| Title: | A tidy interface for rgeoda |
|---|---|
| Description: | An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'. |
| Authors: | Wenbo Lv [aut, cre] |
| Maintainer: | Wenbo Lv <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.1 |
| Built: | 2024-11-15 03:06:23 UTC |
| Source: | https://github.com/SpatLyu/tidyrgeoda |
Data of Social Space Quality Score in Guangzhou Metropolitan Areas of China (2010). The actual
representation of each column is as follows:
SName_EN street name; DName_EN district name;
SSQ_Score total score of social space quality; PS_Score population stability score;
EL_Score educational level score; OH_Score occupational hierarchy score;
IL_Score income level score; FPOP_Pro proportion of foreign population;
TenantsPro proportion of tenants; NoSchPro proportion of no schooling;
PSchPRO proportion of primary school education; JHSchPro proportion of junior high school education;
HSchDipPro proportion of high school diploma; CDegreePro proportion of college degree;
UnderG_Pro proportion of undergraduate; PostG_Pro proportion of postgraduate;
RPSOPMOPro proportion of responsible persons of state organs, party and mass organizations and institutions;
PCE_Pro proportion of person in charge of enterprise; ProTechPro proportion of professional and technical personnel;
ClerkPro proportion of clerk; BusSer_Pro proportion of business and service personnel;
AFAFP_Pro proportion of agriculture, forestry, animal husbandry and fishery personnel;
OPTE_Pro proportion of operators of production and transportation equipment;
UnemPeoPro proportion of households with unemployed people;
B100_Pro proportion of households with below 100 yuan;
100_200Pro proportion of households with 100-200yuan;
200_500Pro proportion of households with 200-500yuan;
500_1000P proportion of households with 500-1000yuan;
1000_1500P proportion of households with 1000-1500yuan;
1500_2000P proportion of households with 1500-2000yuan;
2000_3000P proportion of households with 2000-3000yuan;
A3000_Pro proportion of households with above 3000yuan;
The subdistrict boundary is drawn by with reference to the Atlas of Community Network ResponsibilityDistrict of Urban Management Division in Guangzhou.
gzmagzma
gzma: An sf tibble of social space quality score in guangzhou metropolitan
areas(2010) with 118 rows and 32 variables, where the last column is geometry.
WANG Yang, ZHANG Hong’ou, YE Yuyao, WU Qitao, JIN Lixia. Comprehensive Evaluation and Distribution Pattern of Social Space Quality in Guangzhou, China. Tropical Geography.
Create a spatial weights object from a geoda file
read_geoda(file_path, id_vec = NULL)read_geoda(file_path, id_vec = NULL)
file_path |
The file paht of the geoda file. |
id_vec |
(optional),the id_vec is the id values used in the geoda file. |
A weights object
Wenbo Lv [email protected]
Provide ggplot2 fill scales like geoda software.Now it achieve by using
?ggplot2::scale_fill_manual().Another achieve can see
https://stackoverflow.com/questions/43440068/ggplot2-fix-colors-to-factor-l.
scale_fill_lisa(name = "LISA", ...)scale_fill_lisa(name = "LISA", ...)
name |
The name of the LISA fill scales legend,default is |
... |
Adjust other legend details for the LISA fill scales, like |
Wenbo Lv [email protected]
library(sf) library(ggplot2) guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = read_sf(guerry_path) guerry |> dplyr::mutate(lisa = st_local_moran(guerry,'Crm_prs')) |> dplyr::select(lisa) |> ggplot() + geom_sf(aes(fill = lisa),lwd = .1,color = 'grey') + scale_fill_lisa()library(sf) library(ggplot2) guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = read_sf(guerry_path) guerry |> dplyr::mutate(lisa = st_local_moran(guerry,'Crm_prs')) |> dplyr::select(lisa) |> ggplot() + geom_sf(aes(fill = lisa),lwd = .1,color = 'grey') + scale_fill_lisa()
A wrapper function for rgeoda::azp_greedy().The automatic zoning procedure (AZP) was
initially outlined in Openshaw (1977) as a way to address some of the consequences of
the modifiable areal unit problem (MAUP). In essence, it consists of a heuristic to
find the best set of combinations of contiguous spatial units into p regions, minimizing
the within sum of squares as a criterion of homogeneity. The number of regions needs to
be specified beforehand.
