Population, Land Use, and Environment Research Directions (2005) / Chapter Skim
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11 Patterns of Urban Land Use as Assessed by Satellite Imagery: An Application to Cairo, Egypt--John R. Weeks, Dennis P. Larson, and Debbie L. Fugate
11 Patterns of Urban Land Use as Assessed by Satellite Imagery: An Application to Cairo, Egypt--John R. Weeks, Dennis P. Larson, and Debbie L. Fugate
Pages 265-286
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of people lived in rural places. But, at the beginning of the twenty-first century, we find that almost one in two humans is living in an urban place, with the tipping date estimated to be within the first decade of the century (United Nations Population Division, 2004)
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Most social science literature that describes the nature and character of urban populations focuses almost exclusively on the measurement of the social environment, often drawing on census data to describe this milieu. But variations in the social environment depend in part on variability in the built environment.
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The same built environment can host variation in the social environment, and the same social environment can exist in a range of built environments, but we hypothesize that a relatively narrow range of combined values of the built and social environments would describe a unique set of urban populations. We typically measure the social environment by the responses to questions on surveys and censuses.
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High spatial resolution imagery tends to be quite expensive, so most academic research focuses on moderate resolution data. In the research reported here, we are using Indian Remote Sensing (IRS)
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, so our ability to quantify composition is superior to our ability to quantify configuration. Composition refers to the proportional abundance in a region of particular land cover classes that are of interest to the researcher.
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. The V-I-S model views the urban scene as being composed of combinations of these three distinct land cover classes.
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, but the important point is that the soft classification offers a potentially more accurate representation of the surface area covered by each land cover class when the data are aggregated into areal units, such as census tracts or enumeration areas. Thus far the accuracy assessments have come largely from comparing the results of spectral mixture analysis of moderate spatial resolution imagery with the classification of data from higher spatial resolution imagery, such as the comparison with Ikonos imagery (Small, 2003)
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The proportional abundance of impervious surface is the baseline measure of urbanness, as suggested by the Ridd model, but shade is also a factor, especially in areas dominated by tall buildings creating shade that is then radiated to the sensor, essentially in the place of the underlying impervious surface. Thus, in areas that are generally urban, such as greater Cairo, the simple addition of the impervious surface and shade fractions should provide an appropriate measure of the proportional abundance of land cover most associated with an urban place.
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If no land cover class represented a majority, then the classification was based on an average of the highest proportional abundances among near neighbors. We have used the Fragstats software (McGarigal, 2002)
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. APPLYING THE URBAN INDEX We now apply the urban index to the study site of Cairo, Egypt.
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The pattern is for the older, more central parts of Cairo to the east of the Nile (in the Cairo governorate and in the more industrial governorate of Qalyubia to the north of downtown Cairo) to have higher proportional abundances of these land cover classes, whereas in the more suburban western portion (in the governorate of Giza)
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The data appear visually to support this expectation. Note as well that the measures of composition and configuration are not simple overlays of one another -- they exhibit somewhat different spatial patterns.
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The suburbs of Giza are generally less urban, with the notable exception of the Imbaba area (in the northeastern FIGURE 11-4 Contiguity index measuring the adjacency to one another of pixels classified as being impervious surface, 1996.
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which is a well-known high-density slum area. Figure 11-6 zooms in on several neighborhoods near central Cairo so that the numbers from the urban index for 1996 are displayed at the center of each neighborhood with a high resolution DigitalGlobe Quickbird image
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This allows for a qualitative visualization of what the quantification of that neighborhood (the urban index) stands for.
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and the buildings show some spacing between them. Table 11-1 compares the distribution of the urban index in 1996 (based on the Indian Remote Sensing image)
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We know that neither built nor social environments can have a direct effect on fertility. Rather, they can influence one or more of the proximate determinants of fertility, which include especially age at marriage, breastfeeding, abortion, and contraception (Bongaarts, 1982)
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Of these two factors, the social environment appears, not surprisingly, to be somewhat more important than the built environment in its influence on the proportion of women who are single. The standardized beta coefficient for social status in 1996 was .66, whereas the coefficient for the urban index was .43.
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Yet we know from Figures 11-7 and 11-8 FIGURE 11-9 Path model showing the indirect effects of the change in social and built environments on the change in neighborhood fertility levels: Cairo, 1986 to 1996.
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Neither one of those neighborhood characteristics told us as much individually as did both in combination with each other. DISCUSSION AND CONCLUSION The basic premise of this research is that information gleaned from satellite imagery is a proxy for the built environment of urban places, complementing data obtained from a census, which represent surrogate measures of the social environment.
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Nelson 1998a Change identification using multitemporal spectral mixture analysis: Applications in Eastern Amazonia. In Remote Sensing Change Detection: Environmental Moni toring Applications and Methods, C.M.
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Waller, and B.W. Nelson 1998c Change identification using multitemporal spectral mixture analysis: Applications in eastern Amazonia.
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