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Neighborhoods and Health$
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Dustin T. Duncan and Ichiro Kawachi

Print publication date: 2018

Print ISBN-13: 9780190843496

Published to Oxford Scholarship Online: April 2018

DOI: 10.1093/oso/9780190843496.001.0001

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Statistical Methods in Spatial Epidemiology

Statistical Methods in Spatial Epidemiology

Chapter:
(p.91) 4 Statistical Methods in Spatial Epidemiology
Source:
Neighborhoods and Health
Author(s):

Samson Y. Gebreab

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190843496.003.0004

Most studies evaluating relationships between neighborhood characteristics and health neglect to examine and account for the spatial dependency across neighborhoods, that is, how neighboring areas are related to each other, although the possible presence of spatial effects (e.g., spatial dependency, spatial heterogeneity) can potentially influence the results in substantial ways. This chapter first discusses the concept of spatial autocorrelation and then provides an overview of different spatial clustering methods, including Moran’s I and spatial scan statistics as well as different models to map spatial data, for example, spatial Bayesian mapping. Next, this chapter discusses various spatial regression methods used in spatial epidemiology for accounting spatial dependency and/or spatial heterogeneity in modeling the relationships between neighborhood characteristics and health outcomes, including spatial econometric models, Bayesian spatial models, and multilevel spatial models.

Keywords:   spatial epidemiology, spatial autocorrelation, spatial heterogeneity, spatial clustering models, spatial econometric models, geographically weighted models, Bayesian spatial models, multilevel spatial models

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