st_azp_greedy( sfj, varcol, k, wt = NULL, boundvar = NULL, min_bound = 0, inits = 0, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, rdist = numeric() )st_azp_greedy( sfj, varcol, k, wt = NULL, boundvar = NULL, min_bound = 0, inits = 0, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
The number of clusters. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
(optional) A data frame / tibble with selected bound variable. |
min_bound |
(optional) A minimum bound value that applies to all clusters. |
inits |
(optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location". |
initial_regions |
(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" |
seed |
(optional) The seed for random number generator. Defaults to 123456789. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_azp_greedy(guerry,c('Crm_prs','Crm_prp','Litercy', 'Donatns','Infants','Suicids'),5) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_azp_greedy(guerry,c('Crm_prs','Crm_prp','Litercy', 'Donatns','Infants','Suicids'),5) guerry_clusters
A wrapper function for rgeoda::azp_sa().The automatic zoning procedure (AZP) was
initially outlined in Openshaw (1977) as a way to address some of the consequences of
the modifiable areal unit problem (MAUP). In essence, it consists of a heuristic to
find the best set of combinations of contiguous spatial units into p regions, minimizing
the within sum of squares as a criterion of homogeneity. The number of regions needs to
be specified beforehand.
st_azp_sa( sfj, varcol, k, wt = NULL, boundvar = NULL, cooling_rate = 0.85, sa_maxit = 1, min_bound = 0, inits = 0, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, rdist = numeric() )st_azp_sa( sfj, varcol, k, wt = NULL, boundvar = NULL, cooling_rate = 0.85, sa_maxit = 1, min_bound = 0, inits = 0, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
The number of clusters. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
(optional) A data frame / tibble with selected bound variable. |
cooling_rate |
(optional) The cooling rate of a simulated annealing algorithm. Defaults to 0.85. |
sa_maxit |
(optional) The number of iterations of simulated annealing. Defaults to 1. |
min_bound |
(optional) A minimum bound value that applies to all clusters. |
inits |
(optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location". |
initial_regions |
(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" |
seed |
(optional) The seed for random number generator. Defaults to 123456789. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_azp_sa(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),5) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_azp_sa(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),5) guerry_clusters
A wrapper function for rgeoda::azp_tabu().The automatic zoning procedure (AZP) was
initially outlined in Openshaw (1977) as a way to address some of the consequences of
the modifiable areal unit problem (MAUP). In essence, it consists of a heuristic to
find the best set of combinations of contiguous spatial units into p regions, minimizing
the within sum of squares as a criterion of homogeneity. The number of regions needs to
be specified beforehand.
st_azp_tabu( sfj, varcol, k, wt = NULL, boundvar = NULL, tabu_length = 10, conv_tabu = 10, min_bound = 0, inits = 0, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, rdist = numeric() )st_azp_tabu( sfj, varcol, k, wt = NULL, boundvar = NULL, tabu_length = 10, conv_tabu = 10, min_bound = 0, inits = 0, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
The number of clusters. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
(optional) A data frame / tibble with selected bound variable. |
tabu_length |
(optional) The length of a tabu search heuristic of tabu algorithm. Defaults to 10. |
conv_tabu |
(optional): The number of non-improving moves. Defaults to 10. |
min_bound |
(optional) A minimum bound value that applies to all clusters. |
inits |
(optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location". |
initial_regions |
(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" |
seed |
(optional) The seed for random number generator. Defaults to 123456789. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_azp_tabu(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),5) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_azp_tabu(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),5) guerry_clusters
Univariate Spatial Stratification by invoking rgeoda's *_breaks function.
