I’m a trained environmental engineer, urban planner, environmental public health professional, and social scientist who has spent a 12-year academic and professional career working with data characterizing many aspects of water, the natural and built environments, and people. In doing so, I haved addressed a wide range of pressing policy questions at scales ranging from local land use decisions to international infrastructure finance.
I’m currently the Data Architect for the Internet of Water project of the Nicholas Institute for Environmental Policy Solutions at Duke University, where I develop standards, best practices, and templates for the storage, publishing, and cataloging of water-related data. In this role, I also educate agencies that produce water data in ways to make water data discoverable, accessible, and interoperable by adopting (meta)data standards, data exchange standards, API standards, as well as leveraging open-source software and public cloud services. I am also a statistician for Valor Water Analytics, where I apply econometric methods and quasi-experimental research designs to customer-level water meter data to help utilities understand how their customers react to prices, information, and weather.
The measurement and characterization of urbanization crucially depends upon defining what counts as urban. The government of India estimates that only 31% of the population is urban. We show that this is an artifact of the definition of urbanity and an underestimate of the level of urbanization in India. We use a random forest-based model to create a high-resolution (~ 100 m) population grid from district-level data available from the Indian Census for 2001 and 2011, a novel application of such methods to create temporally consistent population grids. We then apply a community-detection clustering algorithm to construct urban agglomerations for the entire country. Compared with the 2011 official statistics, we estimate 12% more of urban population, but find fewer mid-size cities. We also identify urban agglomerations that span jurisdictional boundaries across large portions of Kerala and the Gangetic Plain.
The fields of global health and international development commonly cluster countries by geography and income to target resources and describe progress. For any given sector of interest, a range of relevant indicators can serve as a more appropriate basis for classification. We create a new typology of country clusters specific to the water and sanitation (WatSan) sector based on similarities across multiple WatSan-related indicators. After a literature review and consultation with experts in the WatSan sector, nine indicators were selected. Indicator selection was based on relevance to and suggested influence on national water and sanitation service delivery, and to maximize data availability across as many countries as possible. A hierarchical clustering method and a gap statistic analysis were used to group countries into a natural number of relevant clusters. Two stages of clustering resulted in five clusters, representing 156 countries or 6.75 billion people. The five clusters were not well explained by income or geography, and were distinct from existing country clusters used in international development. Analysis of these five clusters revealed that they were more compact and well separated than United Nations and World Bank country clusters. This analysis and resulting country typology suggest that previous geography- or income-based country groupings can be improved upon for applications in the WatSan sector by utilizing globally available WatSan-related indicators. Potential applications include guiding and discussing research, informing policy, improving resource targeting, describing sector progress, and identifying critical knowledge gaps in the WatSan sector.
Objectives To estimate exposure to faecal contamination through drinking water as indicated by levels of Escherichia coli (E. coli) or thermotolerant coliform (TTC) in water sources. Methods We estimated coverage of different types of drinking water source based on household surveys and censuses using multilevel modelling. Coverage data were combined with water quality studies that assessed E. coli or TTC including those identified by a systematic review (n = 345). Predictive models for the presence and level of contamination of drinking water sources were developed using random effects logistic regression and selected covariates. We assessed sensitivity of estimated exposure to study quality, indicator bacteria and separately considered nationally randomised surveys. Results We estimate that 1.8 billion people globally use a source of drinking water which suffers from faecal contamination, of these 1.1 billion drink water that is of at least ‘moderate’ risk (>10 E. coli or TTC per 100 ml). Data from nationally randomised studies suggest that 10% of improved sources may be ‘high’ risk, containing at least 100 E. coli or TTC per 100 ml. Drinking water is found to be more often contaminated in rural areas (41%, CI: 31%–51%) than in urban areas (12%, CI: 8–18%), and contamination is most prevalent in Africa (53%, CI: 42%–63%) and South‐East Asia (35%, CI: 24%–45%). Estimates were not sensitive to the exclusion of low quality studies or restriction to studies reporting E. coli. Conclusions Microbial contamination is widespread and affects all water source types, including piped supplies. Global burden of disease estimates may have substantially understated the disease burden associated with inadequate water services.
Community sense of ownership for rural water infrastructure is widely cited as a key factor in ensuring sustainable service delivery, but no empirical investigation has evaluated the relationship between sense of ownership and sustainability outcomes. This study examines the association between system sustainability and sense of ownership among households and water committees, using primary data collected throughout 50 rural communities with piped water systems in Kenya. Data sources include in-person interviews with 1,916 households, 312 water committee members and 50 system operators, as well as technical assessments of water systems. Using principal components analysis we create composite measures of system sustainability (infrastructure condition, users’ confidence, and ongoing management), and of water committees’ and households’ sense of ownership for the system. All else held constant, infrastructure condition is positively associated with water committee members’ sense of ownership, whereas users’ confidence and system management are positively associated with households’ sense of ownership. These findings stand in contrast with much of the published literature on rural water planning, which assumes homogeneity of ownership feelings across all members of a community and which suggests a consistent and positive association between households’ sense of ownership and sustainability.
Monitoring of progress towards the Millennium Development Goal (MDG) drinking water target relies on classification of water sources as “improved” or “unimproved” as an indicator for water safety. We adjust the current Joint Monitoring Programme (JMP) estimate by accounting for microbial water quality and sanitary risk using the only-nationally representative water quality data currently available, that from the WHO and UNICEF “Rapid Assessment of Drinking Water Quality”. A principal components analysis (PCA) of national environmental and development indicators was used to create models that predicted, for most countries, the proportions of piped and of other-improved water supplies that are faecally contaminated; and of these sources, the proportions that lack basic sanitary protection against contamination. We estimate that 1.8 billion people (28% of the global population) used unsafe water in 2010. The 2010 JMP estimate is that 783 million people (11%) use unimproved sources. Our estimates revise the 1990 baseline from 23% to 37%, and the target from 12% to 18%, resulting in a shortfall of 10% of the global population towards the MDG target in 2010. In contrast, using the indicator “use of an improved source” suggests that the MDG target for drinking-water has already been achieved. We estimate that an additional 1.2 billion (18%) use water from sources or systems with significant sanitary risks. While our estimate is imprecise, the magnitude of the estimate and the health and development implications suggest that greater attention is needed to better understand and manage drinking water safety.