gdverse - Analysis of Spatial Stratified Heterogeneity
Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, and also takes into account local spatial dependencies, spatial interpretability, potential spatial interactions, and robust spatial stratification. Additionally, it supports geographical detector models established in academic literature.
Last updated 3 hours ago
geographical-detectorgeoinformaticsgeospatial-analysisspatial-statistics
13 stars 2.35 score 74 dependenciesgeocomplexity - Geocomplexity Mitigates Spatial Bias
The geographical complexity of individual variables can be characterized using the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by the fluctuations in similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.
Last updated 3 hours ago
geospatial-analysisspatial-regressionspatial-relationsspatial-samplingspatial-statistics
3 stars 1.38 score 41 dependenciesgeosimilarity - 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.
Last updated 4 days ago
geoinformaticsspatial-predictions
4 stars 1.27 score 34 dependenciesSecDim - 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.
Last updated 20 days ago
spatial-associationspatial-predictions
1 stars 0.23 score 5 dependencies