st_breaks(sfj, varcol, break_method = "stddev", k = 6)st_breaks(sfj, varcol, break_method = "stddev", k = 6)
sfj |
An sf, tibble or data.frame object |
varcol |
The variables selected to run univariate spatial stratification. |
break_method |
(optional) Which has to be one of "stddev"(default), "hinge15",
"hinge30", "percentile", "natural", "quantile". When the |
k |
(optional)A numeric value indicates how many breaks,default is 6. |
A vector of numeric values of computed breaks
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) st_breaks(guerry,'Crm_prs',break_method = "quantile", k = 5)library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) st_breaks(guerry,'Crm_prs',break_method = "quantile", k = 5)
Create a contiguity spatial weights with options of "queen", "order", "include lower order" and "precision threshold"
st_contiguity_weights( sfj, queen = TRUE, order = 1, include_lower_order = FALSE, precision_threshold = 0 )st_contiguity_weights( sfj, queen = TRUE, order = 1, include_lower_order = FALSE, precision_threshold = 0 )
sfj |
An sf (simple feature) object. |
queen |
(Optional) TRUE (default) or FALSE, TRUE implements Queen Contiguity and FALSE implements Rook Contiguity. |
order |
(Optional) Order of contiguity, default is 1. |
include_lower_order |
(Optional) Whether or not the lower order neighbors should be included in the weights structure,default is False. |
precision_threshold |
(Optional) The precision of the underlying shape file is insufficient to allow for an exact match of coordinates to determine which polygons are neighbors,default is 0. |
An instance of rgeoda Weight-class.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) queenw = st_contiguity_weights(guerry,queen = TRUE)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) queenw = st_contiguity_weights(guerry,queen = TRUE)
Create a distance-based weights
st_distance_weights( sfj, unit = "km", dist_thres = NULL, power = 1, is_inverse = FALSE )st_distance_weights( sfj, unit = "km", dist_thres = NULL, power = 1, is_inverse = FALSE )
sfj |
An sf (simple feature) object. |
unit |
(optional) The unit for calculating spatial distance, can be 'km'(default) or 'mile'. |
dist_thres |
(optional) A positive numeric value of distance threshold. |
power |
(optional) The power (or exponent) of a number indicates how many times to use the number in a multiplication.Default is 1. |
is_inverse |
(optional) FALSE (default) or TRUE, apply inverse on distance value. |
An instance of rgeoda Weight-class.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_distance_weights(guerry)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_distance_weights(guerry)
Create a kernel weights by specifying k-nearest neighbors and a kernel method
st_kernel_knn_weights( sfj, k, kernel = "gaussian", power = 1, adaptive_bandwidth = TRUE, use_kernel_diagonals = FALSE, is_inverse = FALSE, unit = "km" )st_kernel_knn_weights( sfj, k, kernel = "gaussian", power = 1, adaptive_bandwidth = TRUE, use_kernel_diagonals = FALSE, is_inverse = FALSE, unit = "km" )
sfj |
An sf (simple feature) object. |
k |
A positive integer number for k-nearest neighbors. |
kernel |
(optional) A string value, which has to be one of 'triangular', 'uniform', 'epanechnikov', 'quartic', 'gaussian'(default). |
power |
(optional) The power (or exponent) of a number indicates how many times to use the number in a multiplication.Default is 1. |
adaptive_bandwidth |
(optional) TRUE (default) or FALSE: TRUE use adaptive bandwidth calculated using distance of k-nearest neithbors, FALSE use max distance of all observation to their k-nearest neighbors. |
use_kernel_diagonals |
(optional) FALSE (default) or TRUE, apply kernel on the diagonal of weights matrix. |
is_inverse |
(optional) FALSE (default) or TRUE, apply inverse on distance value. |
unit |
(optional) The unit for calculating spatial distance, can be 'km'(default) or 'mile'. |
An instance of rgeoda Weight-class.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_kernel_knn_weights(guerry,6)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_kernel_knn_weights(guerry,6)
Create a kernel weights by specifying a bandwidth and a kernel method
st_kernel_weights( sfj, kernel = "gaussian", bandwidth = NULL, power = 1, use_kernel_diagonals = FALSE, is_inverse = FALSE, unit = "km" )st_kernel_weights( sfj, kernel = "gaussian", bandwidth = NULL, power = 1, use_kernel_diagonals = FALSE, is_inverse = FALSE, unit = "km" )
sfj |
An sf (simple feature) object. |
kernel |
(optional) A string value, which has to be one of 'triangular', 'uniform', 'epanechnikov', 'quartic', 'gaussian'(default). |
bandwidth |
(optional) A positive numeric value of bandwidth. |
power |
(optional) The power (or exponent) of a number indicates how many times to use the number in a multiplication.Default is 1. |
use_kernel_diagonals |
(optional) FALSE (default) or TRUE, apply kernel on the diagonal of weights matrix. |
is_inverse |
(optional) FALSE (default) or TRUE, apply inverse on distance value. |
unit |
(optional) The unit for calculating spatial distance, can be 'km'(default) or 'mile'. |
