LISA class (Internally Used)

Description

A LISA-class that wrappers the statistics of LISA computation

Fields

gda_lisa

An object of GeoDaLISA

p_vals

The pseudo-p values of significance of LISA computation

c_vals

The cluster indicators of LISA computation

lisa_vals

The local spatial autocorrelation values of LISA computation

nn_vals

The number of neighbors of every observations in LISA computation

labels

The cluster labels of LISA

colors

The cluster colors (HEX format) of LISA

Methods

GetBO(current_p)

Get the Bonferroni bound value

GetClusterIndicators()

Get the local cluster indicators returned from LISA computation.

GetColors()

Get the cluster colors of LISA computation.

GetFDR(current_p)

Get the False Discovery Rate value

GetLISAValues()

Get the local spatial autocorrelation values returned from LISA computation.

GetLabels()

Get the cluster labels of LISA computation.

GetLocalSignificanceValues()

Get the local pseudo-p values of significance returned from LISA computation.

GetNumNeighbors()

Get the number of neighbors of every observations in LISA computation.

Run()

Call to run LISA computation

SetPermutations(num_perm)

Set the number of permutations for the LISA computation

SetSignificanceCutoff(cutoff)

Set the cutoff value of significance values

SetThreads(num_threads)

Set the number of CPU threads for the LISA computation

initialize(lisa_obj)

Constructor with a LISA object (internally used)

Weight class (Internally Used)

Description

A wrapper class for p_GeoDaWeight class

Fields

gda_w

An object of p_GeoDaWeight-class

is_symmetric

If weights matrix is symmetric

sparsity

Sparsity of weights matrix

min_neighbors

Minimum number of neighbors

max_neighbors

Maximum number of neighbors

num_obs

Number of observations

mean_neighbors

Mean number of neighbors

median_neighbors

Median number of neighbors

has_isolates

If the weights matrix has any isolates

Methods

GetNeighborWeights(idx)

Get weights values of neighbors for idx-th observation, idx starts from 0

GetNeighbors(idx)

Get neighbors for idx-th observation, idx starts from 0

GetPointer()

Get the C++ object pointer (internally used)

GetSparsity()

Get sparsity computed from weights matrix

HasIsolates()

Check if weights matrix has isolates, or if any observation has no neighbors

IsSymmetric()

Check if weights matrix is symmetric

SaveToFile(out_path, layer_name, id_name, id_values)

Save current spatial weights to a file.

out_path: The path of an output weights file
layer_name : The name of the layer of input dataset
id_name : The id name (or field name), which is an associated column contains unique values, that makes sure that the weights are connected to the correct observations in the data table.
id_values : The tuple of values of selected id_name (column/field)

SetNeighbors(idx, nbrs)

Set neighbors for one observation

SetNeighborsAndWeights(idx, nbrs, nbr_w)

Set neighbors with weights values for one observation

SpatialLag(values)

Compute spatial lag values for values of selected variable

Update(updateStats = TRUE)

Update the weights meta data

initialize(o_gda_w)

Constructor with a GeoDaWeight object (internally used)

convert rgeoda instance to data.frame

Description

Override the as.data.frame function for rgeoda instance

Usage

## S3 method for class 'geoda'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

A data.frame object

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

Description

Create an instance of geoda-class from an 'sf' object returned from 'st_read()' function, or a 'sp' object returned from 'readOGR()' function. NOTE: The table content is NOT used to create an instance of geoda-class.

Usage

as.geoda(obj, with_table = TRUE)

Arguments

Value

An instance of geoda-class

spatial weights to matrix

Description

Convert a GeoDa spatial weights object to a Matrix object

Usage

## S3 method for class 'Weight'
as.matrix(x, rownames = NULL, rownames.value = NULL, ...)

Arguments

Value

A matrix object

A greedy algorithm to solve the AZP problem

Description

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.

Usage

azp_greedy(
  p,
  w,
  df,
  bound_variable = data.frame(),
  min_bound = 0,
  inits = 0,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
azp_clusters <- azp_greedy(5, queen_w, data)
azp_clusters

## End(Not run)

A simulated annealing algorithm to solve the AZP problem

Description

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.

Usage

azp_sa(
  p,
  w,
  df,
  cooling_rate,
  sa_maxit = 1,
  bound_variable = data.frame(),
  min_bound = 0,
  inits = 0,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
azp_clusters <- azp_sa(5, queen_w, data, cooling_rate = 0.85)
azp_clusters

## End(Not run)

A tabu algorithm to solve the AZP problem

Description

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.

