Due to complexity, multiple minerogenic stages, and superposition during geological processes, the spatial distributions of geological variables also exhibit specific trends and non-stationarity. For example, geochemical elements exhibit obvious spatial non-stationarity and trends because of the deposition of different types of coverage. Thus, bias may clearly occur under these conditions when general regression models are applied to mineral prospectivity mapping (MPM). In this study, we used a spatially weighted technique to improve general logistic regression and developed an improved model called the improved logistic regression model based on spatially weighted technique (ILRBSWT, version 1.0). The capabilities and advantages of ILRBSWT are as follows: (1) ILRBSWT is essentially a geographically weighted regression (GWR) model, and thus it has all its advantages when dealing with spatial trends and non-stationarity; (2) the current software employed for GWR mainly applies linear regression whereas ILRBSWT is based on logistic regression, which is used more commonly in MPM because mineralization is a binary event; (3) a missing data process method borrowed from weights of evidence is included to extend the adaptability when dealing with multisource data; and (4) the differences of data quality or exploration level can also be weighted in the new model as well as the geographical distance.