To effectively understand the availability of soil nitrogen and assist in soil nitrogen control at the regional scale, it is essential to understand the accurate spatial distribution patterns of the three soil nitrogen parameters [i.e., total nitrogen (TN), available nitrogen (AN) and nitrogen availability ratio (NAR)] and explore the spatially varying influences of major impact factors on soil AN. Land use affects the spatial distributions of soil TN, AN and NAR (i.e., AN/TN). To explore the effects of different land use types and improve mapping accuracy, residual kriging with land use information and ordinary kriging (without land use information) were compared based on the sample data of soil TN and AN in Hanchuan county, China. A local regression technique, geographically weighted regression (GWR), was adopted to explore the varying relationships between soil AN and its major impact factors in soil (i.e., soil TN and soil pH), due to the advantages of GWR over the traditional ordinary least squares regression (OLS) model. The results showed that (1) land use types as auxiliary information obviously improved the prediction accuracies of the three soil nitrogen parameters; (2) GWR performed much better than OLS in terms of fitting accuracy; and (3) GWR effectively revealed the spatially varying influences of the impact factors on soil AN, which were ignored by OLS. Based on the results, suggestions for soil nitrogen control measures in different subareas were proposed.