Spatial Analysis Using Geographically Weighted Ordinary Logistic Regression (GWOLR) Method for Prediction of Particle-Size Fraction in Soil Surface

Abstract

Abstract Spatial analysis is a method used to understand the spatial variation of geospatial data. In this study, the Geographically Weighted Ordinary Logistic Regression (GWOLR) method was used in spatial analysis to predict the particle size fraction of the surface soil. The particle size fraction of the surface soil is an important parameter in determining soil productivity and environmental quality. However, the particle size fraction in surface soils can vary spatially and is influenced by geographical factors such as elevation, rainfall, and soil texture. This study will be carried out by collecting particle size fraction data and geospatial data at randomly selected locations. Accurate modelling of soil texture is necessary because it‘s a crucial factor in determining how soil management will go. However, because soil texture is a compositional data set, it is one of the soil attributes that is more challenging to model. The challenge presented by this compositional data set is the imposition of constant quantities, specifically the requirement that the total of the fractions of clay, silt, and sand be 100%. Topographical variability can be derived from DEM data, making it an independent variable or predictor for soil texture prediction. The data will then be analyzed using the GWOLR method to predict the particle size fraction at locations that have not been observed before. The resulting prediction model will then be evaluated using cross-validation to check the accuracy of the model. This study will provide benefits for land management and natural resource management and can improve understanding of the spatial variation of particle size fractions in surface soils and the spatial and geographical factors that influence them. The GWOLR model for predicting particle size fractions in surface soils was carried out with a fixed bi-square weight and a bandwidth of 0.28895. The GWOLR model classification accuracy value is 94 percent, this shows that the GWOLR model for predicting soil particle size is more suitable than the ordinal logistic regression model with a classification accuracy of 90 percent. The aims of this study are to: (1) Establish a soil texture prediction model using the GWOLR method; and (2) Test the reliability of the model in predicting surface soil texture.