Applied Spatial Statistics for Public Health DataJohn Wiley & Sons, 2004/07/29 - 520 ページ 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 |
目次
7 | |
3 Spatial Data | 38 |
4 Visualizing Spatial Data | 68 |
5 Analysis of Spatial Point Patterns | 118 |
6 Spatial Clusters of Health Events Point Data for Cases and Controls | 155 |
7 Spatial Clustering of Health Events Regional Count Data | 200 |
8 Spatial Exposure Data | 272 |
9 Linking Spatial Exposure Data to Health Events | 325 |
References | 444 |
Author Index | 473 |
Subject Index | 481 |
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多く使われている語句
analysis applications approach assess associated assume assumptions bandwidth Bayesian Besag centroid Chapter choropleth map circles constant risk hypothesis controls covariance Cressie data break data set defined denotes density Diggle disease distance distribution empirical semivariogram Epidemiology equation errors estimate example expected exposure Figure Gaussian geographic geostatistical GLMM GLMs grid illustrate incidence proportions inference intensity function kernel kriging linear model linear regression locations log ratio matrix methods Monte Carlo Moran's nugget effect null hypothesis observed p-value parameters plot Poisson distribution Poisson process Poisson regression prediction probability public health data random effects rates ratio regional count regression model residuals sample scan statistic Section simulations smoothing spatial autocorrelation spatial correlation spatial data spatial dependence spatial patterns spatial statistics specific standard population studentized residuals study area Tango's index test statistic total number tracts values variable variance variance-covariance matrix variogram vector weighted York leukemia data