the thermal infrared band (60 m as compared to 120 m) and an additional panchromatic band with 15 m resolution (NASA, 2002). Since the first
appearance of the TM images in 1982, there has accumulated a large archive of
images worldwide, facilitated by the US GovernmentÕs ‘‘Open Sky’’ policy (Lillesand
& Kiefer, 1999). Ground stations in over a dozen countries can receive and archive
images directly from the satellite within their operational range.
The Landsat 5 Thematic Mapper (TM) image of December 15, 1986 and Landsat
Enhanced Thematic Mapper (ETM+) image of December 11, 1999 were classified
for the land use/cover and change detection analyses of this study. The seasonal
proximity of the acquisition dates of these images minimizes differences in sun
azimuth and surface differences in vegetation. However, because these images were
acquired by different platforms and are not exact ‘‘anniversary images’’, they are
not suited to direct image differencing change detection methods. The potential
effects of atmospheric conditions may introduce uncertainties in direct image comparisons (Song, Woodcock, Seto, Lenney, & Macomber, 2001). These images depict
a wide variety of land cover types including desert, water, vegetation, and urban
features covering an extensive range of reflectance.
Prior to classification, subsets (approx. 4470 km2) of the 1986 and 1999 images
were used to reduce computation and image interpretation time. The spectral bands
of the images (excluding the thermal band) were converted to relative energy levels
by normalizing the pixel values to the overall energy of each band in the subset scene.
ERDAS ImagineÕs Internal Average Relative Reflectance (IARR) procedure was
applied to both images (ERDAS, 1982–94; Kruse, 1988). This relative radiometric
correction method is based on the theory of band cancellation, in which information
obtained from multiple bands is used to cancel out the atmospheric effect on the
same object (Jensen, 1996). The algorithm is based on the assumption that the scene
average spectrum (digital number or DN) represents the atmospheric contribution
and that the atmospheric effect is uniform across the scene. Therefore, by normalizing
the pixel DNÕs against the scene average, the majority of the atmospheric effect
would be removed.
During May of 2001, the research team conducted fieldwork in the greater Cairo
area. Over 100 points were located using global position system (GPS) receivers and
described for the land use/cover conditions. The focus was the urban–rural interface
where image interpretation might be difficult. Fig. 1 shows the locations of the
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599
ground truth points. The fieldwork and subsequent image analysis were greatly
assisted by high spatial resolution images (IRS panchromatic image with 5 m resolution, IKONOS image with 1 m resolution, and CORONA image with 2–5 m
resolution) and maps of various scales (from 1:500 to 1:50,000). Fig. 2 contains subsets of the images of different spatial resolutions to show the levels of details of urban
land uses.
Bands 1 through 5 and 7 of the TM and ETM+ images (visible, NIR and MIR
bands) were used in an unsupervised classification using the ISODATA procedure
(ERDAS, 1982–1994). Various numbers of clusters (20–100) were created in experimental trials. Through analysis of signatures extracted from unsupervised classification, based on separability measured by Euclidian distance and transformed
divergence (Jensen, 1996), we concluded that 60 clusters would ensure sufficient separations among major land use/cover types while maintaining a minimal amount of
overlap among the resulted clusters, so that the post-clustering interpretation would
be more efficient.
Since the focus of this study is the spatial extent of all urban land uses rather than
internal variation of urban structure, we designed a rather simple classification system with seven classes (Table 1). We defined all man-made surfaces as built-up surfaces, representing all types of land uses for urban and rural development. Field
observation and the subsequent in-house imagery analysis suggested that there are
many mixed pixels in the study area. On the Nile Delta, we often encountered mixed
pixels of cleared land and farmland, which have similar spectral characteristics to
Fig. 2. Subsets of images of different spatial resolutions, from Landsat ETM+ (30 m, 1999), IRS
panchromatic (5 m, 1999), CORONA (2–5 m, 1968), to IKONOS (1 m, 2000).
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Table 1
The land use/cover classification system for the Greater Cairo area, Egypt
Class names
Description
Water
Shallow water/
water edge
Built-up
Deep water of the Nile River, major canals
Shallow water, canals, sometimes water mixed with features along water
edges, including vegetation and man-made features
All types of man-made surfaces, including residential, commercial,
industrial, transportation, etc.
