Theoretical perspectives of CHES research

We leverage knowledge and theory from a number of disciplines. These include complexity theory, landscape ecology, geographic information science, cyberinfrastructure theory and technology, big data science, and domain knowledge from sociology, demography, economics, and so on. At the same time, our group advances the knowledge, theory, and methodology in the above disciplines. With support from our digital 4-D methodology, all these theoretic perspectives render our work to be: 1) multi-disciplinary—with input from social, natural, and engineering sciences; 2) inter-scale—with ranges varying at spatial, temporal, and organizational scales; and 3) cross-type—with use of both quantitative and qualitative data or models. Below are a set of exemplar theories or frameworks we often build on:

Complex Systems Theory (CS Theory)

Complex systems theory (also known as complexity theory or complexity perspective) has partially originated from general systems theory (von Bertalanffy 1968; Warren et al. 1998). It focuses on understanding complex systems or complex adaptive systems. Complex systems often encompass a (often large) number of entities and subsystems, among which we often observe multiple interactions, nonlinear relationships, feedback, thresholds, time lags, and adaptation. As a result, these features in complex systems can lead to emergent phenomena or outcomes that are not analytically tractable from system components and their attributes alone.

From an empirical perspective, meta-analysis of multiple empirical human-nature studies has confirmed the aforementioned complexity features multiple sites around the world including the Amazon, the southern Yucatán in Mexico, the Wolong Nature Reserve of China, and Northern Ecuador (Liu et al. 2007). This perspective, and the associated modeling methods such as agent-based modeling, is not intended to be a replacement of traditional linear or other perspectives, instead their value lies in its capability as a systematic paradigm to help scientists harness the learning possibilities of existing complexity in the system of interest and take innovative action to steer it in beneficial directions (Axelrod & Cohen 1999).

Axelrod, R., and M.D. Cohen (1999). Harnessing complexity: organizational implications of a scientific frontier. The Free Press: New York.

Liu J, T. Dietz, S.R. Carpenter, M. Alberti, C. Folke, E. Moran, A.N. Pell, P. Deadman, T. Kratz, J. Lubchenco, E. Ostrom, Z. Ouyang, W. Provencher, C.L. Redman, S.H. Schneider, and W.W. Taylor (2007). Complexity of coupled human and natural systems. Science: 317:1513-16.

Von Bertalanffy, L. (1968). General System Theory: Foundations, Development, Applications. George Braziller, Inc.: New York.

Warren, K., C. Franklin, and C.L. Streeter (1998). New directions in systems theory: chaos and complexity. Social Work 43: 357–372.

Landscape Ecology Theory (LE Theory)

Landscape ecology is an increasingly recognized discipline, which addresses interactions between landscape pattern and ecological processes with particular attention to the causes and consequences of landscape heterogeneity over varying, usually large, scales. As a discipline cutting across traditional ecology, geography, and social sciences, landscape ecology offers unique insights into causes and measures of landscape patterns, ecosystem processes, disturbance, landscape connectivity, neutral models, restoration of degraded ecosystems, and human interaction with landscape processes and patterns. In particular, the Fragstats software and many landscape metrics offer big help towards better measuring and understanding landscape processes.

When dealing with CHES research questions, we are interested in multiple concepts, measures, and theoretical perspectives from landscape ecology. For instance, how do landscape patterns (e.g., connectivity) affect ecological processes, human decisions, and human-landscape interaction? How does the space-time principle help our understanding of the nature and scale of various disturbances (e.g., from landslide to hurricane) and landscape processes (e.g., from treefall to wildfire to species migration)? These are also topics of a landscape ecology course we offer. An NSF sponsored project illustrates how landscape ecology concepts and methods help undestand CHES patterns, processes, and mutual relatioships.

Levin, S.A. (1992). The problem of pattern and scale in ecology. Ecology 73:1943-1967.

Lewison, R., L. An, and X. Chen (2017). Reframing the payments for ecosystem services framework in a coupled human and natural systems context: Strengthening the integration between ecological and human dimensions. Ecosystem Health and Sustainability 3(5), 2017, 1335931.

Turner, M.G. (2005). Landscape ecology: What is the state of the science? Annual Review of Ecology, Evolution, and Systematics 36:319-344.

Geographic Information Science (GIScience)

Geographic(al) information science (GIScience) is the research or discipline that studies fundamental data structures and computational techniques to capture, represent, process, and analyze geographic information (Goodchild 1992). Though closely related to geographic information system(s) known as GIS, GIScience is more about the fundamental concepts, principles, theories, and data structures that underlie many GIS software tools. As a subarea of information science that is about geographic or spatial information, , GIScience consists of essential components such as cartography, geovisualization, geodesy, spatial statistics, GIS, remote sensing, and global positioning systems (GPS). Recent advances in cognitive and information sciences also contribute to GIScience.

