Modelling Spatial Autocorrelation in Spatial Interaction Data

2007 ◽  
Author(s):  
Manfred M. Fischer ◽  
Daniel A. Griffith
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Interaction ◽  
Interaction Data

Functional Regionalization of Spatial Interaction Data: A Comment

1981 ◽  
Vol 13 (5) ◽  
pp. 645-646 ◽  
Author(s):  
A Findlay ◽  
P B Slater
Keyword(s):  
Spatial Interaction ◽  
Interaction Data ◽  
Functional Regionalization ◽  
Independent Effects

A recent paper by Masser and Scheurwater (1980) favoured the adoption of the intramax procedure for functional regionalization, without satisfactorily investigating the independent effects of the approach of the procedure to standardization and to clustering. From an examination of these phases of the intramax procedure it is shown that the superiority of Masser and Scheurwater's approach is based on doubtful criteria.

scholarly journals Spatial Interaction Model for Healthcare Accessibility: What Scale Has to Do with It

2020 ◽  
Vol 12 (10) ◽  
pp. 4324
Author(s):  
Felipa de Mello-Sampayo
Keyword(s):  
Spatial Autocorrelation ◽  
Multilevel Modeling ◽  
Regression Models ◽  
Spatial Interaction ◽  
Healthcare Providers ◽  
Interaction Model ◽  
Local Variation ◽  
Spatial Interaction Model ◽  
Healthcare Accessibility ◽  
Spatial Autoregressive

This manuscript develops a theoretical spatial interaction model using the entropy approach to relax the assumption of the deterministic utility function. The spatial healthcare accessibility improves as the demand for healthcare increases or the opportunity cost of traveling to and from healthcare providers decreases. The empirical application used different spatial econometric techniques and multilevel modeling to evaluate the spatial distribution of existing hospitals in Texas and their social and economic correlates. To control for spatial autocorrelation, spatial autoregressive regression models were estimated, and geographically weighted regression models examined potential spatial non-stationarity. The multilevel modeling controlled for spatial autocorrelation and also allowed local variation and spatial non-stationarity. The empirical analysis showed that healthcare accessibility was not stationary in Texas in 2015, with areas of poor accessibility in rural and peripheral areas in Texas, when using hospitals’ location and county data. The model of spatial interaction applied to healthcare accessibility can be used to evaluate policies aiming at the provision of health services, such as closures of hospitals and capacity increases.

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