156 The Future of Water in African Cities
Data for both challenges and capacities were normalized from zero to maximum so as to facilitate aggregation. When needed, data was inverted prior to normalization, so that results were consistent with the overall message of the category (for example, percentage of the population with improved sanitation was converted to percentage of the population with no improved sanitation, then normalized, because a higher indicator value means a higher challenge for the city). When this was not possible (due to the unit of the indicator), data was normalized from zero to maximum first, then substracted from a total of 100 percent (for example, for water consumption, a high level of water consumption should be represented by a low value in our index as it represents a lower challenge). Indicators were then aggregated as follows: each water-related indicator was assigned even weighting within each index dimension (challenges and capacity). The practice of even weighting for indices can be subject to debate but is corroborated by expert opinion (Chowdhury and Squire, 2006). The limitations faced during the data collection process for the 31 cities general data set, and outlined in detail in the methodology for 31 cities database, also apply to the capacity/challenge matrix. Gaps in the data collected meant that for some cities, fewer indicators were available than for others, which affected the city’s score. This index is a preliminary attempt to illustrate relative challenges and capacities between cities and thus to inform decision makers of the greatest needs. It is hoped that the index will generate a dialogue to improve evaluation, and incorporate data inputs from cities and other stakeholders with the view to future improvements.
Notes 1. According to data from UNDESA, 2012. 2. UNDESA, 2012. 3. See database on http://water.worldbank.org/AfricaIUWM (forthcoming). 4. The Economist Intelligence Unit and Siemens, 2011.