The lack of reliable subnational data for sectors and geographic areas undermines the design, implementation and monitoring of effective interventions by municipal governments, relevant national ministries and donor organisations. Mitlin and Satterthwaite (2012) highlight the difficulty of accurately assessing the scale of urban poverty, given the paucity of comparable data and difficulties in capturing complexity or urban vulnerability. The inadequacy of statistics on poverty at city level requires attention. Muggah (2012) critiques development agencies for failing to invest in municipal statistical datasets. New sources of data gathered from mobile phones, satellite imagery and the internet may have a role to play in facilitating a data revolution (Stuart et al., 2015).
In an attempt to fill some of these knowledge gaps, the Igarapé Institute, in association with the United Nations University, the World Economic Forum, the World Bank and SecDev, have sought to identify the elements of city fragility. Their focus has been on isolating the drivers, or risks, that make some cities more fragile than others. They have analysed 100+ databases and consulted specialists to map the geography of urban fragility. The fragile cities data visualisation platform tracks risk in over 2,137 cities with populations of 250,000 or more. It includes a fragility scale based on ten indicators statistically associated with instability. The intention is to provide mayors, planners, business people and CSOs with access to data annually on how urban fragility is distributed in upper-, middle- and lower-income settings.
To leverage the potential of technology to fill data gaps, the UN has established a number of Pulse Labs, bringing together government experts, UN agencies, academics and the private sector to pioneer new ways of using Big Data to support development goals. Pulse labs tap into local knowledge and innovation, establish key partnerships, test and pilot real-time monitoring approaches and support the adoption of proven approaches. Examples include collaboration between the Smart City team in the Jakarta Government and Pulse Lab to explore real-time bus location data, service demand data, and real-time traffic information. The study initially focused on mapping locations with abnormal traffic and understanding how customer demand responds to traffic dynamics. The insights from this first phase will be used to improve TransJakarta bus services. The project aims to enhance transport planning and operational decision-making within the Jakarta Government through real-time data analytics.
Similarly, Pulse Lab Kampala is working on a project to use satellite imagery and develop image processing software to count roofs and identify the roofing material used. An online dashboard will analyse roof materials as an indicator of poverty. This tool will complement existing statistical approaches that use surveys and primary data collection to assess poverty levels. The new data generated with the automated roof top counting can provide insights into household economies.
- Mitlin, D. & Satterthwaite, D. (2012). Urban poverty in the global south: Scale and nature. Abingdon: Routledge.
- Muggah, R. (2012). Researching the urban dilemma: Urbanization, poverty and violence. Ottawa: IDRC.
- Stuart, E. Samman, E. Avis, W. and Berliner, T. (2105). The Data Revolution: Finding the Missing Millions. London: ODI.