Barren ground mixed with vegetation on the Delta, and barren ground
or urban land use mixed with vegetation at the urban–rural interface
Mostly farmland on the Delta, but also including vegetation in urban
settings, such as parks, trees in residential complexes, and golf courses
Mostly found along the urban fringe adjacent to the desert, some
representing new development in desert areas
Desert sands and rocks
Barren/urban/
vegetation mix
Vegetation/farmland
Desert/urban mix
Desert
mixed pixels of built-up surface and farmland. Similarly, we also found mixed pixels
of urban land and desert along the margin of the Delta. After image clustering, each
cluster was examined against locations of known land use/cover conditions (ground
truth points), color composites of the images, hi-resolution images, and maps. Then
a land use/cover class was assigned to each of the clusters. An accuracy assessment
was performed for images of both years by randomly selecting 100 pixels for each
class and then manually interpreting them with the help of color composites and high
spatial resolution images (CORONA and IKONOS). The older CORONA image
was useful in places where land use/cover had not changed much over time. Finally,
a 3 by 3 majority filter was employed to reduce the salt-and-pepper effect before the
images were further analyzed for land use/cover changes (Lillesand & Kiefer, 1999).
The classified images were integrated with the census data in a geographic information system (GIS). Using population data from the 1986 and 1996 censuses conducted by the Central Agency for Population Mobilization and Statistics
(CAPMAS), data were collected at the smallest possible census unit, the shiyakhah
(plural: shiyakhat), which is roughly equivalent to the US census blocks. The average
size of all shiyakhat in the greater Cairo region is approximately 320 ha. Creation of
the polygons for the census units was done by reference to historic cadastral maps
and, most importantly, through collaboration with the Centre dÕÉtudes et de Documentation Économiques, Juridiques et Sociales (CEDEJ) at Cairo, which had
worked with CAPMAS to create this set of polygons. The availability of high-resolution satellite imagery facilitated editing of the polygons to correspond accurately to
the streets and other features that formed the census unit boundaries. There is a
three-year difference between the image date (1999) and the census date (1996). To
ensure a valid comparison of urban land use patterns and population distributions
between the two time periods (1986 vs. 1999), the 1999 population was estimated
for each polygon based on the mean growth rate during 1986 and 1996. For a few
polygons, negative population figures resulted from the decreasing population rates
during 1986–1996, which were replaced by a population of zero.
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601
Once appropriate census polygons were constructed, the proportion of built-up
surface, based on image classification, was summarized for each shyakhah and then
related to the population density, a widely used measure of urbanization intensity
(Edmonston, Goldberg, & Mercer, 1985; Jensen & Cowen, 1998; Lo, 1995; Wang
& Zhou, 1999). We also calculated the population per unit area of built-up surface
for the census units (shiyakhat) to use as a measure of the intensity of urban development. The spatial patterns of both measures of population distribution, population density by total area of the census unit and that by urban built-up area, were
compared. When population data for small-area units are available through field
sampling or street-block level census counts, it is possible to use satellite imagerybased land use/cover data to produce more realistic population distribution patterns to overcome the limitation of choropleth mapping based on census units
(Harvey, 2002; Lo, 1986, 1995; Mennis, 2003). For this project, however, the shyakhah level data provided significant improvement in spatial resolution over the
past studies.
3. Results and discussion
3.1. Classification
To illustrate the effectiveness of the IARR procedure, we selected a total of 39 reference points within the study area on the images of the two time periods (Fig. 1).
For each major land use/cover types, including urban, vegetation, water, and desert,
5–12 points were selected depending on the variation within the type. Careful visual
interpretation and the usage of the high spatial resolution images at different time
periods ensured that those points maintained the same land use/cover types in
1986 and 1999. For example, for urban land uses, we only selected points in parts
of the city that had gone through relatively minor or no changes (as judged by the
high spatial resolution CORONA and IKONOS images). We also selected points
from the surfaces of the Great Pyramids on the Giza Plateau to represent the constant surface conditions. Additionally, we intentionally selected points that were
under smoke or haze during one of the two time periods to reflect the atmospheric
influence. For each land use/cover type, the pixel DNÕs before and after the IARR
procedure were plotted with all points and all bands being lumped together (Fig.
3). Assuming no atmospheric influence, the only difference in the DNÕs should be resulted from the differences in the sensor systems, and therefore, the DNÕs for the
same land surface type should have a perfect linear relationship. The scattered pattern should then represent the atmospheric effect. It is clear that the IARR procedure
is effective in eliminating most of the atmospheric effect (Fig. 3).