The CHES research makes use of many GIScience concepts and methods (tools), and the most salient two of them are geographic information system(s) and remote sensing. However our connection to GIScience goes beyond use of GIS, GPS, and remote sensing, but also extends to include space-time analysis, 4-dimensional agent-based modeling, and extending traditional non-spatial methods to spatial data or space-time data analysis. In particular, we borrow and extend metrics from other disciplines to advance our understanding and representation of landscapes or landscape changes. Below is a list of articles that offer basic understanding of GIScience.

Duckham, M., M.F. Goodchild, and M. Worboys (2004). Foundations of Geographic Information Science. Taylor & Francis

Goodchild, M. (1992). Geographical information science. International Journal of Geographical Information Systems 6 (1): 31–45.

Coupled Human And Natural Systems (CHANS) Framework

Human-nature systems used to be studied largely either in separation or with unidirectional connections: when human systems were studied, they were considered to be constrained by, or with input from/output to, natural systems—put another way, natural systems were only considered as context or background. On the other hand, human systems were often viewed as exogenous influences when studying natural systems. This disciplinary chasm, in parallel with unidirectional connections between natural and human systems, has been shown unable to explain many complexity features (e.g., feedback, nonlinearity and thresholds, heterogeneity, time lags) in human-nature systems.

The coupled human and natural systems (CHANS) are integrated systems in which people interact with natural components. The CHANS concept has evolved in parallel with many closely related concepts, including coupled natural and human (CNH) systems, human-environment systems (Turner et al. 2003), social-ecological systems (SES; Ostrom 2007), and social-environmental systems (Eakin and Luers 2006). The CHANS framework addresses complex interactions and feedback between human and natural systems, which necessitates inclusion of biophysical/ecological variables and human variables, participation of biophysical/ecological and social scientists, use of tools and techniques from multiple biophysical and social sciences, etc.

An, L., A. Zvoleff, J. Liu, and W. Axinn (2014). Agent based modeling in coupled human and natural systems (CHANS): Lessons from a comparative analysis. Annals of Association of American Geographers 104(4):723-745.

An, L. (2012). Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecological Modelling 229(24): 25-36.

Liu J, T. Dietz, S.R. Carpenter, M. Alberti, C. Folke, E. Moran, A.N. Pell, P. Deadman, T. Kratz, J. Lubchenco, E. Ostrom, Z. Ouyang, W. Provencher, C.L. Redman, S.H. Schneider, and W.W. Taylor (2007). Complexity of coupled human and natural systems. Science: 317:1513-16.

Liu, J., T. Dietz, S.R. Carpenter, C. Folke, M. Alberti, C.L. Redman, S.H. Schneider, E. Ostrom, A.N. Pell, J. Lubchenco, W.W. Taylor, Z. Ouyang, P. Deadman, T. Kratz, and W. Provencher (2007). Coupled human and natural systems. AMBIO: A Journal of the Human Environment 36(8):639-649

Telecoupling Framework (Telecoupling)

Telecouplings indicate socioeconomic and environmental interactions between two or more areas over (often relatively large) distances (Liu et al. 2013, 2015), and such interactions may take the form of labor migration, tourism, consumption of goods manufactured and transported from afar, and so on. This framework is intellectually connected to the theory of weak ties and social network analysis (Granovetter 1973), which suggest weak ties may translate to strong results at the macro-level under certain conditions such as through connecting groups (Friedkin 1982; Yakubovich 2005).

This integrated framework aims to account for and internalize many socioeconomic and environmental externalities (spillover effects) across space and over time in an increasingly connected world. The framework consists of five major cross-related components: 1) multiple coupled human and natural systems (CHANS), 2) flows of material, information, and energy among systems, 3) agents that facilitate the flows, 4) causes that drive the flows, and 5) consequences that result from the flows. Many CHES systems exactly bear these components, and we are integrating it with other related theories, frameworks, and methods to address many pressing topics such as hazards mitigation & recovery, human decision making and response, landscape planning, and ecosystem restoration.

Friedkin, N. (1982). Information flow through strong and weak ties in intraorganizational social networks. Social Networks 3: 273–285.

Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology 78: 1360–1380.

Liu, J., V. Hull, M. Batistella, R. DeFries, T. Dietz, F. Fu, T. W.Hertel, R. C. Izaurralde, E. F. Lambin, S. Li., L. Martinelli, W. McConnell, E. Moran, R. Naylor, Z. Ouyang, K. Polenske, A. Reenberg, G. de Miranda Rocha, C. Simmons, P. Verburg, P. Vitousek, F. Zhang, and C. Zhu. (2013). Framing sustainability in a telecoupled world. Ecology and Society 18(2):26.

Liu, J., V. Hull, J. Luo, W. Yang, W. Liu, A. Vi?a, C. Vogt, Z. Xu, H. Yang, J. Zhang, L. An, X. Chen, S. Li, W. McConnell, Z. Ouyang, W. Xu, and H. Zhang (2015). Multiple telecouplings and their complex interrelationships. Ecology and Society 20(3):44.

