The smoothed incidence was computed from the total number of cases per 10,000 at each village divided by the total number of people at risk within the village, which was specified using a spatial weights file including the village.
The smoothed incidence was computed from the total number of cases in a spatial "window" divided by the total number of people at risk within the "window", which was specified using a spatial weights file including both county and its neighbor counties' locations.
This is done by using a spatial neighborhood matrix of 5×5 pixels, centered on each pixel and a one-dimensional weight kernel with the following coefficients: (0.0625,0.25,0.375,0.25,0.0625) [2].
This is done by using a spatial neighborhood matrix of 5 × 5 pixels, centered on each pixel, and a one-dimensional weight matrix, (0.0625,0.25,0.375,0.25,0.0625), [24].
To assess genetic isolation by distance, spatial genetic structure was investigated using a spatial autocorrelation method.
Ordinary least squares (OLS) regression, both simple (unadjusted) and multiple (adjusted), and spatially weighted OLS regression with a spatial weights matrix were used.
SAR and other types of models make use of the spatial relationships among neighbors in order to construct a spatial weights matrix, which is further used to model nonindependent (i.e., autocorrelated) errors.
Figure 3 presents the spatial association (LISA) of the local indicators using alternative spatial weight matrices.
Together, a connectivity matrix and a weighting function define a spatial weighting matrix that can be used as a predictor to model spatial variation of biological data.
We propose the use of the Spatial Autoregressive (SAR) model, a type of spatial model that accounts for the proximity of observations in space by including a spatial weighting matrix in the equation [45].