In the classification process, we noticed misclassification of desert surface into the
urban–desert mix or built-up classes in an area to the southeast of the city. Visual
inspection indicated that there was no urban land use in this area on both the
1986 and 1999 images. This was mostly due to the complex terrain with valleys
and shadows that produced similar spectral characteristics as urbanized areas. The
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120
120
Before:
y = 0.5204x + 37.384
R2 = 0.6316
100
Before:
y = 0.8132x + 22.714
R2 = 0.704
100
80
1999 DN
1999 DN
80
60
60
40
Before
After
Before
After
After:
y = 1.0234x + 21.996
R2 = 0.8749
40
20
Before
After
Before
After
After:
y = 0.9813x + 24.2
R2 = 0.8148
20
0
20
40
60
(a)
80
100
120
0
20
40
(b)
1986 DN
140
60
80
100
120
1986 DN
240
Before:
y = 0.6833x + 15.277
R2 = 0.7627
120
Before:
y = 0.5582x + 68.515
R2 = 0.3771
200
1999 DN
100
1999 DN
80
60
160
120
40
Before
After
Before
After
After:
y = 1.1907x + 17.124
R2 = 0.9697
20
0
40
0
10
20
(c)
40
60
80
100
120
40
120
160
1986 DN
200
240
250
Before:
y = 0.7798x + 30.479
R2 = 0.8831
200
150
100
50
Before:
y = 0.8712x + 20.177
R2 = 0.8072
200
1999 DN
1999 DN
80
(d)
1986 DN
250
Before
After
Before
After
After:
y = 0.9333x + 26.401
R2 = 0.9327
150
Before
After
Before
After
100
50
After:
y = 1.0931x + 18.979
R2 = 0.9223
0
0
0
(e)
Before
After
Before
After
After:
y = 1.0856x + 16.408
R2 = 0.9703
80
50
100
150
1986 DN
200
0
250
(f)
50
100
150
200
250
1986 DN
Fig. 3. Image digital numbers before and after the IARR procedure: (a) build-up surface, (b) vegetation,
(c) water, (d) desert surface, (e) Great Pyramids, (f) all types.
misclassified pixels are mostly outside the area covered by the census data. Nevertheless, we masked out these pixels and manually classified them as desert. This proce-
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
603
dure was performed after the accuracy assessment. Fig. 4 shows the classified images
of 1986 and 1999 with seven land use/cover classes.
Table 2 shows the results of the accuracy analysis. The overall accuracy measures
for both years are surprisingly similar at 87%, which are comparable to or better
than the accuracies reported in other studies using similar image sources (Allen &
Kupfer, 2000; De Bruin & Gorte, 2000; Phinn & Stanford, 2001). For both years,
the accuracy of the built-up surface class was relatively high, while the accuracies
of the desert and urban–desert mix classes were among the lowest. Table 3 contains
the area of the land use/cover types obtained from the classified images.
By comparing the 1986 and 1999 images for the Nile Delta region, it was determined that the barren ground–urban–vegetation mix class contained only a small
portion of built-up surfaces. It mostly represented fallow farmlands on the Nile Delta. On the other hand, the urban–desert mix class represented mostly new developments at the image acquisition time, such as road and city infrastructure
constructions, perhaps a prelude to residential related developments. Due to relative
high inaccuracies in classification of the desert–urban mix class for both years (Table
2), we decided that in the following analysis these mixed classes should be excluded.
Fig. 5 shows the changes in spatial extent of the built-up class in the Greater Cairo
area by image differencing. The total area of urban built-up surface was 344.4 km2 in
Fig. 4. Land use/cover classification of the Landsat images (after a 3 · 3 majority filter): (a) based on
December 15, 1986 Landsat TM image, (b) based on December 11, 1999 Landsat ETM+ image.
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Table 2
Accuracy assessment of the classification of the 1986 and 1999 TM images
Classes
Reference data
Deep
water
1999 image classification
Deep water
94
Shallow water
18
Built-up
Barren–vegetation mix
Vegetation/farmland
Desert–urban mix
Desert
Grand total
112
1999 image classification
Deep water
88
Shallow water
4
Built-up
Barren–vegetation mix
Vegetation/farmland
Desert–urban mix
Desert
Grand total
92
Shallow Built-up
water
5
78
2
Barren–
vegetation
mix
12
81
1
94
Desert–
urban
mix
Desert Grand
total
1
89
4
99
7
1
1
85
Vegetation/
farmland
110
94
1
96
3
1
10
2
90
2
2
4
97
104
1986 Accuracy
2
5
72
14
27
86
100
100
100
100
100
100
100
88
120
700
1
92
91
101
2
103
88
27
12
73
100
100
100
100
100
100
100
118
88
700
2
1
3
1999 Accuracy
Classes
ProducerÕs
accuracy (%)
UserÕs
accuracy (%)
Classes
ProducerÕs
accuracy
(%)
UserÕs
accuracy
(%)
Deep water
Shallow water
Built-up
Barren/vegetation
Vegetation/farmland
Desert–urban mix
Desert
83.93
91.76
97.80
90.00
97.87
81.82
71.67
94.00
78.00
89.00
99.00
92.00
72.00
86.00
Deep water
Shallow water
Built-up
Barren/vegetation
Vegetation/farmland
Desert–urban mix
Desert
95.65
86.17
95.05
86.54
94.17
74.58
82.95
88.00
81.00
96.00
90.00
97.00
88.00
73.00
Total accuracy
87.14
Total accuracy
87.57
1986 and 460.4 km2 in 1999, respectively. The 1986 figure conforms closely with an
earlier estimate of 350 km2 for the Greater Cairo area by Ibrahim (1985).
