Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria

In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.

Comparison of maps of spatial variability of soil resistance to penetration constructed with and without covariables using a spatial linear model

A study about the spatial variability of data of soil resistance to penetration (RSP) was conducted at layers 0.0-0.1 m, 0.1-0.2 m and 0.2-0.3 m depth, using the statistical methods in univariate forms, i.e., using traditional geostatistics, forming thematic maps by ordinary kriging for each layer of the study. It was analyzed the RSP in layer 0.2-0.3 m depth through a spatial linear model (SLM), which considered the layers 0.0-0.1 m and 0.1-0.2 m in depth as covariable, obtaining an estimation model and a thematic map by universal kriging. The thematic maps of the RSP at layer 0.2-0.3 m depth, constructed by both methods, were compared using measures of accuracy obtained from the construction of the matrix of errors and confusion matrix. There are similarities between the thematic maps. All maps showed that the RSP is higher in the north region.

Distribution and density of moose in relation to landscape characteristics: effects of scale

We analyzed the relation between early winter distribution and density of female moose (Alces alces L.) and habitat heterogeneity in interior Alaska. We tested for effects of vegetation type, topography, distance to rivers and towns, occurrence and timing of fire, and landscape metrics. A spatial linear model was used to analyze effects of independent variables organized at multiple scales. Because densities of moose vary widely as a result of differences in management and other factors, a spatial response surface of the log of moose density was fit to remove large-scale effects. The analysis revealed that the densest populations of moose occurred closer to towns, at moderate elevations, near rivers, and in areas where fire occurred between 11 and 30 years ago. Furthermore, moose tended to occur in areas with large compact patches of varied habitat and avoided variable terrain and nonvegetated areas. Relationships of most variables with moose density occurred at or below 34 km2, suggesting that moose respond to environmental variables within a few kilometres of their location. The spatial model of density of moose developed in this study represents an important application for effective monitoring and management of moose in the boreal forest.