Understanding Social Networks within Complex, Nonlinear Systems: Geographically Integrated History and Dynamics GIS
CDI – Type II Collaborative Research: Understanding social networks within complex, nonlinear systems: geographically-integrated history and dynamics GIS [acronym: SOCNET]
This project, CDI – Type II Collaborative Research: “Understanding social networks within complex, nonlinear systems: geographically-integrated history and dynamics GIS” (acronym: SOCNET), will form a virtual organization of historians, geographers, computer scientists, and mathematicians to share historical social science data and develop geographically integrated frameworks to address complex, dynamic, nonlinear systems and social networks.
Computational thinking has enabled many new scientific discoveries through the development of new algorithms, simulation models,visualization, and novel approaches to summarize the patterns and structures of complex systems. In contrast to the natural sciences, the historical social sciences (anthropology/archaeology, economics, geography, history, and sociology) pose additional challenges because data are often qualitative, vague, inconclusive, and highly uncertain. Existing computational methods reach their limits quickly with data for the historical social sciences. SOCNET will develop new computational methods to overcome these limits by addressing the importance of “place” to integrate data as the foundation of knowledge creation about connections among humans, events, and environments.
SOCNET will advance computational thinking through creation and innovation of new data frameworks and analytical approaches to the new paradigm of geographically-integrated history. Dynamics GIS (geographic information systems) and related information technologies will provide the backbone for understanding complex historical social systems.
SOCNET’s premise posits that (1) the history of any place is shaped in significant ways by the way the place is connected to other places and by the changes in these connections over time; (2) historical periods are complex, dynamic, nonlinear systems that are spatially large, and in more recent centuries, global in extension, and that sometimes become unstable, leading to a phase transition, bifurcation, and the organization of new systems; and (3) within such systems, people and places are connected by selforganizing networks that are the sources of innovation and the emergence of new forms.
Intellectual Merit: SOCNET will realize this transformative research through the cyber-enabled collaboration of computer scientists, geographic information scientists, geographers, and historians. SOCNET will advance research on (1) how to connect concepts (e.g. social networks, business cycles, climate change); (2) how to make use of data that are vague,uncertain, and incomplete and of qualitative data within a computational context;(3)new means for the representation of data for organizing, storing, manipulating, and recovering them for exploration using computational tools; (4) new tools for data harmonization and text mining; (5) new forms of modeling to represent the inferences of domain experts;and (6) new metaphors beyond the map for temporal GIS and new forms of visualization. SOCNET will concentrate on the first global age (1400-1800) because this complex, dynamic, nonlinear system has disappeared and its outcomes can be used in an iterative process to improve our understanding of the system itself and of any models and simulations that are created.
SOCNET members’ collective current research addresses these issues, but to transform the research paradigm within the historical social sciences, the collaborative protocols, tools, models, and algorithms must be shaped and presented in forms that will provide interested researchers and their students with access without having to climb over prohibitively high learning thresholds. Thus, SOCNET will advance all of these interrelated research fronts and produce web-based educational materials for classroom use and self-training.
Broader Impacts: The project promotes international, multidisciplinary collaboration in geographically integrated history for the transformation of the historical social sciences and geographic information science, with potential impacts on computer science, mathematical modeling and simulation, environmental sciences, and transportation studies. As useful tools for the evaluation of long-term effects of policies at a variety of scales, SOCNET’s products will potentially serve the needs of administrators and policy-makers. Student participants will become a cohort of transformative researchers, and because of the higher percentage of women and minorities among majors in the historical social sciences, the project will attract such students into a technologically rich educational and employment environment.
More information is available at the project website (https://narratives.csa.ou.edu)