Using Geographically Weighted Regression to Validate Approaches for Modelling Accessibility to Primary Health Care

Bagheri, Nasser; Holt, Alec; Benwell, George L.

doi:10.1007/s12061-009-9021-0

Cited by 25 publications

(24 citation statements)

References 28 publications

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“…The relationship among the census variables of population, homes, and housing and the dependent variable suggests that, in general, the zones with shorter travel times to the hospital network centres showed better socio-economic characteristics when compared to those with longer ones. This aspect is in line with the results found by Bagheri et al (2009) in the exploration of the local variation of the accessibility to primary health care based on a deprivation index in an GWR analysis. Likewise, Shah and Bell (2013) have published reports, where the advantages of this method are shown in the disaggregation of the relationships between socio demographic variables and the geographic accessibility to the primary health care services at the local scale.…”

Section: Discussionsupporting

confidence: 90%

“…Taking the localities as a unit of origin, an aggregation of values was done considering the average travel times from these localities to the district level for their incorporation as a dependent variable in the modelling. The travel times were integrated into diverse studies in order to model accessibility to primary health care centres/hospitals (Brabyn and Skelly, 2001;Hare and Barcus, 2007;Bagheri et al, 2009;Rodríguez, 2010;Munoz and Kallestal, 2012), where the location factor (population, health care centres) and the characteristics of the network were considered in order to obtain the travel time factor (speed and length of the road sections).…”

Section: Accessibility Modelling Methodsmentioning

confidence: 99%

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Geographically weighted regression for modelling the accessibility to the public hospital network in Concepción Metropolitan Area, Chile

Bascuñán¹,

Rojas²

2016

Geospat Health

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Accessibility models in transport geography based on geographic information systems have proven to be an effective method in determining spatial inequalities associated with public health. This work aims to model the spatial accessibility from populated areas within the Concepción metropolitan area (CMA), the second largest city in Chile. The city's public hospital network is taken into consideration with special reference to socio-regional inequalities. The use of geographically weighted regression (GWR) and ordinary least squares (OLS) for modelling accessibility with socioeconomic and transport variables is proposed. The explanatory variables investigated are: illiterate population, rural housing, alternative housing, homes with a motorised vehicle, public transport routes, and connectivity. Our results identify that approximately 4.1% of the population have unfavourable or very unfavourable accessibility to public hospitals, which correspond to rural areas located south of CMA. Application of a local GWR model (0.87 R 2 adjusted) helped to improve the settings over the use of traditional OLS methods (multiple regression) (0.67 R 2 adjusted) and to find the spatial distribution of both coefficients of the explanatory variables, demonstrating the local significance of the model. Thus, accessibility studies have enormous potential to contribute to the development of public health and transport policies in turn to achieve equality in spatial accessibility to specialised health care.

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Section: Discussionsupporting

confidence: 90%

Section: Accessibility Modelling Methodsmentioning

confidence: 99%

Geographically weighted regression for modelling the accessibility to the public hospital network in Concepción Metropolitan Area, Chile

Bascuñán¹,

Rojas²

2016

Geospat Health

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“…The usefulness of GWR in studying issues of spatial accessibility to primary healthcare has already been demonstrated (Bagheri et al, 2009). In our hands, the GWR approach outperformed the OLS models by being capable to completely reset the residual spatial autocorrelation frequent in spatial datasets.…”

Section: Usefulness Of Geographically Weighted Regression In Modelingmentioning

confidence: 65%

Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach

Domnich¹,

Arata²,

Amicizia³

et al. 2016

Geospat Health

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Geographical accessibility is an important determinant for the utilisation of community pharmacies. The present study explored patterns of spatial accessibility with respect to pharmacies in Liguria, Italy, a region with particular geographical and demographic features. Municipal density of pharmacies was proxied as the number of pharmacies per capita and per km 2 , and spatial autocorrelation analysis was performed to identify spatial clusters. Both non-spatial and spatial models were constructed to predict the study outcome. Spatial autocorrelation analysis showed a highly significant clustered pattern in the density of pharmacies per capita (I=0.082) and per km 2 (I=0.295). Potentially under-supplied areas were mostly located in the mountainous hinterland. Ordinary least-squares (OLS) regressions established a significant positive relationship between the density of pharmacies and income among municipalities located at high altitudes, while no such association was observed in lower-lying areas. However, residuals of the OLS models were spatially auto-correlated. The best-fitting mixed geographically weighted regression (GWR) models outperformed the corresponding OLS models. Pharmacies per capita were best predicted by two local predictors (altitude and proportion of immigrants) and two global ones (proportion of elderly residents and income), while the local terms population, mean altitude and rural status and the global term income functioned as independent variables predicting pharmacies per km 2 . The density of pharmacies in Liguria was found to be associated with both socio-economic and landscape factors. Mapping of mixed GWR results would be helpful to policy-makers.

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“…It is a powerful tool and is widely used in geographic research [37][38][39][40]. Similar to linear regression, the GWR model also requires normal distribution of the dependent variable.…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Chen

Liu

Zhou

et al. 2017

IJGI

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Abstract:The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

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Using Geographically Weighted Regression to Validate Approaches for Modelling Accessibility to Primary Health Care

Cited by 25 publications

References 28 publications

Geographically weighted regression for modelling the accessibility to the public hospital network in Concepción Metropolitan Area, Chile

Geographically weighted regression for modelling the accessibility to the public hospital network in Concepción Metropolitan Area, Chile

Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

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