Sentence examples for both spatial regression models from inspiring English sources
Similarly, we have found that districts with the higher proportion of households in the bottom wealth quintile were characterized by high IMR for both spatial regression models.
The two spatial regression models were consistent in both the performance statistics and the selection of significant explanatory variables, thus confirming the usefulness and reliability of spatial regression in modeling urban land patterns.
Spatial regression models have therefore been developed under both frequentist and Bayesian approaches, with common methods used in epidemiological studies being Conditional Autoregressive Models (CAR), Geographically Weighted Regression (GWR) and the Besag York and Molliè (BYM) approach (Auchincloss et al. 2012; Chaney and Rojas-Guyler 2016; Rezaeian et al. 2007).
Spatial regression models also suggested a stronger microbial influence on the availability of these nutrients when compared to stand characteristics.
Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis.
Spatial regression models were then used to explore the relationships between urban landscape change and its spatial determinants.
Spatial regression models further revealed that changes of agricultural landscapes showed diverging relationships with urbanization indicators for each landscape metric.
Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations.
The results also confirm the superiority of the spatial regression models, and better fitting is produced with the increase in the scale.
Two spatial regression models, spatial lag and spatial error models, are used to quantify the relationships at 18 resolutions ranging from 60 m to 1080 m, at 60 m intervals.
Spatial regression models were used to investigate how sociodemographic variables such as income, race/ethnicity, education, and age correlate with driving, bicycling, and walking distances to stores that sell organic or local food.