Usage

azp_tabu(
  p,
  w,
  df,
  tabu_length = 10,
  conv_tabu = 10,
  bound_variable = data.frame(),
  min_bound = 0,
  inits = 0,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
azp_clusters <- azp_tabu(5, queen_w, data, tabu_length=10, conv_tabu=10)
azp_clusters

## End(Not run)

Create an empty weights

Description

Create an empty weights

Usage

create_weights(num_obs)

Arguments

Value

An instance of Weight-class

Distance-based Spatial Weights

Description

Create a distance-based weights

Usage

distance_weights(
  sf_obj,
  dist_thres,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
dist_thres <- min_distthreshold(guerry)
dist_w <- distance_weights(guerry, dist_thres)
summary(dist_w)

Empirical Bayes(EB) Rate

Description

The function to compute EB Rate from an event variable and a base variable.

Usage

eb_rates(df)

Arguments

Value

A data.frame with two columns "EB Rate" and "IsNull".

Examples

## Not run: 
library(sf)
nat <- st_read("natregimes.shp")
ebr <- eb_rates(nat[c("HR60", "PO60")])
ebr

## End(Not run)

Empirical Bayes(EB) Rate Standardization

Description

The function to compute EB Rate Standardization from an event variable and a base variable.

Usage

eb_rates_standardization(df)

Arguments

Value

A data.frame with two columns "EB Rate" and "IsNull".

Examples

## Not run: 
library(sf)
nat <- st_read("natregimes.shp")
ebr <- eb_rates_standardization(nat[c("HR60", "PO60")])
ebr

## End(Not run)

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

Description

Create a distance-based weights

Usage

gda_distance_weights(
  geoda_obj,
  dist_thres,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
dist_thres <- gda_min_distthreshold(guerry)
dist_w <- gda_distance_weights(guerry, dist_thres)
summary(dist_w)

## End(Not run)

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

Description

Create a kernel weights by specifying k-nearest neighbors and a kernel method

Usage

gda_kernel_knn_weights(
  geoda_obj,
  k,
  kernel_method,
  adaptive_bandwidth = TRUE,
  use_kernel_diagonals = FALSE,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
adptkernel_w = gda_kernel_knn_weights(guerry, 6, "uniform")
summary(adptkernel_w)

## End(Not run)

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

Description

Create a kernel weights by specifying a bandwidth and a kernel method

Usage

gda_kernel_weights(
  geoda_obj,
  bandwidth,
  kernel_method,
  use_kernel_diagonals = FALSE,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
bandwidth <- gda_min_distthreshold(guerry)
kernel_w <- gda_kernel_weights(guerry, bandwidth, kernel_method = "uniform")
summary(kernel_w)

## End(Not run)

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

Description

Create a k-nearest neighbors based spatial weights

Usage

gda_knn_weights(
  geoda_obj,
  k,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
knn6_w <- gda_knn_weights(guerry, 6)
summary(knn6_w)

## End(Not run)

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

Description

Get minimum threshold of distance that makes sure each observation has at least one neighbor

Usage

gda_min_distthreshold(geoda_obj, is_arc = FALSE, is_mile = TRUE)

Arguments

Value

A numeric value of minimum threshold of distance

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

Description

Create a Queen contiguity weights with options of "order", "include lower order" and "precision threshold"

Usage

gda_queen_weights(
  geoda_obj,
  order = 1,
  include_lower_order = FALSE,
  precision_threshold = 0
)

Arguments

Value

An instance of Weight-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
queen_w <- gda_queen_weights(guerry)
summary(queen_w)

## End(Not run)

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

Description

Create a Rook contiguity weights with options of "order", "include lower order" and "precision threshold"

Usage

gda_rook_weights(
  geoda_obj,
  order = 1,
  include_lower_order = FALSE,
  precision_threshold = 0
)

Arguments

Value

An instance of Weight-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
rook_w <- gda_rook_weights(guerry)
summary(rook_w)

## End(Not run)

'geoda' class

Description

'geoda' is a RefClass that wraps the C++ GeoDa class (via p_GeoDa defines in rgeoda.R)

Fields

gda

The pointer to the instance of p_GeoDa-class

map_type

The map type, could be either Point or Polygon

n_cols

The number of columns

n_obs

The number of observations

field_names

A string vector of field names

field_types

A string vector of field types (integer, real, string)

Methods

GetFieldNames(...)