An instance of rgeoda Weight-class.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_kernel_weights(guerry)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_kernel_weights(guerry)
Create a k-nearest neighbors based spatial weights
st_knn_weights(sfj, k, power = 1, is_inverse = FALSE, unit = "km")st_knn_weights(sfj, k, power = 1, is_inverse = FALSE, unit = "km")
sfj |
An sf (simple feature) object. |
k |
A positive integer number for k-nearest neighbors. |
power |
(optional) The power (or exponent) of a number indicates how many times to use the number in a multiplication.Default is 1. |
is_inverse |
(optional) FALSE (default) or TRUE, apply inverse on distance value. |
unit |
(optional) The unit for calculating spatial distance, can be 'km'(default) or 'mile'. |
An instance of rgeoda Weight-class.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_knn_weights(guerry,3)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_knn_weights(guerry,3)
Compute the spatial lag for idx-th observation using selected variable and spatial weights matrix
st_lag(sfj, varcol, wt = NULL)st_lag(sfj, varcol, wt = NULL)
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
A numeric vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_lag(guerry,'Pop1831')guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_lag(guerry,'Pop1831')
The local neighbor match test is to assess the extent of overlap between k-nearest neighbors in geographical space and k-nearest neighbors in multi-attribute space.
st_lnmt( sfj, varcol, k, unit = "km", scale_method = "standardize", distance_method = "euclidean", power = 1, is_inverse = FALSE )st_lnmt( sfj, varcol, k, unit = "km", scale_method = "standardize", distance_method = "euclidean", power = 1, is_inverse = FALSE )
sfj |
An sf (simple feature) object. |
varcol |
The variables selected to run local neighbor match test. |
k |
A positive integer number for k-nearest neighbors searching. |
unit |
(optional) The unit for calculating spatial distance, can be 'km'(default) or 'mile'. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The type of distance metrics used to measure the distance between input data. Options are 'euclidean', 'manhattan'. Default is 'euclidean'. |
power |
(optional) The power (or exponent) of a number says how many times to use the number in a multiplication. |
is_inverse |
(optional) FALSE (default) or TRUE, apply inverse on distance value. |
A tibble with two columns "Cardinality" and "Probability".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata","Guerry.shp",package = "rgeoda")) st_lnmt(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),6)library(sf) guerry = read_sf(system.file("extdata","Guerry.shp",package = "rgeoda")) st_lnmt(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),6)
Function to apply local Bivariate Join Count statistics
st_local_bijoincount( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_bijoincount( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
library(magrittr) guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) %>% dplyr::mutate(InvCrm = 1 - TopCrm) st_local_bijoincount(guerry,c("TopCrm", "InvCrm"))library(magrittr) guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) %>% dplyr::mutate(InvCrm = 1 - TopCrm) st_local_bijoincount(guerry,c("TopCrm", "InvCrm"))
Function to apply bivariate local Moran statistics
st_local_bimoran( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_bimoran( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_bimoran(guerry,c('Crm_prs','Litercy'))guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_bimoran(guerry,c('Crm_prs','Litercy'))
Function to apply Getis-Ord's local G statistics
st_local_g( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_g( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_g(guerry,'Crm_prp')guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_g(guerry,'Crm_prp')
Function to apply local Geary statistics
st_local_geary( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_geary( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_geary(guerry,'Crm_prp')guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_geary(guerry,'Crm_prp')
Function to apply Getis-Ord's local G*statistics
st_local_gstar( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_gstar( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_gstar(guerry,'Crm_prp')guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_gstar(guerry,'Crm_prp')
Function to apply local Join Count statistics
st_local_joincount( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_joincount( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_joincount(guerry,'Crm_prp')guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_joincount(guerry,'Crm_prp')
Function to apply local Moran statistics
st_local_moran( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_moran( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_moran(guerry,'Crm_prp')guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_moran(guerry,'Crm_prp')
Function to apply local Moran with EB Rate statistics. The EB rate is first computed from "event" and "base" variables, and then used in local moran statistics.
