gdverse - Analysis of Spatial Stratified Heterogeneity

Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, while also taking into account local spatial dependencies, spatial interpretability, complex spatial interactions, and robust spatial stratification. Additionally, it supports the spatial stratified heterogeneity family established in academic literature.

Last updated 5 days ago

geographical-detectorgeoinformaticsgeospatial-analysisspatial-statistics

8.32 score 22 stars 1 packages 39 scripts 474 downloads

GD - Geographical Detectors for Assessing Spatial Factors

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

Last updated 1 days ago

geographical-detectorspatial-stratified-heterogeneity

7.24 score 7 stars 50 scripts 2.5k downloads

geocomplexity - Mitigating Spatial Bias Through Geographical Complexity

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the 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 by 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 10 days ago

geospatial-analysisspatial-regressionspatial-relationsspatial-samplingspatial-statistics

6.46 score 16 stars 12 scripts 151 downloads

sdsfun - Spatial Data Science Complementary Features

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

Last updated 2 days ago

spatial-data-analysisspatial-data-science

5.97 score 11 stars 4 packages 6 scripts 465 downloads

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.

Last updated 20 days ago

geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics

5.24 score 5 stars 5 scripts 399 downloads

sesp - Spatially Explicit Stratified Power

Assesses spatial associations between variables through an equivalent geographical detector (q-statistic) within a regression framework and incorporates a spatially explicit stratified power model by integrating spatial dependence and spatial stratified heterogeneity, facilitating the modeling of complex spatial relationships.

Last updated 2 days ago

spatial-explicit-geographical-detectorspatial-stratified-heterogeneity

5.17 score 11 stars 4 scripts

tidyrgeoda - A tidy interface for rgeoda

An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'.

Last updated 3 months ago

geocomputationgeoinformaticsgisciencespatial-analysisspatial-statistics

5.02 score 13 stars 5 scripts

spEcula - Spatial Prediction Methods In R

Advanced spatial prediction methods based on various spatial relationships.

Last updated 5 months ago

geoinformaticsgisciencespatial-analysisspatial-predictionsspatial-statistics

4.90 score 16 stars 6 scripts

geocn - Loads Spatial Data Sets of China

Providing various commonly used spatial data related to Chinese regions in the R programming environment.

Last updated 1 months ago

chinachina-regiongeospatial-visualizationmaps

4.69 score 11 stars 10 scripts

cisp - A Correlation Indicator Based on Spatial Patterns

Use the spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.

Last updated 2 hours ago

associationcorrelationgeoinformaticsspatial-patternspatial-stratified-heterogeneity

3.00 score