Get the field names of all columns

GetFieldTypes(...)

Get the field types (integer, real, string) of all columns

GetIntegerCol(col_name)

Get the integer values from a column

GetMapType(...)

Get the map type

GetNumCols(...)

Get the number of columns

GetNumObs(...)

Get the number of observations

GetPointer()

Get the C++ object pointer (internally used)

GetRealCol(col_name)

Get the real values from a column

GetUndefinedVals(col_name)

Get the undefined flags from a column

initialize(o_gda)

Constructor with a geoda object (internally used)

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

Description

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

Usage

geoda_open(ds_path)

Arguments

Value

An instance of geoda-class

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- geoda_open(guerry_path)
guerry_df <- as.data.frame(guerry) # access as a data.frame
head(guerry_df)

## End(Not run)

Neighbors of one observation

Description

Get neighbors for idx-th observation, idx starts from 1

Usage

get_neighbors(gda_w, idx)

Arguments

Value

A numeric vector of the neighbor indices, which start from 1

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
nbrs <- get_neighbors(queen_w, idx = 1)
cat("\nNeighbors of the 1-st observation are:", nbrs)

## End(Not run)

Weights values of the neighbors of one observation

Description

Get the associated weights values of neighbors for idx-th observation

Usage

get_neighbors_weights(gda_w, idx)

Arguments

Value

A numeric vector of the neighbor indices, which start from 1

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
nbrs <- get_neighbors_weights(queen_w, idx = 1)
cat("\nNeighbors of the 1-st observation are:", nbrs)

## End(Not run)

Isolation/Island in Spatial Weights

Description

Check if weights matrix has isolates, or if any observation has no neighbors

Usage

has_isolates(gda_w)

Arguments

Value

A boolean value indicates if weights matrix is symmetric

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
has_isolates(queen_w)

## End(Not run)

(Box) Hinge15 Breaks

Description

Hinge15 breaks data into 6 groups like box plot groups (Lower outlier, < 25

Usage

hinge15_breaks(df)

Arguments

Value

A vector of numeric values of computed breaks

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
hinge15_breaks(guerry['Crm_prs'])

(Box) Hinge30 Breaks

Description

Hinge30 breaks data into 6 groups like box plot groups (Lower outlier, < 25

Usage

hinge30_breaks(df)

Arguments

Value

A vector of numeric values of computed breaks

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
hinge30_breaks(guerry['Crm_prs'])

Symmetry of Weights Matrix

Description

Check if weights matrix is symmetric

Usage

is_symmetric(gda_w)

Arguments

Value

A boolean value indicates if weights matrix is symmetric

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
is_symmetric(queen_w)

## End(Not run)

Join Count Ratio

Description

Join count ratio is the join counts, the number of times a category is surrounded by neighbors of the same category, over the total number of neighbors after converting each category to a dummy variable.

Usage

join_count_ratio(clusters, w)

Arguments

Value

A data.frame with names "Cluster", "N", "Neighbors", "Join Count", "Ratio"

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
clusters <- skater(5, queen_w, data)
results <- join_count_ratio(clusters, queen_w)
results

## End(Not run)

K-NN Kernel Spatial Weights

Description

Create a kernel weights by specifying k-nearest neighbors and a kernel method

Usage

kernel_knn_weights(
  sf_obj,
  k,
  kernel_method,
  adaptive_bandwidth = TRUE,
  use_kernel_diagonals = FALSE,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
adptkernel_w = kernel_knn_weights(guerry, 6, "uniform")
summary(adptkernel_w)

Distance-based Kernel Spatial Weights

Description

Create a kernel weights by specifying a bandwidth and a kernel method

Usage

kernel_weights(
  sf_obj,
  bandwidth,
  kernel_method,
  use_kernel_diagonals = FALSE,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
bandwidth <- min_distthreshold(guerry)
kernel_w <- kernel_weights(guerry, bandwidth, kernel_method = "uniform")
summary(kernel_w)

K-Nearest Neighbors-based Spatial Weights

Description

Create a k-nearest neighbors based spatial weights

Usage

knn_weights(
  sf_obj,
  k,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
knn6_w <- knn_weights(guerry, 6)
summary(knn6_w)

Bonferroni bound value of local spatial autocorrelation

Description

Get Bonferroni bound value based on current LISA computation and current significat p-value

Usage

lisa_bo(gda_lisa, current_p)

Arguments

Value

A numeric value of Bonferroni bound

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
bo <- lisa_bo(lisa, 0.05)
bo

## End(Not run)

Get local cluster indicators

Description

Get the local cluster indicators returned from LISA computation.