st_local_moran_eb( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_moran_eb( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
## Not run: guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_moran_eb(guerry,c("hr60", "po60")) ## End(Not run)## Not run: guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_moran_eb(guerry,c("hr60", "po60")) ## End(Not run)
Function to apply local Multivariate Geary statistics
st_local_multigeary( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_multigeary( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variables selected to calculate spatial lag, which is a character vector. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_multigeary(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants', 'Suicids'))guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_multigeary(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants', 'Suicids'))
Function to apply (multivariate) colocation local Join Count statistics
st_local_multijoincount( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_multijoincount( sfj, varcol, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_multijoincount(guerry,c('TopWealth','TopWealth', 'TopLit'))guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_multijoincount(guerry,c('TopWealth','TopWealth', 'TopLit'))
Function to apply multivariate quantile LISA statistics
st_local_multiquantilelisa( sfj, varcol, k, q, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_multiquantilelisa( sfj, varcol, k, q, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
A value indicates the number of quantiles. Value range e.g. |
q |
A value indicates which quantile or interval used in local join count statistics. Value stars from 1. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_multiquantilelisa(guerry,c("Crm_prp", "Litercy"),c(4,4), c(1,1))guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_multiquantilelisa(guerry,c("Crm_prp", "Litercy"),c(4,4), c(1,1))
Function to apply quantile LISA statistics
st_local_quantilelisa( sfj, varcol, k, q, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )st_local_quantilelisa( sfj, varcol, k, q, wt = NULL, permutations = 999, permutation_method = "complete", significance_cutoff = 0.05, cpu_threads = 6, seed = 123456789 )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
A value indicates the number of quantiles. Value range e.g. |
q |
A value indicates which quantile or interval used in local join count statistics. Value stars from 1. |
wt |
(optional) The spatial weights object,which can use |
permutations |
(optional) The number of permutations for the LISA computation. |
permutation_method |
(optional) The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default is 'complete'. |
significance_cutoff |
(optional) A cutoff value for significance p-values to filter not-significant clusters. |
cpu_threads |
(optional) The number of cpu threads used for parallel LISA computation. |
seed |
(optional) The seed for random number generator. |
A factor vector.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_quantilelisa(guerry,"Crm_prs",k = 4, q = 1)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_local_quantilelisa(guerry,"Crm_prs",k = 4, q = 1)
A wrapper function for rgeoda::maxp_greedy().The max-p-region problem is a special case
of constrained clustering where a finite number of geographical areas are aggregated into
the maximum number of regions (max-p-regions), such that each region is geographically
connected and the clusters could maximize internal homogeneity.
st_maxp_greedy( sfj, varcol, wt = NULL, boundvar, min_bound, iterations = 99, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )st_maxp_greedy( sfj, varcol, wt = NULL, boundvar, min_bound, iterations = 99, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
A numeric vector of selected bounding variable. |
min_bound |
A minimum value that the sum value of bounding variable int each cluster should be greater than. |
iterations |
(optional) The number of iterations of greedy algorithm. Defaults to 99. |
initial_regions |
(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" |
seed |
(optional) The seed for random number generator. Defaults to 123456789. |
cpu_threads |
(optional) The number of cpu threads used for parallel computation.Default is 6. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_maxp_greedy(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_maxp_greedy(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67) guerry_clusters
A wrapper function for rgeoda::maxp_sa().The max-p-region problem is a special case
of constrained clustering where a finite number of geographical areas are aggregated into
the maximum number of regions (max-p-regions), such that each region is geographically
connected and the clusters could maximize internal homogeneity.
