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Applied Spatial Statistics for Public Health Data: Wiley Series in Probability and Statistics

Autor LA Waller
en Limba Engleză Hardback – 26 iul 2004
While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of "data breaks") Exercises based on data analyses reinforce concepts
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Specificații

ISBN-13: 9780471387718
ISBN-10: 0471387711
Pagini: 520
Dimensiuni: 156 x 234 x 29 mm
Greutate: 0.85 kg
Ediția:New.
Editura: Wiley
Seria Wiley Series in Probability and Statistics

Locul publicării:Hoboken, United States

Public țintă

Graduate Students in Biostatistics or Epidemiology; Public Health Researchers in Universities or Government Institutions; Students and Readers of Spatial Analysis with Varied Backgrounds (in Agriculture, Ecology, Geography, Geology, and/or Environmental Studies); Corporate, Public, and Private Libraries

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Descriere

While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a growth, thanks in part to the development of geographical information systems (GIS's).