When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

doi:10.4081/gh.2014.45

機構典藏 > College of Business and Management > Graduate Institute & Department of Statistics > Journal Article > Item 987654321/99137

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/99137

Title:	When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization
Authors:	Carla Shoff;Chen, Vivian Yi-Ju;Yang, Tse-Chuan
Contributors:	淡江大學統計學系
Keywords:	prenatal care;geographically weighted regression;spatial non-stationarity
Date:	2014-05-01
Issue Date:	2014-10-15 14:28:02 (UTC+8)
Publisher:	Naples: Universita degli Studi di Napoli "Federico II" * Facolta di Medicina Veterinaria
Abstract:	Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilisation in the United States of America (USA) and found that most of the relationships between late or no prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study was to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employed an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and that the results change after considering the SL effect of prenatal care utilisation. The GWR-SL approach allowed us to gain a placespecific understanding of prenatal care utilisation in USA counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., ordinary least squares and spatial lag models) and found that GWR-SL is the preferred modelling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilisation across space, and determine whether and how the level of prenatal care utilisation in neighbouring counties matters.
Relation:	Geospatial Health 8(2), pp.557-568
DOI:	10.4081/gh.2014.45
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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