3.2. Spatial pattern of urban land use growth
Figs. 4 and 5 show a significant increase in the extent of CairoÕs urban area. Most
notable are the east–west expansion of the city onto surrounding desert and the
growth along a belt north of the city on the Nile Delta. A major growth vector to
the northeast of the city included road access and proximity to the international air-
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605
Table 3
The area of land use/cover types in Grater Cairo region, 1986 and 1999
Land use/cover types
1986 area (km2)
% of total area
1999 area (km2)
% of total area
Clouds/unclassified
Water
Shallow water/water edge
Built-up
Barren/urban/vegetation
Vegetation/farmland
Desert/urban
Desert
Total area
0.0
28.7
2.9
344.4
11.0
738.7
73.4
252.7
1451.9
0.0
2.0
0.2
23.7
0.8
50.9
5.1
17.4
100.0
12.3
30.1
2.9
460.4
33.4
634.7
93.9
184.3
1451.9
0.8
2.1
0.2
31.7
2.3
43.7
6.5
12.7
100.0
port. The image analysis also identified ‘‘pre-urban’’ nodes in the desert, sometimes
20 km distance from Cairo. While these nodes were not included as part of the contiguous Cairo urban agglomeration in this study, they should be noted as future sites
of urban growth, eventually being connected to the main agglomeration. Indeed, this
analysis substantiated the trend of emerging decentralizing forces in the spatial structure of Cairo (Stewart, 2001a). Of the total built-up area in 1999 (460.4 km2),
91.4 km2 or 19.9% were converted from vegetation/farmland or barren ground on
the Nile Delta, while 60.4 km2 or 13.1% were from either desert or urban–desert
mix class (Table 4). The area of the urban built-up class increased by 33.7% from
1986 to 1999.
3.3. Spatial pattern of population distribution
We examined the relationship between population density at the shiyakhah level
(log-transformed) and the proportions of built-up surface (Table 5). It can be assumed that the proportion of built-up surface is closely related to population density
(Mesev, 1998). We found that the built-up surface class is strongly correlated with
population density (R2 = 0.741 for 1986 and R2 = 0.683 for 1999). The weaker relationship for 1999 may be caused by several reasons. First, it was noticed that the
population in the central core of the city reduced over time, which occurred without
much change in the built-up surface since the development here has been saturated
for decades. Additionally, the more recently developments in the outskirts of the city
tend to have lower population density to accommodate the more affluent class. Such
trends would increase the variability in the relationship between population density
and built-up surface. Assuming stable functionalities of the city, such relationships
can be used to predict population density for areas of interest between census intervals and provide guidance to various urban and regional planning solutions in the
near future. Regression models (Table 5) of the two years suggest that the rate of
increase of population density with increasing built-up surface was reduced in
1999 (3.077) as compared with that in 1986 (3.525). In other words, for the same
amount of built-up surface, it would accommodate a smaller population during
the later period. This could be an indicator of a general reduction in housing density,
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Fig. 5. Changes in built-up surface by image differencing of the classified images.
especially for the newly developed areas of high-priced housing for the upper-income
class, the outward migration of population and businesses from the crowded central
city core, or recent industrial/commercial/infrastructure developments that have
lower population density as compared to residential developments.
Many studies have pointed out the tendency of decentralization in large, wellestablished cities around the world (Brun & Wheeler, 1980; Cervero, 2001; Hall,
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607
Table 4
Summary of population and urban land use changes in the greater Cairo region during 1986–1999
Variables
Number of shiyakhat
Total area (km2)
Population density (persons/km2)
Built-up surface (km2)
Proportion of built-up surface
Population/km2 built-up surface
1986
1999
Changes as %
7158
344.4
0.237
30,175
453
1451.9
9074
460.4
0.317
28,617
26.8
33.7
33.7
5.2
Area of built-up surface in
1999 from the 1986 classes
Area (km2)
% of total built-up surface in 1999
Barren ground/vegetation
Vegetation/farmland
Desert/urban
Desert
1.8
89.6
27.4
33.0
0.4
19.5
5.9
7.2
Table 5
Results of regression between population density (log-transformed) and proportion of built-up surface by
shiyakhat
Models
Intercept
Built-up
1986
1999
Coefficients
t
Signif.
R2
Coefficients
t
Signif.
R2
2.740
3.525
34.376
33.868
0.00000
0.00000
0.741
2.880
3.077
37.595
30.882
0.00000
0.00000
0.683
1998; Pacione, 2001; Saurez-Villa, 1989). In developed countries, decentralization
occurred since the 1970s, with such problems as high crime rates, loss of business
and employment, traffic congestion, and environmental pollution. In developing
countries, this started in the 1980s and 1990s (De Souza, 2001; Firman, 1998;
Gaubatz, 1999; Ribeiro, 1995; Wu, 1997). In most cases, such trends are quantified
by decreasing population density in the core or central business district of these cities. In this study, decentralization is most notable when population density is examined by two different methods: population density by census unit and population
density by built-up area in the census unit.
