To address the complex interactions between humans and wildlife habitat, we developed a conceptual framework that links human factors with forested landscapes and wildlife habitat. All the components in the framework are integrated into systems models that analyze the effects of human factors and project how wildlife habitat would change under different policy scenarios. As a case study, we applied this framework to the Wolong Nature Reserve in Sichuan Province (southwestern China), the largest home of the giant panda (Ailuropoda melanoleuca). We collected ecological and socioeconomic data with a combination of various methods ( field observations, aerial photographs, government documents and statistics, interviews, and household surveys) and employed geographic information systems and systems modeling to analyze and integrate the data sources. Human population size has increased by 66% and the number of households in the reserve has increased by 115% since 1975, when the reserve was established. During the same period, the quality and quantity of the giant panda habitat dramatically decreased because of increasing human activities such as fuelwood collection. Systems modeling predicted that under the status quo, human population in the reserve would continue to grow and cause more destruction of the remaining panda habitat, whereas reducing human birth rates and increasing human emigration rates would lower human population size and alleviate human impacts on the panda habitat. Furthermore, our simulations and surveys suggested that policies encouraging the emigration of young people would be more effective and feasible than relocating older people in reducing human population size and conserving giant panda habitat in the reserve.

Internal migration within the United States continues to transform both the magnitude and composition of population at all geographic scales. During 1994 ‐ 1995, the majority of counties gained both people and income, largely as a consequence of net outmigration by higher income migrants from the nation's most populous cities. Regionally, net gainers of both people and income included counties in the West and South as well as other areas renowned for environmental amenities. Spatially, net migration flowed down the urban hierarchy from large central cities to adjacent suburbs which, in turn, exported migrants to exurban areas. Large cities tended to exchange migrants with nearby counties as well as other large cities. Migration patterns such as these are contributing to spatial deconcentration and economic disparity.

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BackgroundCommunity hospital placement is dictated by a diverse set of geographical factors and historical contingency. In the summer of 2004, a multi-organizational committee headed by the State of Michigan's Department of Community Health approached the authors of this paper with questions about how spatial analyses might be employed to develop a revised community hospital approval procedure. Three objectives were set. First, the committee needed visualizations of both the spatial pattern of Michigan's population and its 139 community hospitals. Second, the committee required a clear, defensible assessment methodology to quantify access to existing hospitals statewide, taking into account factors such as distance to nearest hospital and road network density to estimate travel time. Third, the committee wanted to contrast the spatial distribution of existing community hospitals with a theoretical configuration that best met statewide demand. This paper presents our efforts to first describe the distribution of Michigan's current community hospital pattern and its people, and second, develop two models, access-based and demand-based, to identify areas with inadequate access to existing hospitals.ResultsUsing the product from the access-based model and contiguity and population criteria, two areas were identified as being "under-served." The lower area, located north/northeast of Detroit, contained the greater total land area and population of the two areas. The upper area was centered north of Grand Rapids. A demand-based model was applied to evaluate the existing facility arrangement by allocating daily bed demand in each ZIP code to the closest facility. We found 1,887 beds per day were demanded by ZIP centroids more than 16.1 kilometers from the nearest existing hospital. This represented 12.7% of the average statewide daily bed demand. If a 32.3 kilometer radius was employed, unmet demand dropped to 160 beds per day (1.1%).ConclusionBoth modeling approaches enable policymakers to identify under-served areas. Ultimately this paper is concerned with the intersection of spatial analysis and policymaking. Using the best scientific practice to identify locations of under-served populations based on many factors provides policymakers with a powerful tool for making good decisions.

Interstate migration exchanges in the United States are temporally and spatially transitory. Both the early and mid‐1980s exhibited significant fluctuations in the origins and destinations of U.S. migrants, while the late 1980s and early 1990s were even more unstable. Regions once favored by interstate movers such as the West and the South, while remaining attractive, showed evidence of declining favor in the early 1990s. Meanwhile, numerous states in the national interior regained their attractiveness, including several that gained net migrants for the first time in decades. California exhibited a major turnaround in its migration, perturbing the entire U.S. migration system.