Showing 4 of total 4 results (show query)
ausgis
geosimilarity:Geographically Optimal Similarity
Understanding spatial association is essential for spatial statistical inference, including factor exploration and spatial prediction. Geographically optimal similarity (GOS) model is an effective method for spatial prediction, as described in Yongze Song (2022) <doi:10.1007/s11004-022-10036-8>. GOS was developed based on the geographical similarity principle, as described in Axing Zhu (2018) <doi:10.1080/19475683.2018.1534890>. GOS has advantages in more accurate spatial prediction using fewer samples and critically reduced prediction uncertainty.
Maintained by Wenbo Lv. Last updated 2 months ago.
geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics
6 stars 5.38 score 5 scripts
spatlyu
spEcula:Spatial Prediction Methods In R
Advanced spatial prediction methods based on various spatial relationships.
Maintained by Wenbo Lv. Last updated 9 months ago.
geoinformaticsgisciencespatial-analysisspatial-predictionsspatial-statistics
21 stars 5.02 score 6 scripts
iiasa
ibis.iSDM:Modelling framework for integrated biodiversity distribution scenarios
Integrated framework of modelling the distribution of species and ecosystems in a suitability framing. This package allows the estimation of integrated species distribution models (iSDM) based on several sources of evidence and provided presence-only and presence-absence datasets. It makes heavy use of point-process models for estimating habitat suitability and allows to include spatial latent effects and priors in the estimation. To do so 'ibis.iSDM' supports a number of engines for Bayesian and more non-parametric machine learning estimation. Further, the 'ibis.iSDM' is specifically customized to support spatial-temporal projections of habitat suitability into the future.
Maintained by Martin Jung. Last updated 5 months ago.
bayesianbiodiversityintegrated-frameworkpoisson-processscenariossdmspatial-grainspatial-predictionsspecies-distribution-modelling
21 stars 4.36 score 12 scripts 1 dependentsausgis
SecDim:The Second Dimension of Spatial Association
Most of the current methods explore spatial association using observations at sample locations, which are defined as the first dimension of spatial association (FDA). The proposed concept of the second dimension of spatial association (SDA), as described in Yongze Song (2022) <doi:10.1016/j.jag.2022.102834>, aims to extract in-depth information about the geographical environment from locations outside sample locations for exploring spatial association.
Maintained by Wenbo Lv. Last updated 7 months ago.
spatial-associationspatial-predictions
1 stars 2.70 score 2 scripts