help geographical scientists account for the impacts of exposure to potentially hazardous substances in different places (Meliker and Jacquez, 2007). Geographical scientists are also exploring ways of using remote sensing (satellite data and hyperspectral imagery) in the study of disease location and spread (Goovaerts et al., 2005; Avruskin et al., 2008).

The geographical sciences add at least four dimensions to the investigation of disease incidence and response, and of conditions such as drug addiction (Thomas et al., 2008). First, they provide a series of techniques suited to the detection of patterns and anomalies in the incidence of disease, and to their interpretation in terms of disease-spreading and disease-causing mechanisms. From the earliest successes achieved by John Snow in unraveling the etiology of cholera (Johnson, 2006) to modern techniques for detecting statistical significance in apparent clusters (O’Sullivan and Unwin, 2003), a vast amount of pure and applied research has gone into the examination of health and disease patterns, and their connection to other circumstances and processes in the human–environment system (Cromley and McLafferty, 2002; Waller and Gotway, 2004; Koch, 2005). Second, geographical science tools allow researchers to ask questions about the factors present in an area that correlate with disease outcomes, whether as causes or as statistical correlates. Geographic information systems (GIS) have proved valuable in this regard because they support a range of multivariate analyses and can be used to analyze the impacts of factors at different scales. Third, geographical scientists have developed spatially explicit models of disease spread that incorporate concepts such as distance and connectivity directly into the models, often in the form of cellular automata2 (Schiff, 2008) or as agent-based models (Maguire et al., 2005). Finally, the concern of the geographical sciences with location promotes consideration of access to health care. When facing conditions such as heart attacks, the distance between the patient and the emergency room and the ability to get to the emergency room quickly can literally be the difference between life and death. Geographical scientists have found dramatic impacts of differentially located health care facilities, at scales that range from the global to the local (e.g., O’Meara et al., 2009), and they have developed a range of techniques for optimizing the location of facilities to achieve desirable goals (e.g., Rushton, 2003).

Much health research makes use of statistical techniques to make general conclusions from samples of laboratory animals or patients. When these experiments are controlled, by choosing random samples and giving them identical treatments, the conditions conform precisely to the assumptions made in standard statistical tests. In such cases significance levels can be determined, and results generalized to the populations from which the samples were randomly drawn. However, in research focused on actual fine-scale geographical patterns rather than controlled conditions, these assumptions are rarely valid (O’Sullivan and Unwin, 2003). Analysis at the national level often uses health statistics aggregated to the county or even state level—units of analysis that have their origins in previous centuries, vary enormously in size, and average or smooth out much of the actual variation that occurs at smaller scales. These statistical issues provide examples of the substantial problems confronting health researchers that the technical arsenal of the geographical sciences can help resolve. The following questions illustrate the types of research that allow the geographical sciences to contribute to the development of a multidisciplinary synthesis, which is needed to tackle major health issues in the years to come.


How do diseases respond to changes in ecosystems and climate?

Spatial distributions of diseases are seldom uniform, and understanding the reasons for their heterogeneity can lead to valuable insights. As our ability to conduct spatial and temporal analysis has improved, so has our ability to characterize both sides of the disease equation—the genetics of diseases themselves and human risk factors. Advances in spatial and temporal analysis are also facilitating efforts to respond to changes in the human–environment system.

On the genetic side, new insights into how and why diseases emerge and sometimes lead to pandemics such


Cellular automata models attempt to simulate processes operating across geographical space by dividing the space into a set of normally rectangular cells and applying a series of rules to those cells to approximate changes in each cell’s state through time, in response to the state of neighboring cells as well as to external factors.

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