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
8.69 score 32 stars 1 dependents 41 scripts 405 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 2 months ago
geographical-detectorspatial-stratified-heterogeneity
7.24 score 7 stars 50 scripts 2.5k 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 7 hours ago
geoinformaticsspatial-data-analysisspatial-data-sciencespatial-statisticsopenblascppopenmp
6.53 score 15 stars 8 dependents 6 scripts 424 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 2 months ago
geographical-complexitygeospatial-analysisspatial-regressionspatial-relationsspatial-samplingspatial-statisticsopenblascppopenmp
6.53 score 19 stars 12 scripts 520 downloads
spEDM - Spatial Empirical Dynamic Modeling
Analyze causality in geospatial data using empirical dynamic modeling (EDM) through geographical convergent cross mapping (GCCM) by Gao et al. (2023) <doi:10.1038/s41467-023-41619-6> and multispatial convergent cross mapping (multispatialCCM) by Clark et al. (2015) <doi:10.1890/14-1479.1>.
Last updated 8 hours ago
causal-inferencecppempirical-dynamic-modelinggeoinformaticsgeospatial-causalityspatial-statisticscpp
5.54 score 10 stars 2 scripts 365 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 9 days ago
spatial-explicit-geographical-detectorspatial-stratified-heterogeneitycpp
5.43 score 15 stars 4 scriptsgeosimilarity - 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 3 months ago
geoinformaticsgeospatial-analyticsspatial-predictionsspatial-statistics
5.24 score 5 stars 5 scripts 229 downloads
tidyrgeoda - A tidy interface for rgeoda
An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'.
Last updated 5 months ago
geocomputationgeoinformaticsgisciencespatial-analysisspatial-statistics
5.08 score 15 stars 5 scripts
spEcula - Spatial Prediction Methods In R
Advanced spatial prediction methods based on various spatial relationships.
Last updated 7 months ago
geoinformaticsgisciencespatial-analysisspatial-predictionsspatial-statistics
4.95 score 18 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
4.85 score 14 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 months ago
associationcorrelationgeoinformaticsspatial-patternspatial-stratified-heterogeneity
4.60 score 2 stars 2 scripts 466 downloads
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 1 months ago
geoinformaticsgeospatial-analysisinformation-theoryspatial-statisticsspatial-stratified-heterogeneitycpp
4.48 score 2 stars 2 scripts 157 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 26 days ago
spatial-econometricsspatial-regressionspatial-statisticsopenblascppopenmp
4.12 score 3 stars 29 scripts 50 downloads
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 26 days ago
cpp
3.30 score 1 starsHSAR - 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 26 days ago
openblascppopenmp
3.16 score 1 stars 29 scripts 50 downloads
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 26 days ago
cpp
3.00 score