Analysis of the change in population density during the study period finds
decreasing density in the central core area and increasing density in peripheral parts
of the city, especially the desert fringe (Fig. 6). Shiyakhat centered around the historic core and aging central business district, demonstrate a significant population
density decline. The 1999 population density for these shiyakhat, when expressed
as a ratio with 1986, commonly have values below 0.75. These shiyakhat, especially
those experiencing the greatest population density decline, include some of the cityÕs
poorest neighborhoods. Much of the housing stock in these areas is in poor condition. There is no available land on which to construct new housing and infrastructure is poor. Economic activity has moved away from these areas, as a result of
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Fig. 6. 1999 population density and the changes in population density by shiyakhat, with 1999 population
density displayed as ratios of the 1986 population density.
both political-economic change and government policies that have moved economic
activity out of the central core. Shiyakhat with ratios of .75–1.25 indicate areas of
little change in population density between 1986 and 1999. These polygons occupy
a middle area between the traditional central business district and the expanding urban periphery. Shiyakhat with increasing population density are perhaps the clearest
indicator of the driving forces of decentralization in the Cairo agglomeration. These
shiyakhat demonstrate an increase of population density between 1986 and 1996.
Most of the polygons exhibit 1999/1986 ratios between 1.25 and 5, though ratios
of higher than 5 were also present, mostly along the east and northeast growth
vectors.
3.4. Analysis of population density by built-up area
Though the change in population density by census units is the typical method
of assessing changing population patterns, it may not fully reflect the expansion of
urban land uses because different urban land uses may have significantly different
population distribution patterns. Fig. 7 shows the proportion of urban land surface
in 1999 and the changes during the study period as ratios to that of 1986. While
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
609
Fig. 7. 1999 proportion of urban land surface (weighted) and changes displayed as ratios of the 1999
values over the 1986 values by shiyakhat.
the central core of the city possessed high proportions of built-up surface, the
greatest changes are seen along the peripheral areas. To illustrate the dynamics
in both population and urban land use growth, the utilization of satellite imagery
in this project allowed us to calculate population density by built-up surface (Fig.
8). This figure is especially important for understanding population change on the
cityÕs periphery, where census units still contain much undeveloped or agricultural
land.
As expected, the central core—which exhibited a decline in population density by
census unit—also exhibits a decline in population density by built-up area. As the
total amount of built-up surface for the core was unchanged during the study period,
population appears to be moving out of the downtown zone. Indeed the middle zone
also experienced no significant change in built-up surface during the study period,
reflecting CairoÕs historical character as a dense, tightly bound city.
Though the urban periphery has already been identified as the major area for population growth, an overall decline in population density by build-up area is found.
Indeed, census units with 1999/1986 ratios below 0.75 are common. Two clusters
of very large decrease in population density by built-up area are noted, in the north
and northeast section of the city and in the desert west of the traditional core. These
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Fig. 8. 1999 population per unit area of urban land surface and changes displayed as ratios of the 1999
values over the 1986 values by shiyakhat.
clusters have been the focus of large-scale development by private contractors in
recent years, after the shift of the political-economic environment in the mid1980s. For the urban periphery as a whole, the decline in population density per
built-up area is a key indicator of the expansion of housing and construction. As
a result, the overall average population per unit area of built-up surface decreased
from 30,175 persons/km2 in 1986 to 28,617 persons/km2 in 1999.
With three different but related variables for each time period (population density,
proportion of built-up surface, and population per unit built-up area in 1986 and
1999), it is difficult to assess the overall pattern of changes. Therefore, we employed
a data redundancy reduction technique, principle component analysis (PCA), to
identify variables with similar spatial patterns between the two time periods and
those with different spatial patterns. In PCA, a new data set is generated with fewer
variables (principle components or PCs), which contains most of the information in
the original data set. Additionally, these new variables are orthogonal to each other
with correlation coefficients of zero. After an orthogonal rotation (VARIMAX),
each PC should represent one or more variables in the original data set, as indicated
by the loading values or correlation coefficients between the PC and the original vari-
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
611
ables (Johnston, 1980). For each pair of variables, e.g., population density of 1986
and 1999, if the overall spatial pattern remained the same, then these variables
should be represented by a single PC. It is critical that the PCA process produces
as many PCs as possible without under or over ‘‘factoring’’ the data. In other words,
the new data set should contain as much information as possible, while each resultant PC must individually represent at least 1 original variable.
