Global Urban Footprint datasets
The analysis of urban built-up area was based on the European Commission’s Global Human Settlement Layers (GHSL) dataset, which uses optical sensors technology. This dataset was chosen because data are available for multiple years (1975, 1990, 2000, and 2014), which enables comparing built-up areas over time.
However, some anomalies appeared in the identification of urban built-up areas when
specific surfaces were analyzed. To identify those anomalies, the data were compared with another dataset, the German Aerospace Center’s Global Urban Footprint (GUF), which is only available for 2011. The GUF dataset uses radar technology, which enables more precise and accurate detection of urban built-up areas.
Comparing both datasets reveals that some cities suffer from much larger anoma-lies than others (map 1D.1). For example, although the GHSL and GUF layers align in Casablanca, there are wide disparities in Cairo. The following recurrent anomalies were detected:
• The GHSL layer tends to overestimate built-up areas, identifying wider areas than the GUF dataset and sometimes spe-cific open areas within the city (such as the airport in Cairo).
• In many cases, rivers crossing through cit-ies are incorrectly identified as built-up areas in the GHSL dataset (such as the Nile River in Cairo).
When possible, those anomalies were cor-rected manually. For example, built-up area associated with the Nile River in Cairo was erased before running the analysis. However, it is important to highlight that any anoma-lies that could not be corrected (such as the overestimation of random built-up areas) might result in moderate margins of error in the analysis.
TABLE 1C .1 Selected neighborhoods for analysis of road and intersection densities in the Middle East and North Africa
City Historic center Informal neighborhood Modernist neighborhood
Cairo, Egypt, Arab Rep. Bab el Wazir Fostat New Cairo
Abu Dhabi, United Arab Emirates n.a. n.a. Mohammed Bin Zayed
Dubai, United Arab Emirates n.a. n.a. Al Warqa 2
Beirut, Lebanon Bourj Abi Haydar Borj-el Barajneh Jnah
Rabat, Morocco Medina Kariat Secteur 10
Tripoli, Libya Old City Al Hadba Al Khadra Asahabah
Tunis, Tunisia Ez-Zitouna Cité Khalid Ibn al-Walid El Mourouj 5
Amman, Jordan Ras al-Ain Awajan Al Zohour
Source: Compilation based on Google Earth.
Note: n.a. = not applicable.
Notes
1. In this report, the Maghreb refers to Algeria, Libya, Morocco, and Tunisia; the Mashreq to the Arab Republic of Egypt, Iraq, Jordan, Lebanon, the Syrian Arab Republic, and West Bank and Gaza; and the Gulf Cooperation Council (GCC) to Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. Middle East and North Africa regional aggregates typically include all of these countries as well as Djibouti and the Republic of Yemen, with some notable excep-tions.
2. Population data from the World Development Indicators database.
3. Urbanization here refers to the percentage of total population living in urban areas.
4. Refugee estimates from the United Nations High Commissioner for Refugees (UNHCR) 2018 data portal: http://reporting.unhcr.org /node/36.
5. Urbanization rates are from the World Development Indicators database: https://
data.worldbank.org/.
6. Urban primacy indicates the ratio of the pri-mate city to the next largest (that is, the
second largest) in a country or region. In other words, urban primacy can be defined as the central place in an urban or city network that has acquired or obtained a great level of dominance.
7. The urban form indicators used in this chap-ter are discussed in annex 1B.
8. By replicating the methodology established by Henderson and Nigmatulina (2016), we ana-lyze the difference in fragmentation controlling first for city size (population) and then for both city size and national GDP per capita (which is used as a proxy for commuting costs).
9. For the findings of the CAPSUS (2018) study, see the “Urban Growth Scenarios: Hashemite Kingdom of Jordan” website: http://jordan .capitalsustentable.com.mx/.
10. Densification figures were calculated using the European Commission Joint Research Centre’s Global Human Settlement Layers datasets.
11. The LEI (Landscape Expansion Index)—a spatial metric developed by Liu et al. (2010)—
enabled us to capture the information of the formation processes of a landscape pattern.
Classification: 0 = Leapfrog, 0–50 = Extension, 50–100 = Infill.
Sources: European Commission Joint Research Centre’s Global Human Settlement Layers 2015 and German Aerospace Center’s Global Urban Footprint 2016 datasets.
Note: GHSL = Global Human Settlement Layers. GUF = Global Urban Footprint.
MAP 1D.1 Comparison of Global Human Settlement Layers and Global Urban Footprint datasets for Cairo and Casablanca, 2016
12. “Segregation” in this chapter is interpreted as the opposite of inclusion (loosely based on Shah et al. [2015]): the marginalization of groups and individuals on the basis of socio-economic status, gender, age, caste, ethnicity, and other categories and where space acts as catalyst for such exclusion.
13. Compactness is also associated with negative effects, such as an increase in criminality (Burton 2001), which can produce contradic-tory effects. For example, in Mexico City, growing criminality and violence have deeply influenced social interactions. Privileged classes have abandoned public spaces, defected from public schools and health services, and are using the car for transport, which has resulted in a drastic reduction in interaction opportuni-ties with strangers. All this has rendered multi-class interactions virtually nonexistent (Bayón, Saraví, and Breña 2013).
14. But only partially, as Chetty et al. (2018) show that, for black males in the United States, differences in upward mobility cannot be entirely explained by socioeconomic or spatial dimensions.
15. For the UN World Urbanization Prospects database, see https://population.un.org/wup/.
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