Notes
N o t e s Chapter 1. World Urbanization 1. The author had this exchange with one of the member of the Stiglitz Commission at the Global Agenda Council Meetings, Dubai, UAE, November 2009. Chapter 2. Human Population Grows Up This article is based on two prior versions (Cohen 2005, 2009). This revision has been updated with estimates and projections available as of June 2010. Chapter 3. Measuring and Coping with Urban Growth in Developing Countries 1. UN-HABITAT analysts developed the 350-city Global City Sample from a universe of 4,000 cities with populations over 100,000 in order to track urbanfocused Millennium Development Goals. 2. The source for urban growth rates is UN Department of Economic and Social Affairs, World Urbanization Prospects: The 2003 Revision. The development indicators come from the UN Demographic and Health Survey, supplemented by UN-generated Population Census Tables where DHS was missing data. Currently , UN-HABITAT has produced one or more development indicators for 119 cities, a sample that will grow to 350 cities in the future. 3. There is a long tradition in development studies to use U5MR as a proxy for poverty in the absence of income data. Another indicator, gender disparities in literacy, is not commonly used to indicate poverty, but it is also a key factor because literacy is one of the most essential survival skills in cities (UNFPA 2007). If a significant portion of women are illiterate, neither they nor their children can benefit from the opportunities offered by cities. 4. The UN-HABITAT analysts first clustered the fifty-two cities into three groups (high, medium, and low development) according to the indicators. High-development cities were those with high levels of infrastructure, low U5MR, and low or no gender literacy gap. Medium-development cities were those that had lower levels of infrastructure coverage, higher U5MR, and greater gender literacy gaps. Low development had even worse indicators. They next divided the sample by population growth rate, with ‘‘low’’ being at or below 2.5 percent per year and ‘‘high’’ above 2.5 percent. 5. Granting that intracity disparities in development are the weakest point of these cities, the authors did not factor this into the analysis because it cuts across 324 Notes to Pages 67–128 all levels of population growth, high or low. Among the low-growth cities that suffer such inequalities are Ho Chi Minh City, Cairo, Istanbul, and Rio de Janeiro (ACHR 2001; Sutton and Fahmi 2001; Güvenç 1996; Pamuk and Cavallieri 1998). Chapter 5. Urban Growth and Spatial Development 1. Cities and towns are both urban settlements in the urban system of China. A city (shi) is an administrative unit affiliated to and under the leadership of a province (sheng), autonomous region (zizhi qu) or autonomous state (zizhi zhou). A town (zhen) is an administrative unit affiliated to and under the leadership of a county (xian) or autonomous county (zizhi xian) which is normally rural. 2. The informal sector (fei zhenggui jingji) refers to individual economic operation and activities, household-based small firms, and other small firms engaging in activities with little legal restriction on their operational scope and with little capital. Chapter 7. Measuring and Modeling Global Urban Expansion 1. The financial support of the World Bank and the National Science Foundation (SES-0433278) helped to make this research possible. Special thanks are due to Alison Kraley, whose efforts and assistance have been central to this work. Thanks are also due to Solly Angel and Daniel Civco, who collaborated on and served as co-investigators in earlier stages of this research. Chapter 8. Urban Growth Models 1. These are not the only available examples of operational urban planning models. Other well-known models in use in the U.S. include DRAM/Empal, MEPLAN, What-If, U-Plan, LEAM, Places3, and CommunityViz (U.S. EPA 2000). Urban models in use outside the U.S.—where they are also known as spatial development models—include MEPLAN, TRANUS, and LILT. 2. Different impact models require different levels of spatial resolution. Travel-demand models have traditionally relied on zonal data, whereas air and water pollution models make use of point- or line-level data. Land-cover, habitat, and environmental impact assessment models typically draw on spatially explicitly site-level data, while fiscal impact models typically require community-level data. As locally inaccurate or untested as UGMs can be, it is worth noting that many impact assessment models are even more untested and inaccurate. Coupling an...
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