Applied Spatial Statistics for Public Health Data
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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analysis applications approach assess associated assume assumptions autoregressive bandwidth Bayesian Besag case–control centroid Chapter choropleth map circles constant risk hypothesis controls covariance Cox process Cressie data break data set defined denotes density Diggle disease distance empirical semivariogram equation errors estimate example expected exposure Figure Gaussian geographic geostatistical GLMMs GLMs grid illustrate incidence proportions inference intensity function kernel kriging linear regression locations log ratio medieval grave methods Moran’s nugget effect null hypothesis observed p-value parameters plot Poisson distribution Poisson process Poisson regression prediction prior distributions probability public health data random effects rates ratio regional count regression model relative risk residuals sample scan statistic Section semivariogram model simulations smoothing spatial autocorrelation spatial correlation spatial data spatial dependence spatial patterns specific standard population studentized residuals study area Tango’s index test statistic total number tracts values variable variance variogram vector weights York leukemia data