st_maxp_sa( sfj, varcol, wt = NULL, boundvar, min_bound, cooling_rate = 0.85, sa_maxit = 1, iterations = 99, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )st_maxp_sa( sfj, varcol, wt = NULL, boundvar, min_bound, cooling_rate = 0.85, sa_maxit = 1, iterations = 99, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
A numeric vector of selected bounding variable. |
min_bound |
A minimum value that the sum value of bounding variable int each cluster should be greater than. |
cooling_rate |
(optional) The cooling rate of a simulated annealing algorithm. Defaults to 0.85. |
sa_maxit |
(optional) The number of iterations of simulated annealing. Defaults to 1. |
iterations |
(optional) The number of iterations of greedy algorithm. Defaults to 99. |
initial_regions |
(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" |
seed |
(optional) The seed for random number generator. Defaults to 123456789. |
cpu_threads |
(optional) The number of cpu threads used for parallel computation.Default is 6. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_maxp_sa(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_maxp_sa(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67) guerry_clusters
A wrapper function for rgeoda::maxp_tabu().The max-p-region problem is a special case
of constrained clustering where a finite number of geographical areas are aggregated into
the maximum number of regions (max-p-regions), such that each region is geographically
connected and the clusters could maximize internal homogeneity.
st_maxp_tabu( sfj, varcol, wt = NULL, boundvar, min_bound, tabu_length = 10, conv_tabu = 10, iterations = 99, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )st_maxp_tabu( sfj, varcol, wt = NULL, boundvar, min_bound, tabu_length = 10, conv_tabu = 10, iterations = 99, initial_regions = vector("numeric"), scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
A numeric vector of selected bounding variable. |
min_bound |
A minimum value that the sum value of bounding variable int each cluster should be greater than. |
tabu_length |
(optional) The length of a tabu search heuristic of tabu algorithm. Defaults to 10. |
conv_tabu |
(optional): The number of non-improving moves. Defaults to 10. |
iterations |
(optional) The number of iterations of greedy algorithm. Defaults to 99. |
initial_regions |
(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan" |
seed |
(optional) The seed for random number generator. Defaults to 123456789. |
cpu_threads |
(optional) The number of cpu threads used for parallel computation.Default is 6. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_maxp_tabu(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_maxp_tabu(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns', 'Infants','Suicids'),boundvar = 'Pop1831',min_bound = 3236.67) guerry_clusters
A wrapper function for rgeoda::redcap().REDCAP (Regionalization with dynamically constrained agglomerative clustering and partitioning)
is developed by D. Guo (2008). Like SKATER, REDCAP starts from building a spanning tree with 4 different ways (single-linkage, average-linkage,
ward-linkage and the complete-linkage). The single-linkage way leads to build a minimum spanning tree. Then,REDCAP provides 2 different ways
(first-order and full-order constraining) to prune the tree to find clusters. The first-order approach with a minimum spanning tree is exactly
the same with SKATER. In GeoDa and pygeoda, the following methods are provided: \* First-order and Single-linkage \* Full-order and Complete-linkage
\* Full-order and Average-linkage \* Full-order and Single-linkage \* Full-order and Ward-linkage.
st_redcap( sfj, varcol, k, wt = NULL, boundvar = NULL, method = "fullorder-averagelinkage", min_bound = 0, scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )st_redcap( sfj, varcol, k, wt = NULL, boundvar = NULL, method = "fullorder-averagelinkage", min_bound = 0, scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
The number of clusters. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
(optional) A data frame / tibble with selected bound variable. |
method |
(optional) "firstorder-singlelinkage", "fullorder-completelinkage", "fullorder-averagelinkage"(default),"fullorder-singlelinkage", "fullorder-wardlinkage" |
min_bound |
(optional) A minimum bound value that applies to all clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance between observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan". |
seed |
(int,optional) The seed for random number generator. Defaults to 123456789. |
cpu_threads |
(optional) The number of cpu threads used for parallel computation. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_redcap(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'), 4,method = "fullorder-completelinkage") guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_redcap(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'), 4,method = "fullorder-completelinkage") guerry_clusters
A wrapper function for rgeoda::schc().Spatially constrained hierarchical clustering is
a special form of constrained clustering, where the constraint is based on contiguity (common borders).
