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. data preservation ͉ data sharing ͉ disclosure risk ͉ social surveys ͉ spatial social science C onsidering individuals in their spatial contexts opens a rich array of analytic possibilities. Geographers have traditionally considered the spatial organization of populations and their characteristics. Other social scientists have focused on the importance of social location (defined by age, race, gender, education, position in social networks, etc.) for actions of individuals and families, with little attention to the importance of spatial location and spatial relationships. The increasing integration of these two lines of inquiry is made possible by methodological advances in the spatial sciences and data collection advances in the social sciences. As a result, spatially explicit data sets that contain information on the attitudes and behaviors of individuals and households are now being created that permit researchers to address important scientific questions that have heretofore resisted methodologically defensible empirical analysis.Although the creation of these new data sets is good news for the spatial and social scientific communities, their exploitation to further our understanding of the causes and consequences of human behavior is currently being hampered by uncertainty about the effects of the availability of such spatially explicit data on the risk of causing harm to respondents through confidentiality breaches. This uncertainty is leading to the underutilization of data and is increasing the ...

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.

This paper analyzes factors that affect net migration rates in counties in the U.S. Great Plains between 1930 and 1990, emphasizing the roles of weather (especially drought), environmental amenities, employment, and population, making use of a rich county-level data set. Using a pooled time series model the paper shows that environment is important in population processes, with weather and agricultural change more important in the 1930s and 1940s, and environmental amenities more important in later time periods. The paper provides important insights into how environmental impacts on migration might change over time, and how those changes might be measured.

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