Yakubovich, V. (2005). Weak ties, information, and influence: How workers find jobs in a local Russian labor market. American Sociological Review 70: 408–421.

Multiphasic Response Theory (MR Theory)

An important theoretical perspective in our CHES research is to leverage and advance the theory of the multiphasic response (David 1963). This theory hypothesizes that in the early stages of the demographic transition, households had lower child mortality while fertility was still high, leading to increasing population pressures. Therefore these households had to respond in various ways, including deferring marriage, reducing marital fertility, or out-migration. Taking one response would decrease the chances of taking other alternative responses. This was the situation of Japan and Europe in the 19th century. This theory was later expanded to include economic responses, such as expanding the land area, intensifying agricultural production, or finding off-farm work (Bilsborrow 1987). Therefore facing population pressures, people in developing countries will not only respond through demographic transition, but also through economic responses and technological adaptations.

In one of our CHES projects, we are innovatively testing the theory. At the micro-level (e.g., households), more than two multiphasic responses—e.g., out-migration, off-farm work, and intensification of agriculture—are being placed in multilevel simultaneous equations models. Our findings will shed light into what demographic, economic, and/or technological responses will be implemented under what conditions.

Bilsborrow, R. 1987. Population pressures and agricultural development in developing countries: A conceptual framework and recent evidence. World Development 15(2): 183-203.

Davis, K. 1963. The theory of change and response in modern demographic history. Population Index 29(4): 345-366.

Spatial Demography

Demography is the discipline of population studies—in a broader sense, populations of any species could be targets to demographic studies; though human populations are often the "default" subjects. Demographers study the size, structure, distribution, changes, and many other characteristics of populations (e.g., birth, migration, ageing, and death), along with the economic, social, cultural, and biological contexts or processes that exert influences on population processes.

As time goes on, demography has been developing an increasingly explicit awareness of spatial variation and its importance towards demographic studies. In addition to some universal principles, spatial variation may also play an important role in explaining demographic characteristics and/or transitions. Spatial analysis is not only essential for demographic theory development, but also for empirical studies. This is the essence of spatial demography, which focuses on the spatial analysis of demographic processes. In our CHES research, we focus on envisioning or modeling low level (e.g., individual level), spatially-variant population processes (such as birth, marriage, and death) or features (e.g., health outcomes) and how contextual factors may affect them in a spatially explicit manner. Below we list two books that help generic understanding of spatial demography and several of our papers pertaining to spatial demography.

An, L., W. Yang, and J. Liu (2016). Demographic decisions and cascading consequences. Book chapter in Liu et al.: Pandas and People: Coupling Human and Natural Systems for Sustainability. Oxford, UK: Oxford University Press.

An, L., M. Linderman, Guangming He, Z. Ouyang, and J. Liu (2011). Long-term ecological effects of demographic and socioeconomic factors in Wolong Nature Reserve (China). In R.P. Cincotta & L.J. Gorenflo (Eds.), Human Population: Its Influences on Biological Diversity. Berlin, Germany: Springer-Verlag.

An, L., G. He, Z. Liang, and J. Liu (2006). Impacts of demographic and socioeconomic factors on spatio-temporal dynamics of panda habitats. Biodiversity and Conservation 15:2343-2363.

Crook, S.E.S., L. An, D.A. Stow, and J.R. Weeks (2016). Latent trajectory modeling of spatiotemporal relationships between land cover and land use, socioeconomics, and obesity in Ghana. Spatial Demography 4(3):221-244.

Howell, F.M., J.R. Porter, and S.A. Matthews (2016). Recapturing Space: New Middle-Range Theory in Spatial Demography (Spatial Demography Book Series Volume 1). Springer. ISBN: 978-3-319-22809-9 (Print); 978-3-319-22810-5 (Online).

Weeks, J.R., D. Stow, and L. An (2018). Demographics, health drivers & impacts on land cover and land use change in Ghana. Chapter for Stephen J. Walsh (ed.), Remote Sensing Applications for Societal Benefits (Comprehensive Remote Sensing Vol. 9), Elsevier.

Weeks, J.R. (2015). Population: Introduction to Concepts and Issues (Twelfth Edition). Boston, MA: Cengage Learning.

Zvoleff, A., and L. An (2014). The effect of reciprocal connections between demographic decision making and land use on decadal dynamics of population and land use change. Ecology and Society 19(2):31.

Zvoleff, A., L. An, J. Stoler, and J.R. Weeks (2013). What if neighbors' neighborhoods differ? The influence of neighborhood definition on health outcomes in Accra. In J.R. Weeks & A.G. Hill (Eds.), Spatial Inequalities: Health, Poverty and Place in Accra, Ghana. Springer.

Our theoretical support is not limited to these perspectives. As an evolving process, our research will leverage a broader knowledgebase, including potential contributions from computer science and engineering, spatial semantics, computational linguistics, human (cultural) geography, political science, and the like. We look forward to new perspectives or theories from more researchers, practitioners, and/or projects.