Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-278
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
16 Jan 2018
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Geoscientific Model Development (GMD).
Improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
Daojun Zhang1,2, Na Ren1, and Xianhui Hou1 1College of Economics and Management, Northwest A&F University, Yangling 712100, China
2Center for Resource Economics and Environment Management, Northwest A&F University, Yangling 712100, China
Abstract. 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.
Citation: Zhang, D., Ren, N., and Hou, X.: Improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-278, in review, 2018.
Daojun Zhang et al.
Daojun Zhang et al.
Daojun Zhang et al.

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Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression, and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on spatially weighted technique. The new model also support anisotropic local window, which is another innovative point in this work.
Geographically weighted regression is a widely used method to deal with spatial heterogeneity,...
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