Usage

lisa_clusters(gda_lisa, cutoff = 0)

Arguments

Value

A numeric vector of LISA cluster indicator

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
clsts <- lisa_clusters(lisa)
clsts

## End(Not run)

Get cluster colors

Description

Get the cluster colors of LISA computation.

Usage

lisa_colors(gda_lisa)

Arguments

Value

A string vector of cluster colors

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
clrs <- lisa_colors(lisa)
clrs

## End(Not run)

False Discovery Rate value of local spatial autocorrelation

Description

Get False Discovery Rate value based on current LISA computation and current significant p-value

Usage

lisa_fdr(gda_lisa, current_p)

Arguments

Value

A numeric vector of False Discovery Rate

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
fdr <- lisa_fdr(lisa, 0.05)
fdr

## End(Not run)

Get cluster labels

Description

Get cluster labels of LISA computation.

Usage

lisa_labels(gda_lisa)

Arguments

Value

A string vector of cluster labels

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
lbls <- lisa_labels(lisa)
lbls

## End(Not run)

Get numbers of neighbors for all observations

Description

Get numbers of neighbors for all observations

Usage

lisa_num_nbrs(gda_lisa)

Arguments

Value

A numeric vector of the number of neighbors

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
nn <- lisa_num_nbrs(lisa)
nn

## End(Not run)

Get pseudo-p values of LISA

Description

Get the local pseudo-p values of significance returned from LISA computation.

Usage

lisa_pvalues(gda_lisa)

Arguments

Value

A numeric vector of pseudo-p values of local spatial autocorrelation

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
pvals <- lisa_pvalues(lisa)
pvals

## End(Not run)

Get LISA values

Description

Get the local spatial autocorrelation values returned from LISA computation

Usage

lisa_values(gda_lisa)

Arguments

Value

A numeric vector of local spatial autocorrelation

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
lms <- lisa_values(lisa)
lms

## End(Not run)

Bivariate Local Join Count Statistics

Description

The function to apply local Bivariate Join Count statistics

Usage

local_bijoincount(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
guerry["InvCrm"] <-  1 - guerry[["TopCrm"]]
lisa <- local_bijoincount(queen_w, guerry[c("TopCrm", "InvCrm")])
clsts<- lisa_clusters(lisa)
clsts

Bivariate Local Moran Statistics

Description

The function to apply bivariate local Moran statistics

Usage

local_bimoran(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_bimoran(queen_w, guerry[c('Crm_prs','Litercy')])
lms <- lisa_values(lisa)
lms

Local Getis-Ord's G Statistics

Description

The function to apply Getis-Ord's local G statistics

Usage

local_g(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_g(queen_w, guerry["Crm_prs"])
lms <- lisa_values(lisa)
lms

Local Geary Statistics

Description

The function to apply local Geary statistics

Usage

local_geary(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_geary(queen_w, guerry["Crm_prs"])
lms <- lisa_values(lisa)
lms

Local Getis-Ord's G* Statistics

Description

The function to apply Getis-Ord's local G* statistics

Usage

local_gstar(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_gstar(queen_w,  guerry["Crm_prs"])
lms <- lisa_values(lisa)
lms

Local Join Count Statistics

Description

The function to apply local Join Count statistics

Usage

local_joincount(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_joincount(queen_w, guerry['TopCrm'])
clsts<- lisa_clusters(lisa)
clsts

Local Moran Statistics

Description

The function to apply local Moran statistics

Usage

local_moran(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_moran(queen_w, guerry["Crm_prs"])
lms <- lisa_values(lisa)
lms

Local Moran with Empirical Bayes(EB) Rate

Description

The 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.

