Title: Quantifying Cities for Sustainable Development: Transforming urban data collection to support research and policy making in developing countries
Funder: Economic and Social Research Council

Abstract: Our project addresses a critical gap in social research methodology that has important implications for combating urban poverty and promoting sustainable development in low and middle-income countries. Simply put, we're creating a low-cost tool for gathering critical information about urban population dynamics in cities experiencing rapid spatial-demographic and socioeconomic change. Such information is vital to the success of urban planning and development initiatives, as well as disaster relief efforts. By improving the information base of the actors involved in such activities we aim to improve the lives of urban dwellers across the developing world, particularly the poorest and most vulnerable. The key output for the project will be a freely available 'City Sampling Toolkit' that provides detailed instructions and opensource software tools for replicating the approach at various spatial scales. Our research is motivated by the growing recognition that cities are critical arenas for action in global efforts to tackle poverty and transition towards more environmentally sustainable economic growth. Between now and 2050 the global urban population is projected to grow by over 2 billion, with the overwhelming majority of this growth taking place in low and middle-income countries in Africa and Asia. Developing evidence-based policies for managing this growth is an urgent task. As UN Secretary General Ban Ki Moon has observed: "Cities are increasingly the home of humanity. They are central to climate action, global prosperity, peace and human rights...To transform our world, we must transform its cities." Unfortunately, even basic data about urban populations are lacking in many of the fastest growing cities of the world. Existing methods for gathering vital information, including censuses and sample surveys, have critical limitations in urban areas experiencing rapid change. And 'big data' approaches are not an adequate substitute for representative population data when it comes to urban planning and policymaking. We will overcome these limitations through a combination of conceptual innovation and creative integration of novel tools and techniques that have been developed for sampling, surveying and estimating the characteristics of populations that are difficult to enumerate. This, in turn, will help us capture the large (and sometimes uniquely vulnerable) 'hidden populations' in cities missed by traditional approaches. By using freely available satellite imagery, we can get an idea of the current shape of a rapidly changing city and create a 'sampling frame' from which we then identify respondents for our survey. Importantly, and in contrast with previous approaches, we aren't simply going to count official city residents. We are interested in understanding the characteristics of the actually present population, including recent migrants, temporary residents, and those living in informal or illegal settlements, who are often not considered formal residents in official enumeration exercises. In other words, our 'inclusion criterion' for the survey exercise is presence not residence. By adopting this approach, we hope to capture a more accurate picture of city populations. We will also limit the length of our survey questionnaire to maximise responses and then use novel statistical techniques to reconstruct a rich statistical portrait that reflects a wide range of demographic and socioeconomic information. We will pilot our methodology in a city in Pakistan, which recently completed a national census exercise that has generated some controversy with regard to the accuracy of urban population counts. To our knowledge this would be the first project ever to pilot and validate a new sampling and survey methodology at the city scale in a developing country.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE