POVERTY ATLAS (GHANA): In 2008, for the first time in history, more than half of the world's population lived in urban areas. Of that urban population, almost a third, or 924 million people, live in slums--a 2,500-fold increase from 35 million slum dwellers in 1957. Image: COURTESY OF CHF INTERNATIONAL
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Economic opportunity has always been a big part of the allure of urban life, yet most cities are at least pockmarked by areas of extreme poverty. Often the scope of the problem eludes government agencies as well as the impoverished communities themselves. Poverty atlases that map the extent of privation have existed for decades as a means to alert urban leaders to areas lacking basic services, such as water, electricity and sanitation. More recently, however, groups are looking to deliver this information beyond bureaucrats, going straight to the residents in an effort to empower them to take charge of devising and implementing long-term solutions to their problems.
Since 2007 nonprofit CHF International has been mapping urban poverty in areas of India and Africa through a program called Slum Communities Achieving Livable Environments with Urban Partners (SCALE-UP) (pdf). "There isn't a lot of granular information on poverty and slums in the cities we're working in," says Brian English, country director of slum upgrading, urbanization and climate change initiatives in India for CHF, which was founded in 1952 as the Foundation for Cooperative Housing to provide affordable homes for low-income families in rural and urban America.
CHF's goal in mapping slums is to provide a more complete picture of why poverty exists in certain areas and how conditions can be improved. For now, SCALE-UP, with the help of about $9 million in funding from the Bill and Melinda Gates Foundation, focuses on three cities in India—Bangalore, Nagpur and Pune—as well as three in Ghana—its capital Accra and its twin port cities Sekondi and Takoradi. Some mapping work has also begun in Haiti.
There are several reasons why information about slum neighborhoods may be lacking. Often, when a local government designates an area as a slum it has certain obligations to that locale, such as providing access to health care, water and sanitation services. "So until the government is ready to accept their obligations to provide services to these populations they won't collect any details on them or include them in official statistics," English says. "Another way of saying this is policy makers often turn a blind eye to impermanent settlements."
In addition, high-poverty neighborhoods are spreading faster than some cities can track them, particularly if local governments are relying on outdated census information to allocate resources. And government-conducted surveys on poverty in developing countries are often considered crude estimates because the poor's mistrust of authorities skews honest answers, English adds.
In general, the first stage in creating a poverty atlas is to identify the communities that meet the criteria for being considered a slum and to plug their locations into geographic information system (GIS) software. (Esri's ArcGIS has been used in Ghana, for example.) This enables CHF and its partners to analyze data and query conditions in a particular community. CHF is likewise using satellite imagery and survey-grade handheld GPS devices to ensure the accuracy of its maps. "We've also experimented with open-source platforms like Walking Papers that allow anyone to add detail to maps and upload them to wiki-style, Web-based OpenStreetMap," English says.
CHF's Pune atlas, created with help from local NGO Maharashtra Social Housing and Action League (MASHAL), is one of the organization's most complete projects, with 477 slum neighborhoods identified. CHF has also been able to drill down and perform socioeconomic surveys within 360 of the slums, according to English. Within those, 85,000 households have responded to a questionnaire, representing roughly 430,000 individuals. To get this more detailed information, CHF broke each slum down into clusters of 25 households and asked a volunteer from each one to gather information concerning the people living in their household. For example, households were asked if they had their own toilet, used a public toilet (and whether it was free) or defecated in the open.