Results of PCA are presented in Table 6. We used multiple trials to determine the
number of PC to be retained for analysis and found that four PCs rendered the best
results. When five PCs were extracted, it was clear that the new data set was over
Table 6
Results of principal component analysis (PCA)a
Number
of PCs
Original variables
3
Population density 1986
Population density 1999
Proportion of built-up
surface 1986
Proportion of built-up
surface 1999
Population per unit
built-up area 1986
Population per unit
built-up area 1999
0.551
0.405
0.944
0.727
0.869
0.261
0.229
0.196
0.031
0.952
0.251
0.036
0.049
0.062
0.975
0.123
0.527
0.758
Population density 1986
Population density 1999
Proportion of built-up
surface 1986
Proportion of
built-up surface 1999
Population per unit
built-up area 1986
Population per unit
built-up area 1999
0.439
0.366
0.943
0.844
0.830
0.299
0.230
0.062
0.025
0.093
0.363
0.018
0.938
0.315
0.007
0.028
0.019
0.135
0.942
0.297
0.039
0.269
0.432
0.856
Population density 1986
Population density 1999
Proportion of built-up
surface 1986
Proportion of built-up
surface 1999
Population per unit
built-up area 1986
Population per unit
built-up area 1999
0.455
0.377
0.949
0.824
0.836
0.282
0.208
0.089
0.030
0.131
0.326
0.026
0.230
0.210
0.053
0.942
0.303
0.003
0.040
0.054
0.017
0.131
0.943
0.305
0.006
0.034
0.265
0.415
0.869
0.001
4
5
a
Loadings
PC1
PC2
PC3
PC4
PC5
Total variance
explained
93.311%
97.854%
99.393%
On population density, proportion of built-up surface, and population per unit built-up area in 1986
and 1999 by shiyakhat, including the number of PCs extracted, total explained variance, and the rotated
PC loadings in relation to the original variables (the highest two loadings values are highlighted).
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factored as PC5 did not represent any original variable. With four PCs, PC1
(rotated) represented the proportion of built-up surface in 1986 and 1999, and
PC2 represented the population density of the two time periods. For PC3 and
PC4, however, each represented the population per unit built-up area in 1986
and 1999, respectively. This suggest that spatial patterns of population density
and proportion of built-up surface had not changed much between the two years,
but population per unit built-up area had different spatial patterns. We mapped
the scores of PC3 and PC4 for comparison (Fig. 9), with high score values representing high population per unit area of built-up surface. The scores of PC3 reveal a centralized distribution pattern of population per unit built-up area in 1986. On the
other hand, the spatial pattern for the scores of PC4 clearly shows a decentralized
pattern and the growth of population in the suburban areas in 1999, especially to
the north and northeast of the city. This further validates the dissociation between
population density and proportion of built-up surface in recent years (Table 5), as
the mechanisms or driving forces of urbanization changed from accommodating
basic housing needs in the 1970s and 1980s to meeting diversified housing demands
for different income levels and urban infrastructure development in the 1990s. In
Fig. 9. Maps to show scores of PC3 (for 1986 population per unit built-up area) and PC4 (for 1999
population per unit built-up area) by shiyakhat.
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
613
other words, our results suggest a trend of spatial segregation as the city became
more diversified in terms of socioeconomic status. The role of free market economics, newly introduced in Egypt, must also be mentioned as it encouraged the development of high-end speculative properties designed for the elite, a marked change
from the governmentÕs emphasis on large scale high-density projects to meet pressing
housing needs in previous decades.
4. Conclusions
Using the Landsat TM and ETM+ images, assisted by high spatial resolution
images and fieldwork, spatial extent of urban land uses was accurately defined for
the greater Cairo area. By integrating remote sensing and GIS technologies, the relationships between built-up surface and population were quantified. During the study
period of 1986–1999, the rate of urban growth in Cairo in terms of built-up surface
outpaced the rate of population growth rate. Two aspects of this project resulted in a
more detailed understanding of CairoÕs spatial expansion and the relationship
between urban land use change and population density. First, the analysis of population density patterns, by two different measures—census unit and built-up area—
allowed a better understanding of emerging decentralization trends in the Cairo
region. Secondly, combining the technologies of remote sensing and GIS with methods of urban geography greatly expanded the analysis and interpretation capabilities
of the research team.
The results of this case study contribute to the growing body of evidence of urban
decentralization in the developing world. Cities in the developing world, especially
the so-called mega-cities are often conceptualized as being fundamentally different
urban structures than North American cities. Indeed specific models such as the
Latin American city model and Islamic city model have been developed to understand their structure (Griffin & Ford, 1980; Stewart, 2001b). However, the Cairo case
suggests that the North American experience of decentralization may be relevant to
the developing world. There is a wide range of possible implications for Cairo,
including the potential for greater socio-economic spatial segregation as the core declines. Such an occurrence would be consistent with the North American experience.
