Decision algorithms and software developed for particular types of decision support that deal with geographic space. Developed with GIS technology, but without requiring programming skills or knowledge of GIS software, SDSS uses digital maps, tables, and charts that are intuitive and help reduce the amount of information processing needed to make complex decisions.
Published in Chapter:
Geography and Public Health
Robert Lipton (Prevention Research Center, USA), D. M. Gorman (Texas A&M University, USA), William F. Wieczorek (Center for Health and Social Research, Buffalo State College-State University of New York, USA), Aniruddha Banerjee (Prevention Research Center, USA), and Paul Gruenewald (Prevention Research Center, USA)
Copyright: © 2009
|Pages: 12
DOI: 10.4018/978-1-60566-026-4.ch258
Abstract
From John Snow’s pioneering work on cholera in the 19th century until the present day, placing illness and disease within the context of a geographic framework has been an integral, if understated, part of the practice of public health. Indeed, geographical/spatial methods are an increasingly important tool in understanding public health issues. Spatial analysis addresses a seemingly obvious yet relatively misunderstood aspect of public health, namely, studying the dynamics of people in places. As advances in computer technology increase almost exponentially, computer intensive spatial methods (including mapping) have become an appealing way to understand the manner in which the individual relates to larger frameworks that compose the human community and the physical nature of human environments (streets with intersections, dense vs. sparse neighborhoods, high or low densities of liquor stores or restaurants, etc.). Spatial methods are extremely data intensive, often pulling together information from disparate sources that have been collected for other purposes such as research, business practice, governmental policy, and law enforcement. Although initially more demanding in regard to data manipulation compared to typical population level methods, the ability to compile and compare data in a spatial framework provides information about human populations that lies beyond typical survey or census research. We will discuss general methods of spatial analysis and mapping that will help to elucidate when and how spatial analysis might be used in a public health setting. This discussion will include a method for transforming arbitrary administrative units, such as zip codes, into a more useable uniform grid structure. In addition, a practical research example will be discussed focusing on the relationship between alcohol and violence. A relatively new Bayesian spatial method will be part of this example.