Understanding spatial variability of soil properties is needed for agricultural productivity, food safety and environmental modeling. The main aim of the study is to understand factors influencing spatial variations in soil acidity and other soil properties among different land use types, within upper and lower catchments of the present study area. Four major land use types were identified: grazing land, crop land, agro-forest land and natural forest land. Soil samples were collected for laboratory analysis from each of four different land use types in both catchments with three replications. 1kg of soil was thoroughly mixed at each of the land use systems and a total of 24 composite samples were taken along the transect lines of 20x20m plots. Based on laboratory result by using geospatial and geo statistical analysis tools of semi-variogram and krinking interpolation in Arc GIS v 10.7, semi-vario gram models and geospatial distribution maps of the study area were generated. Four parameters of soil: pH, electrical con-ductility (EC), cation exchange capacity (CEC) and organic carbon (OC) were analyzed. Using a spatial analysis (spatial statistical) technique kringing interpolation method, the distribution of these parameters were mapped. The results show that soil pH, EC, CEC and OC inclined to exponentially following; grazing, forest, crop and forest lands at lower catchment respectively. It indicates that soil acidity increases with altitude increase. A positive correlation was observed between OC and CEC. The Quantileâ€“ quantile (Q-Q) plot shows that pH, EC, CEC and OC exhibited a normal distribution between the actual and predicted value. The coefficient of variation indicates that pH, elevation, precipitation and temperature have the least variability on both catchments. Moderate variability was observed in CEC, EC, and OC in lower and upper catchments and high variability was also observed in the slope of lower and upper catchments. The value for nugget/sill of pH was 0.25â€“0.75 which indicates moderate spatial autocorrelation among the variables. The cold spot or statistically significant aggregation of low values of pH (acidity) acidity was shown in crop land area at the upper catchment, whereas the hotspot or aggregation of high pH values was shown in forest land area at the lower catchment. The present study suggests that the geospatial model can reveal the variability of soil properties and its relationships with different land use/cover types and topographic variables. This type of spatially explicit information helps in targeted management interventions, and effective soil fertility management in problematic soils in the study area.