However, we must be careful not to assume that an urban spatial structure that is
similar to the North American decentralized model is caused by the same factors.
More research is needed to understand, in comparative perspective, the processes
driving spatial change.
For Cairo, the spatial change of the urban area presents a number of planning
challenges including how to provide services over a larger geographic area. Equity
issues could be exacerbated as resources are shifted from the depopulating core to
the periphery. Given the relatively low level of car ownership, spatial decentralization could encourage a huge increase in personal automobile ownership and use, this
could severely worsen CairoÕs pollution problems. Finally, though expansion onto
desert land is certainly preferred over expansion onto prime agricultural land, rampant expansion into the desert will need to be controlled. The results of this study
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cover only a 10 year time period, yet a decentralizing trend is strongly apparent.
Early attention to this expansion by Egyptian government offices is needed to
develop an overall growth strategy for the coming decade.
Acknowledgments
This study was in part supported by a grant from Georgia State UniversityÕs Research Team Grant program (#01–006). The authors would like to thank the Centre
dÕÉtudes et de Documentation Économiques, Juridiques et Sociales (CEDEJ) in
Cairo, and Eric Denis especially, for their collaboration on this research project.
Thanks also to three anonymous reviewers who provided constructive suggestions
and comments.
References
Allen, T. R., & Kupfer, J. A. (2000). Application of spherical statistics to change vector analysis of
Landsat data: Southern Appalachian spruce–fir forests. Remote Sensing of Environment, 74(3),
482–493.
Biswas, A. K. (1993). Land resources for sustainable agricultural development in Egypt. Ambio, 22(8),
556–560.
Brun, S., & Wheeler, J. (1980). The American metropolitan system: present and future. New York: Wiley
(216 pp.).
CAPMAS (Central Agency for Population Mobilization and Statistics) (1986). General Census of
Population, Housing and Buildings, Cairo, Egypt.
CAPMAS (Central Agency for Population Mobilization and Statistics) (1996). General Census of
Population, Housing and Buildings, Cairo, Egypt.
Cervero, R. (2001). Efficient urbanization: economic performance and the shape of the metropolis. Urban
Studies, 38(10), 1651–1671.
Chen, S., Zeng, S., & Xie, C. (2000). Remote sensing and GIS for urban growth analysis in China.
Photogrammetric Engineering and Remote Sensing, 66(5), 593–598.
DeBruin, S., & Gorte, B. G. H. (2000). Probabilistic image classification using geological map units
applied to land-cover change detection. International Journal of Remote Sensing, 21(12), 2389–2402.
Denis, E. (1997). Urban planning and growth in Cairo. Middle East Report, 27(202), 7–12.
Souza, M. L. (2001). De Metropolitan deconcentration, socio-political fragmentation and extended
suburbanisation: Brazilian urbanization in the 1980s and 1990s. Geoforum, 32(4), 437–447.
Edmonston, E., Goldberg, M. A., & Mercer, J. (1985). Urban form in Canada and the United States: an
examination of urban density gradients. Urban Studies, 22(3), 209–217.
ERDAS (1982–1994). ERDAS Field Guide (3rd ed). Atlanta, Georgia: ERDAS Inc.
Fahim, M. M., El-Mowelhi, M. N., Pax-Lenney, M., Khalil, K. I., Hawela, F., & Zaki, H. K. (1999).
Identification of urban expansion onto agricultural lands using satellite remote sensing: Two case
studies in Egypt. Geocarto International, 14(1), 45–48.
Firman, T. (1998). The restructuring of Jakarta metropolitan area: a Ôglobal cityÕ in Asia. Cities, 15(4),
229–243.
Gaubatz, P. (1999). ChinaÕs urban transformation: patterns and processes of morphological change in
Beijing, Shanghai and Guangzhou. Urban Studies, 36(9), 1495–1521.
Griffin, E., & Ford, L. (1980). A model of Latin American city structure. The Geographical Review, 70(4),
397–422.
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
615
Gupta, D. M., & Munshi, M. K. (1985). Urban change detection and land-use mapping of Delhi (India).
International Journal of Remote Sensing, 6(3–4), 529–534.
Hall, T. (1998). Urban geography. London: Routledge (180 pp.).
Harvey, J. T. (2002). Estimating census district population fro satellite imagery: some approaches and
limitations. International Journal of Remote Sensing, 23(10), 2071–2095.
Harris, R., & Wahba, M. (2002). The urban geography of low-income housing: Cairo (1947–96)
exemplifies a model. International Journal of Urban and Regional Research, 26(1), 58–79.
Hashiba, H., Takasaki, K., Kameda, K., & Sugimura, T. (1998). Analysis of land use change in periphery
of Tokyo during last 25 years using the same season Landsat data. Advances in Space Research, 22(5),
681–684.
Hefny, K. (1983). Land-use and management problems in the Nile Delta (Egypt). Nature and Resources,
18(2), 22–27.
