Applied Spatial Statistics for Public Health DataWhile 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 |
Contents
| 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 |
| 473 | |
| 481 | |
Other editions - View all
Applied Spatial Statistics for Public Health Data Lance A. Waller,Carol A. Gotway No preview available - 2004 |
Applied Spatial Statistics for Public Health Data Lance A. Waller,Carol A. Gotway No preview available - 2004 |
Applied Spatial Statistics for Public Health Data Lance A. Waller,Carol A. Gotway No preview available - 2004 |
Common terms and phrases
addition allow alternative analysis appear applications approach assess associated assume assumptions bandwidth Chapter circles clustering collection components conditional consider constant risk containing controls correlation corresponding counts covariance data set defined denotes dependence described direction discussion disease distance distribution effects empirical equation errors estimate example expected exposure Figure function Gaussian geographic given hypothesis illustrate incidence proportions increase independent indicate inference intensity interest kriging leukemia likelihood linear locations matrix maximum mean measure methods Monte Carlo null observed obtain occur parameters particular patterns plot Poisson population prediction prior probability random rates ratio reference regions regression relative represents residuals respectively risk sample semivariogram similar simulations smoothing spatial specific standard statistical structure study area suggest surface Table tests tracts units values variable variance weights