in key parameters allow researchers to uncover greater specificity of spatial relationships. This method has gained widespread use in geographic analysis, from its introduction into the literature (Casetti, 1972) to its use in a range of applications and interpretations (Jones and Casetti, 1992). Research by geographers and regional scientists has shown how to derive spatial interaction models based on either traditional information theory or optimal decision making theory. This theoretical work has been extended to analysis of the interaction between consumers and suppliers of services. Spatial interaction simulations can pose "what if" questions about retail patterns and behavior similar to the questions about the flows of goods among states. Much of the literature in relatively new academic journals such as Geographical Systems; Location Science; and Computers, Environments, and Urban Systems contains illustrations of such models.

Example: Human Migration

Decisions to relocate are among the more important decisions made by households, with far-reaching implications for the links between places. Conceptualization of the search and selection process by Wolpert (1965) and Brown and Moore (1971) has been formalized in a model of decision making and housing search under uncertainty (Smith et al., 1979). This model incorporates both preferences and expectations of relocation decisions and provides important insights into household searches within the residential environment.

Recent work on modeling of migration and mobility seeks to address the dynamic nature of the process and the way in which decisions to move are related to age, family composition, and economic circumstances (Clark, 1992; Clark et al., 1994). For example, one of the strongest microlevel determinants of whether individuals are likely to move is age or stage in the life cycle (see Sidebar 5.7). During the 1970s, all of these influences were evidenced as the extremely large baby boom cohort (people born from 1946 to 1964) passed through the peak mobility ages (ages 20-34).

Few social science variables can be confidently forecasted far into the future. Barring major calamities, however, the inexorability of the aging process makes future age composition one of the best independent variables for population forecasting applications. As geographers learn more about these demographic influences on migration, population analysts should become better able to inform public policy at both national and local scales.

Example: Watershed Dynamics

Through their research, physical geographers have demonstrated the importance of interdependencies between places on understanding the environment. A major contribution to research on river ecosystems, for instance, has been the



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