Ibrahim, S. E. (1985). Cairo: a sociological profile. In A. Evin (Ed.), The Expanding Metropolis: Coping
with the urban growth of Cairo (pp. 25–33). Singapore: Concept Media.
Jensen, J. R. (1996). Introductory digital image processing—a remote sensing perspective (2nd ed.). Upper
Saddle River, NJ: Prentice Hall (316 pp.).
Jensen, J. R., & Cowen, D. J. (1998). Extraction and modeling of urban attributes using remote sensing
technology. In D. Liverman, et al. (Eds.), People and pixels: Linking remote sensing and social science
(pp. 244). Washington, DC: National Academy Press.
Johnston, R. J. (1980). Multivariate statistical analysis in geography, a primer on the general linear model.
London: Longman (280 pp.).
Kruse, F. A. (1988). Use of airborne Imaging spectrometer data to map minerals associated with
hydrothermally altered rocks in the northern Grapevine Mountains, Nevada. Remote Sensing of the
Environment, 24(11), 31–51.
Kwarteng, A. Y., & Chavez, P. S. Jr., (1998). Change detection study of Kuwait City and environs using
multi-temporal Landsat Thematic Mapper data. International Journal of Remote Sensing, 19(9),
1651–1662.
Lillesand, T. M., & Kiefer, R. W. (1999). Remote sensing and image interpretation (4th ed.). New York:
John Wiley & Sons (724 pp.).
Lo, C. P. (1986). Applied remote sensing. Harlow: Longman.
Lo, C. P. (1995). Automated population and dwelling unit estimation from high-resolution satellite
images: a GIS approach. International Journal of Remote Sensing, 16(1), 17–34.
Masek, J. G., Lindsay, F. E., & Goward, S. N. (2000). Dynamics of urban growth in the Washington, DC
metropolitan area, 1973–1996, from Landsat observations. International Journal of Remote Sensing,
21(18), 3437–3486.
Mennis, J. (2003). Generating surface models of population using desymmetric mapping. The Professional
Geographer, 55(1), 31–42.
Mesev, V. (1998). The use of census data in urban image classification. Photogrammetric Engineering and
Remote Sensing, 64(5), 431–438.
Pacione, M. (2001). Models of urban land use structure in cities of the developing world. Geography, 86(2),
97–119.
Phinn, S., & Stanford, M. (2001). Monitoring land-cover and land-use change in a rapidly urbanizing
coastal environment: the Maroochy and Mooloolah Rivers catchments, southeast Queensland, 1988–
1997. Australian Geographical Studies, 39(2), 217–232.
Ribeiro, L. C. Q. (1995). Restructuring in large Brazilian cities: the center/periphery model. International
Journal of Urban and Regional Research, 19(3), 369–382.
Saurez-Villa, L. (1989). Policentric restructuring, metropolitan evolution, and the decentralization of
manufacturing. Tijdschrift voor Economische en Sociale Geografie, 80(4), 194–205.
Sobhi, H. (1987). The big urban bias. In A. Saqqaf (Ed.), The Middle East city: Ancient traditions confront
a modern world. New York: Paragon House Publishers.
Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and
change detection using Landsat TM data: when and how to correct atmospheric effects? Remote
Sensing of Environment, 75(2), 230–244.
616
Z.-Y. Yin et al. / Comput., Environ. and Urban Systems 29 (2005) 595–616
Stewart, D. J. (1996). Cities in the desert: the Egyptian new town program. Annals of the Association of
American Geographers, 86(3), 459–480.
Stewart, D. J. (1999). Changing Cairo: the political economy of urban form. International Journal of Urban
and Regional Research, 23(1), 128–146.
Stewart, D. J. (2001a). New Egyptian desert cities. In H. Lithwick & Y. Gradus (Eds.), Developing frontier
cities: global perspectives—regional contexts (pp. 301–312). Dordrecht, Netherlands: Kluwer Acadmic
Publishers.
Stewart, D. J. (2001b). Middle East urban studies: identity and meaning. Urban Geography, 22(2),
175–181.
Sultan, M., Abdel Hady, Y., El Araby, H., Mahani, A., Mehanee, S., Mecker, R., Fiske, M., Stein, T., &
Gamal, M. (1999). Monitoring the urbanization of the Nile Delta, Egypt. Ambio, 28, 628–631.
Wang, F., & Zhou, Y. (1999). Modelling urban population densities in Beijing 1982–90: suburbanisation
and its causes. Urban Studies, 36(2), 271–287.
World Bank (1997). World development indicators 1997, Cd-Rom, World Bank, Washington, DC.
Wu, F. (1997). Urban restructuring in ChinaÕs emerging market economy: towards a framework for
analysis. International Journal of Urban and Regional Research, 21(4), 640–663.