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-statisticsspatial-stratified-heterogeneitycpp
9.08 score 32 stars 2 dependents 41 scripts 423 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 4 months ago
geographical-detectorspatial-stratified-heterogeneity
7.74 score 13 stars 51 scripts 4.1k 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 23 days ago
geoinformaticsspatial-data-analysisspatial-data-sciencespatial-statisticsopenblascppopenmp
6.58 score 16 stars 8 dependents 6 scripts 620 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 4 months ago
geographical-complexitygeospatial-analysisspatial-regressionspatial-relationsspatial-samplingspatial-statisticsopenblascppopenmp
6.53 score 19 stars 12 scripts 586 downloads
spEDM - Spatial Empirical Dynamic Modeling
Inferring causal associations in cross-sectional earth system data through empirical dynamic modeling (EDM), with extensions to convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023)<doi:10.1016/j.fmre.2023.01.007>.
Last updated 2 days ago
causal-inferencecppempirical-dynamic-modelinggeoinformaticsgeospatial-causalityspatial-statisticsopenblascppopenmp
6.13 score 17 stars 2 scripts 235 downloadsHSAR - Hierarchical Spatial Autoregressive Model
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
Last updated 3 months ago
spatial-econometricsspatial-regressionspatial-statisticsopenblascppopenmp
5.56 score 8 stars 30 scripts 208 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 3 months ago
spatial-explicit-geographical-detectorspatial-stratified-heterogeneitycpp
5.43 score 15 starsgeosimilarity - 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 2 months ago
geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics
5.38 score 6 stars 5 scripts 334 downloads
tidyrgeoda - A tidy interface for rgeoda
An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'.
Last updated 7 months ago
geocomputationgeoinformaticsgisciencespatial-analysisspatial-statistics
5.11 score 16 stars 5 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 months ago
associationcorrelationgeoinformaticsspatial-patterns
5.10 score 5 stars 2 scripts 409 downloads
spEcula - Spatial Prediction Methods In R
Advanced spatial prediction methods based on various spatial relationships.
Last updated 9 months ago
geoinformaticsgisciencespatial-analysisspatial-predictionsspatial-statistics
5.02 score 21 stars 6 scripts
itmsa - Information-Theoretic Measures for Spatial Association
Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).
Last updated 3 months ago
cpp
5.00 score 5 stars 125 downloads
geocn - Loads Spatial Data Sets of China
Providing various commonly used spatial data related to Chinese regions in the R programming environment.
Last updated 4 months ago
chinachina-regiongeospatial-visualizationmaps
4.93 score 17 stars 10 scripts
sshicm - Information Consistency-Based Measures for Spatial Stratified Heterogeneity
Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).
Last updated 3 months ago
geoinformaticsgeospatial-analysisinformation-theoryspatial-statisticsspatial-stratified-heterogeneitycpp
4.65 score 3 stars 2 scripts 136 downloads
localsp - Local Indicator of Stratified Power
Implements a local indicator of stratified power to analyze local spatial stratified association and demonstrate how spatial stratified association changes spatially and in local regions, as outlined in Hu et al. (2024) <doi:10.1080/13658816.2024.2437811>.
Last updated 2 months ago
4.60 score 2 stars 218 downloadsSecDim - 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 7 months ago
spatial-associationspatial-predictions
2.70 score 1 stars 2 scripts 72 downloads