Geo-Social Complexity

While previous studies are primarily based on the US, a recently published article shifts focus to Ghana in Africa. We ( Emmanuel Frimpong Boamah , Thomas Yeboah , Kahad Adamu , Enoch Yeleliere , Thomas Padi Appai, Kwaku O. Twum and myself) examined Ghana’s decentralized irrigation reform and their impacts on water users’ capacities to self-govern and cooperate in managing their irrigation systems. The study area is the Kpong Irrigation Scheme (KIS) that started operation in 1998, irrigating about 2125 hectares of paddy rice farms and 1140 hectares of banana farms.  We used both primary and secondary data including content analysis of key documents, stakeholder interviews, and surveys of farmers to explore the institutional designs informing the governance of KIS. The KIS case suggests an improvement in authority and responsibility principles in the institutional design of Ghana’s decentralized irrigation reforms. It also indicates that, on paper (rules-in-form), the decentrali...

The geo-simulation research domains including agent-based modeling requires high-quality synthetic population dataset that can capture individuals' demographic characteristics, spatial distribution and social connections.  In this paper published in Nature - Scientific Data ,  ( Na (Richard) Jiang , Boyu Wang ,  Andrew Crooks , and myself, we introduced a Python-based workflow that uses US Census 2020 dataset to generate  large-scale geographically explicit synthetic population for America’s 50 states and Washington  D.C. . The generated synthetic population is at individual level and their aggregated demographic attributes including age, gender distributions can match the US Census Data at the census tract level. In addition to demographic attributes such as age, gender, household and urban/rural status , our synthetic population data is also  geographically explicit by assigning home locations to individuals using road networks as a proxy. We also i...