The method builds up the clusters using agglomerative hierarchical clustering methods: single linkage,
complete linkage, average linkage and Ward's method (a special form of centroid linkage). Meanwhile,
it also maintains the spatial contiguity when merging two clusters.
st_schc( sfj, varcol, k, wt = NULL, boundvar = NULL, method = "average", min_bound = 0, scale_method = "standardize", distance_method = "euclidean", rdist = numeric() )st_schc( sfj, varcol, k, wt = NULL, boundvar = NULL, method = "average", min_bound = 0, scale_method = "standardize", distance_method = "euclidean", rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
The number of clusters. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
(optional) A data frame / tibble with selected bound variable. |
method |
(optional) "single", "complete", "average"(default),"ward". |
min_bound |
(optional) A minimum bound value that applies to all clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance between observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan". |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_schc(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'), 4,method = "complete") guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_schc(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'), 4,method = "complete") guerry_clusters
A wrapper function for rgeoda::skater().SKATER forms clusters by spatially
partitioning data that has similar values for features of interest.
st_skater( sfj, varcol, k, wt = NULL, boundvar = NULL, min_bound = 0, scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )st_skater( sfj, varcol, k, wt = NULL, boundvar = NULL, min_bound = 0, scale_method = "standardize", distance_method = "euclidean", seed = 123456789, cpu_threads = 6, rdist = numeric() )
sfj |
An sf (simple feature) object. |
varcol |
The variable selected to calculate spatial lag, which is a character. |
k |
The number of clusters. |
wt |
(optional) The spatial weights object,which can use |
boundvar |
(optional) A data frame / tibble with selected bound variable. |
min_bound |
(optional) A minimum bound value that applies to all clusters. |
scale_method |
(optional) One of the scaling methods 'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust' to apply on input data. Default is 'standardize' (Z-score normalization). |
distance_method |
(optional) The distance method used to compute the distance between observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan". |
seed |
(int,optional) The seed for random number generator. Defaults to 123456789. |
cpu_threads |
(optional) The number of cpu threads used for parallel computation. |
rdist |
(optional) The distance matrix (lower triangular matrix, column wise storage). |
A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
Wenbo Lv [email protected]
library(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_skater(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'),4) guerry_clusterslibrary(sf) guerry = read_sf(system.file("extdata", "Guerry.shp", package = "rgeoda")) guerry_clusters = st_skater(guerry,c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids'),4) guerry_clusters
Warpping the summary() function for spatial weights
st_summary(wt, ...)st_summary(wt, ...)
wt |
A Weight object |
... |
summary optional parameters |
A summary description of an instance of Weight-class
Wenbo Lv [email protected]
## Not run: library(sf) guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = read_sf(guerry_path) queen_w = tidyrgeoda::st_weights(guerry,'contiguity') st_summary(queen_w) ## End(Not run)## Not run: library(sf) guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = read_sf(guerry_path) queen_w = tidyrgeoda::st_weights(guerry,'contiguity') st_summary(queen_w) ## End(Not run)
Create a spatial weights
st_weights(sfj, weight = NULL, ...)st_weights(sfj, weight = NULL, ...)
sfj |
An sf (simple feature) object. |
weight |
The method used to create spatial weights,which has to be one of 'contiguity', 'distance', 'knn', 'kernel', 'kernel_knn'. |
... |
Other arguments to construct spatial weight, see 'tidyrgeoda::st_contiguity_weights','tidyrgeoda::st_distance_weights', 'tidyrgeoda::st_knn_weights','tidyrgeoda::st_kernel_weights', 'tidyrgeoda::st_kernel_knn_weights'. |
An instance of rgeoda Weight-class.
Wenbo Lv [email protected]
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_weights(guerry,'kernel_knn',6)guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda") guerry = sf::read_sf(guerry_path) st_weights(guerry,'kernel_knn',6)
Save spatial weights to a file
write_geoda(wt, dsn, id_vec = NULL, layer = NULL)write_geoda(wt, dsn, id_vec = NULL, layer = NULL)
wt |
A Weight object |
dsn |
The path of an output weights file |
id_vec |
(optional) Defines the unique value of each observation when saving a
weights file. Default is |
layer |
(optional) The name of the layer of input dataset,default is |
A boolean value indicates if save successfully or failed
Wenbo Lv [email protected]