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rgeoda (version 0.1.0)

R Library for Spatial Data Analysis

Description

Provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of 'GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The 'GeoDa' software and its documentation are available at .

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Version

Install

install.packages('rgeoda')

Monthly Downloads

2,492

Version

0.1.0

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Repository

https://github.com/geodacenter/rgeoda/

Homepage

https://geodacenter.github.io/rgeoda/

Maintainer

Xun Li

Last Published

March 6th, 2025

Functions in rgeoda (0.1.0)

Empirical Bayes(EB) Rate

gda_rook_weights

(For internally use and test only) Rook Contiguity Spatial Weights

eb_rates_standardization

Empirical Bayes(EB) Rate Standardization

Symmetry of Weights Matrix

gda_min_distthreshold

(For internally use and test only) Minimum Distance Threshold for Distance-based Weights

gda_queen_weights

(For internally use and test only) Queen Contiguity Spatial Weights

(Box) Hinge15 Breaks

(Box) Hinge30 Breaks

K-Nearest Neighbors-based Spatial Weights

Get numbers of neighbors for all observations

Neighbors of one observation

Create an instance of geoda-class by reading from an ESRI Shapefile dataset

Get pseudo-p values of LISA

Bonferroni bound value of local spatial autocorrelation

join_count_ratio

Join Count Ratio

Local Geary Statistics

gda_distance_weights

(For internally use and test only) Distance-based Spatial Weights

Local Getis-Ord's G* Statistics

Isolation/Island in Spatial Weights

Get LISA values

get_neighbors_weights

Weights values of the neighbors of one observation

gda_kernel_knn_weights

(For internally use and test only) K-NN Kernel Spatial Weights

Bivariate Local Moran Statistics

percentile_breaks

Percentile Breaks

A greedy algorithm to solve the max-p-region problem

Maximum Neighbors of Spatial Weights

Get local cluster indicators

quantile_breaks

Quantile Breaks

gda_knn_weights

(For internally use and test only) K-Nearest Neighbors-based Spatial Weights

False Discovery Rate value of local spatial autocorrelation

Distance-based Kernel Spatial Weights

gda_kernel_weights

(For internally use and test only) Distance-based Kernel Spatial Weights

kernel_knn_weights

K-NN Kernel Spatial Weights

Get cluster colors

A simulated annealing algorithm to solve the max-p-region problem

A tabu-search algorithm to solve the max-p-region problem

Set neighbors of an observation

local_quantilelisa

Quantile LISA Statistics

Local Moran Statistics

p_GeoDaWeight-class

Natural Breaks (Jenks)

local_bijoincount

Bivariate Local Join Count Statistics

local_joincount

Local Join Count Statistics

set_neighbors_with_weights

Set neighbors and weights values of an observation

local_multijoincount

(Multivariate) Colocation Local Join Count Statistics

neighbor_match_test

Local Neighbor Match Test

Regionalization with dynamically constrained agglomerative clustering and partitioning

Get cluster labels

Rook Contiguity Spatial Weights

spatial_validation

Spatial Validation

local_multigeary

Local Multivariate Geary Statistics

Update meta data of a spatial weights

min_distthreshold

Minimum Distance Threshold for Distance-based Weights

Local Moran with Empirical Bayes(EB) Rate

Minimum Neighbors of Spatial Weights

Queen Contiguity Spatial Weights

Summary of Spatial Weights

Standard Deviation Breaks

Read a .GAL file

Local Getis-Ord's G Statistics

local_multiquantilelisa

Multivariate Quantile LISA Statistics

Spatial C(K)luster Analysis by Tree Edge Removal

Create an instance of geoda-class from a 'sf' object

weights_sparsity

Sparsity of Spatial Weights

Save Spatial Weights

Spatially Constrained Hierarchical Clucstering (SCHC)

Mean Neighbors of Spatial Weights

median_neighbors

Median Neighbors of Spatial Weights

p_GeoDaTable-class

Read a .GWT file

Read a .SWM file

Create an instance of geoda-class from a 'sp' object

as.data.frame.geoda

convert rgeoda instance to data.frame

A greedy algorithm to solve the AZP problem

A tabu algorithm to solve the AZP problem

as.matrix.Weight

spatial weights to matrix

A simulated annealing algorithm to solve the AZP problem

LISA class (Internally Used)

Create an instance of geoda-class from either an 'sf' or 'sp' object

Create an empty weights

Weight class (Internally Used)

distance_weights

Distance-based Spatial Weights