China is now the world’s largest source of bilateral development finance and will likely continue to play a prominent role in sovereign lending through its multi-billion-dollar Belt and Road Initiative. This paper introduces major methodological enhancements in tracking this finance: the use of an original application programming interface (API) to gathers news in multiple languages; double-verification of every record to ensure every finance commitment has been formalized; and visual geo-location to trace the precise footprint of every project. The resulting dataset enables economic, environmental, and social analyses with high-precision spatial accuracy, as well as spatiotemporal monitoring by project stakeholders and enhanced planning by project managers. It covers the years 2008–2019 to enable analysis before and after the announcement of the Belt and Road Initiative. It includes 862 finance commitments, 669 of which have geographic location, to 94 countries across the world.

With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data . The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the “supply chain” of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions.

The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.