Myron P. Gutmann

Computational Social Science

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Life in the network: the coming age of computational social science

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Social science. Computational social science.

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Confidentiality and spatially explicit data: Concerns and challenges

Recent theoretical, methodological, and technological advances in the spatial sciences create an opportunity for social scientists to address questions about the reciprocal relationship between context (spatial organization, environment, etc.) and individual behavior. This emerging research community has yet to adequately address the new threats to the confidentiality of respondent data in spatially explicit social survey or census data files, however. This paper presents four sometimes conflicting principles for the conduct of ethical and high-quality science using such data: protection of confidentiality, the social–spatial linkage, data sharing, and data preservation. The conflict among these four principles is particularly evident in the display of spatially explicit data through maps combined with the sharing of tabular data files. This paper reviews these two research activities and shows how current practices favor one of the principles over the others and do not satisfactorily resolve the conflict among them. Maps are indispensable for the display of results but also reveal information on the location of respondents and sampling clusters that can then be used in combination with shared data files to identify respondents. The current practice of sharing modified or incomplete data sets or using data enclaves is not ideal for either the advancement of science or the protection of confidentiality. Further basic research and open debate are needed to advance both understanding of and solutions to this dilemma.

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

Building Partnerships Among Social Science Researchers, Institution-based Repositories and Domain Specific Data Archives

Purpose – In developing and debating digital repositories, the digital library world has devoted more attention to their missions and roles in supporting access to and stewardship of academic research output than to discussing discipline, or domain, specific digital repositories. This is especially interesting, given that in social science these domain‐specific repositories have been in existence for many decades. The goal of this paper is to juxtapose these two kinds of repositories and to suggest ways that they can help build partnerships between themselves and with the research community.Design/methodology/approach – The approach taken in the paper is based on the fundamental idea that all the parties involved share important goals, and that by working together these goals can be advanced successfully.Findings – The key message is that by visualizing the role of repositories explicitly in the life cycle of the social science research enterprise, the ways that the partnerships work will be clear. These ...

ECOLOGICAL IMPACT OF HISTORICAL LAND-USE PATTERNS IN THE GREAT PLAINS: A METHODOLOGICAL ASSESSMENT

This paper demonstrates a method for using historical county-level agricultural land-use data to drive an ecosystem model. Four case study counties from the U.S. Great Plains during the 19th and 20th centuries are used to represent different agroecosystems. The paper also examines the sensitivity of the estimates of county-level ecosystem properties when using different levels of detail in the land-use histories. Using weighted averages of multiple-model runs for grassland, dryland cropping, and irrigated cropping improved prediction over a simple, single-run approach that models the prevailing land use. Model runs with the same land use and environment generally reach similar levels of soil carbon and nitrogen mineralization after ∼50 years, no matter when they began, with faster convergence for irrigated cropland. Model results show that cultivation of grasslands results in large losses of soil carbon and an increase in soil nitrogen mineralization for the first 20–30 years of cultivation, which is followed by low soil carbon loss and nitrogen mineralization 50 years after cultivation started. The recently observed increase in irrigated agriculture in the central and northern Great Plains (2.7 million ha) has resulted in a net carbon storage of 21.3 Tg carbon, while irrigated cotton production has resulted in a net loss of 12.1 Tg carbon.

Long-term Trends in Population, Farm Income, and Crop Production in the Great Plains

ABSTRACT Despite concern about the social, economic, and ecological viability of the agricultural Great Plains, a century-long examination reveals that threats to society, economy, and environment are counterbalanced by surprising stability and the potential for short- and medium-term sustainability. Populations in metropolitan counties have grown, whereas rural populations may now be stable; both metropolitan and rural populations are aging. Technological advances in the past five decades enhanced production in the Great Plains despite periodic adverse economic and environmental conditions, and increases in crop yields, animal feeding, and government payments have sustained agriculture and income. Nonmetropolitan counties with irrigated farming have been more successful than those without irrigation. However, overuse of groundwater and rising energy costs for irrigation affect economic margins and the ability to sustain environmental integrity. Long-term projections of agricultural productivity must balance recent stability with the risks posed by reduced irrigation, higher energy prices, disruptive demographic changes, and further loss of environmental integrity.

Hispanics in the United States, 1850–1990: Estimates of Population Size and National Origin

(2000). Hispanics in the United States, 1850–1990: Estimates of Population Size and National Origin. Historical Methods: A Journal of Quantitative and Interdisciplinary History: Vol. 33, No. 3, pp. 137-153.

Impact of historical land-use changes on greenhouse gas exchange in the U.S. Great Plains, 1883-2003.

European settlement of North America has involved monumental environmental change. From the late 19th century to the present, agricultural practices in the Great Plains of the United States have dramatically reduced soil organic carbon (C) levels and increased greenhouse gas (GHG) fluxes in this region. This paper details the development of an innovative method to assess these processes. Detailed land-use data sets that specify complete agricultural histories for 21 representative Great Plains counties reflect historical changes in agricultural practices and drive the biogeochemical model, DAYCENT, to simulate 120 years of cropping and related ecosystem consequences. Model outputs include yields of all major crops, soil and system C levels, soil trace-gas fluxes (N2O emissions and CH4 consumption), and soil nitrogen mineralization rates. Comparisons between simulated and observed yields allowed us to adjust and refine model inputs, and then to verify and validate the results. These verification and validation exercises produced measures of model fit that indicated the appropriateness of this approach for estimating historical changes in crop yield. Initial cultivation of native grass and continued farming produced a significant loss of soil C over decades, and declining soil fertility led to reduced crop yields. This process was accompanied by a large GHG release, which subsided as soil fertility decreased. Later, irrigation, nitrogen-fertilizer application, and reduced cultivation intensity restored soil fertility and increased crop yields, but led to increased N2O emissions that reversed the decline in net GHG release. By drawing on both historical evidence of land-use change and scientific models that estimate the environmental consequences of those changes, this paper offers an improved way to understand the short- and long-term ecosystem effects of 120 years of cropping in the Great Plains.