Table 3.

Model validation and main results of the ordinary least squares (OLS) regression and geographically weighted regression (GWR) models of malaria incidence (API) in northeastern Venezuela

Model (year)	AICc	R2	ANOVA
	OLS† | GWR†	OLS | GWR	F-value
2001	59.87 | 70.311	0.18 | 0.47	3.72*
2002	66.77 | 71.613	0.37 | 0.53	3.89*
2003	60.47 | 62.913	0.28 | 0.51	4.28*
2004	51.017 | 56.7913	0.33 | 0.47	2.94
2005	54.67 | 64.011	0.16 | 0.49	3.85*
2006	43.97 | 43.513	0.57 | 0.74	5.70*
2007	46.97 | 72.58	0.41 | 0.86	9.29**

Analysis of variance (ANOVA) tests the null hypothesis that the GWR model represents no improvement over the global OLS model. A Monte Carlo test compares the difference in the residual sums of squares of the OLS model with the residual sum of squares of the GWR model. R² represents the adjusted coefficient of determination, and AIC_c is the corrected Akaike information criteria. Model variables were population density, terrain elevation, terrain slope, number of aquatic habitats, distance to the nearest breeding site, and distance to the main road.

^*P < 0.05; ** P < 0.01.

^†DF of OLS and GWR residuals, respectively.