Arika Ligmann-Zielinska, Ph.D. - US grants

This Geography and Spatial Sciences (GSS) project will develop novel methods for understanding and categorizing uncertainty in complex spatial models of human and environmental systems interactions. Increasingly scientists are utilizing such models to understand the dynamics of various processes including urban expansion, climate change, deforestation, and nonpoint pollution of water bodies. An inherent complexity in these processes, leading to model uncertainty, is the variability over both space and time. For example, the dynamics of urban expansion in Detroit are different than those in Cleveland, and their dynamics today are different than those ten years ago. Models of such systems typically require a large number of explanatory variables that describe the economic, social, political, and environmental components of the overall system. To address model uncertainty, scientists use sensitivity analysis: a technique employed to understand how different values of an explanatory variable affect the outcome of interest. For example, how do increasing payments to farmers affect their willingness to convert agricultural lands for conservation purposes? This project will develop a new theoretical framework for applying sensitivity analysis to complex human-environment systems models exhibiting great variability over space and time. Specifically, the framework will facilitate 1) the identification of explanatory variables that account for the greatest variability in model outcomes leading to model simplification; 2) the exploration of the model outcome variability over space and time; and 3) the simulation of system dynamics to understand the implications of various policy scenarios. The developed framework will be tested on a model designed to understand agricultural land conservation decisions and their effects on lakes, local economies, and people.

This project will contribute greatly to scientists', policy-makers', and citizens' abilities to understand the extent to which various drivers in human-environment systems models affect model outcomes. Moreover, the project will facilitate the development of simpler systems models with greater explanatory power. As such, the methods developed will improve the transparency of such models potentially allowing greater community use, understanding, and participation in such modeling exercises. The framework will also provide methods for prioritizing data acquisition, improving the robustness of complex human-environment models, and enhancing the usefulness of such modeling endeavors for policy and decision making to address major societal problems. The project will train students, through graduate student mentoring and a modeling certification graduate program, to employ the new methods developed in order to describe and solve complex human-environment systems problems. The results of our research, including a spatiotemporal sensitivity analysis toolbox (ST-SA), will be disseminated electronically online, in peer reviewed manuscripts, and at professional conferences.

Despite recent gains in global agricultural productivity, sustained, equitable, and stable access to food continues to be a concern in many parts of the world. This interdisciplinary research project will examine why people in one region are faced with food insecurities in order to gain insights that will have wide-ranging utility. This project will enhance understanding of the critical societal problem of food security and will communicate potential solutions to decision makers by developing tools that synthesize qualitative and quantitative information from geography, sociology, decision science, and sustainability science as well as cross-disciplinary knowledge on food insecurity. Because food security is a complex problem that involves many stakeholders across different spatial scales and within different contexts, the investigators will use computer modeling to enhance understanding of the key elements of the food system, explain food insecurity, and provide tools for long-term policy making for improved food availability, access, and stability.

This research project will focus on a study of food insecurity in dryland West Africa, a region where changing environmental conditions and socioeconomic systems have left many people hungry despite increases in agricultural productivity. The investigators will develop and test a collection of small, independent computer models to describe and analyze different aspects of the food system. They will use mental modeling, multilevel structural equation modeling, agent-based modeling embedded in a geographic information system, and system dynamics modeling in an integrative, participatory, and iterative manner in order to examine mechanisms affecting food security. Mental modeling will help depict and communicate stakeholder perceptions, while multilevel structural equation modeling will be used to explain how individual attributes and structural factors affect food availability. Agent-based modeling embedded in a geographic information system will help describe spatial and temporal variability of food access, thereby explaining how individual behaviors link with livelihood vulnerability, while system dynamic models will evaluate how the dynamics of climate change, drought, hunger, and humanitarian relief over time impact on food security. Each model will build a progressively richer understanding of the problem, and an overarching scenario study tool will encapsulate the models and provide a transdisciplinary platform for synthesizing information from the models through scenario generation and evaluation. Model development, evaluation, and application will be informed by on-the-ground discussions with stakeholders in the study region. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.

The MSU Data Machine addresses the exponential growth of large and complex datasets in many fields of study, particularly those where computing has not been widely used or where the research and teaching approaches needed to work with “big data”, which present a different set of computing requirements than in traditional high performance computing. The Data Machine facilitates data-intensive research by having computing nodes with large amounts of memory, a high speed file system, graphics processing units that are optimized for machine learning and artificial intelligence-based analysis techniques, and a high speed file system. It also includes software, usage policies, and training that makes it easy for users to interactively analyze and visualize their data.

This project focuses on four specific research areas - in the areas of microbial genomics, social system modeling, spatial and community ecology, and data-driven turbulence modeling - however, the Data Machine broadly enables MSU’s research community to pursue data-intensive research projects by lowering barriers to engaging with these types of resources. The project also provides a valuable computational resource to the nation via the Open Science Grid and MSU’s NSF-funded Science DMZ project, advancing research in a wide spectrum of areas. Furthermore, MSU undergraduate and graduate students are participating in the deployment and administration of the Data Machine as well as using it for research and educational activities, contributing to the development of a globally competitive STEM workforce and promoting the advancement of under-represented groups in computational and data science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.