Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2525-2018
https://doi.org/10.5194/gmd-11-2525-2018
Model description paper
 | 
25 Jun 2018
Model description paper |  | 25 Jun 2018

An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping

Daojun Zhang, Na Ren, and Xianhui Hou

Viewed

Total article views: 3,576 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,278 1,155 143 3,576 280 94 127
  • HTML: 2,278
  • PDF: 1,155
  • XML: 143
  • Total: 3,576
  • Supplement: 280
  • BibTeX: 94
  • EndNote: 127
Views and downloads (calculated since 16 Jan 2018)
Cumulative views and downloads (calculated since 16 Jan 2018)

Viewed (geographical distribution)

Total article views: 3,576 (including HTML, PDF, and XML) Thereof 3,335 with geography defined and 241 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 Dec 2024
Download
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 a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.