Usage

local_moran_eb(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

## Not run: 
library(sf)
nat <- st_read("natregimes.shp")
nat_w <- queen_weights(nat)
lisa <- local_moran_eb(queen_w, guerry[c("hr60", "po60")])
lms <- lisa_values(lisa)
lms

## End(Not run)

Local Multivariate Geary Statistics

Description

The function to apply local Multivariate Geary statistics

Usage

local_multigeary(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants',
'Suicids')]
lisa <- local_multigeary(queen_w, data)
lms <- lisa_clusters(lisa)
lms

(Multivariate) Colocation Local Join Count Statistics

Description

The function to apply (multivariate) colocation local Join Count statistics

Usage

local_multijoincount(
  w,
  df,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_multijoincount(queen_w,
guerry[c('TopWealth','TopWealth', 'TopLit')])
clsts <- lisa_clusters(lisa)
clsts

Multivariate Quantile LISA Statistics

Description

The function to apply multivariate quantile LISA statistics

Usage

local_multiquantilelisa(
  w,
  df,
  k,
  q,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_multiquantilelisa(queen_w, guerry[c("Crm_prp", "Litercy")],
k=c(4,4), q=c(1,1))
clsts <- lisa_clusters(lisa)
clsts

Quantile LISA Statistics

Description

The function to apply quantile LISA statistics

Usage

local_quantilelisa(
  w,
  df,
  k,
  q,
  permutations = 999,
  permutation_method = "complete",
  significance_cutoff = 0.05,
  cpu_threads = 6,
  seed = 123456789
)

Arguments

Value

An instance of LISA-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
lisa <- local_quantilelisa(queen_w, guerry["Crm_prs"], k=4, q=1)
clsts <- lisa_clusters(lisa)
clsts

Make Spatial

Description

Make spatially constrained clusters from spatially non-constrained clusters using the contiguity information from the input weights

Usage

make_spatial(clusters, w)

Arguments

Value

A vector of categorical values (cluster classification)

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
clusters <- kmeans(5, data)
queen_w <- queen_weights(guerry)
results <- make_spatial(clusters, queen_w)
results

## End(Not run)

Maximum Neighbors of Spatial Weights

Description

Get the number of maximum neighbors of spatial weights

Usage

max_neighbors(gda_w)

Arguments

Value

The number of maximum neighbors of spatial weights

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
max_neighbors(queen_w)

## End(Not run)

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

Description

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.

Usage

maxp_greedy(
  w,
  df,
  bound_variable,
  min_bound,
  iterations = 99,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
bound_variable <- guerry['Pop1831']
min_bound <- 3236.67 # 10% of Pop1831
maxp_clusters <- maxp_greedy(queen_w, data, bound_variable, min_bound, iterations=99)
maxp_clusters

## End(Not run)

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

Description

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.

Usage

maxp_sa(
  w,
  df,
  bound_variable,
  min_bound,
  cooling_rate,
  sa_maxit = 1,
  iterations = 99,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
bound_variable <- guerry['Pop1831']
min_bound <- 3236.67 # 10% of Pop1831
maxp_clusters <- maxp_sa(queen_w, data, bound_variable, min_bound, cooling_rate=0.85, sa_maxit=1)
maxp_clusters

## End(Not run)

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

Description

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.

Usage

maxp_tabu(
  w,
  df,
  bound_variable,
  min_bound,
  tabu_length = 10,
  conv_tabu = 10,
  iterations = 99,
  initial_regions = vector("numeric"),
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
bound_variable <- guerry['Pop1831']
min_bound <- 3236.67 # 10% of Pop1831
maxp_clusters <- maxp_tabu(queen_w, data, bound_variable, min_bound, tabu_length=10, conv_tabu=10)
maxp_clusters

## End(Not run)

Mean Neighbors of Spatial Weights

Description

Get the number of mean neighbors of spatial weights

Usage

mean_neighbors(gda_w)

Arguments

Value

The number of mean neighbors of spatial weights

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
mean_neighbors(queen_w)

## End(Not run)

Median Neighbors of Spatial Weights

Description

Get the number of median neighbors of spatial weights

Usage

median_neighbors(gda_w)

Arguments

Value

The number of median neighbors of spatial weights

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
median_neighbors(queen_w)

## End(Not run)

Minimum Distance Threshold for Distance-based Weights

Description

Get minimum threshold of distance that makes sure each observation has at least one neighbor

Usage

min_distthreshold(sf_obj, is_arc = FALSE, is_mile = TRUE)

Arguments

Value

A numeric value of minimum threshold of distance

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
dist_thres <- min_distthreshold(guerry)
dist_thres

## End(Not run)

Minimum Neighbors of Spatial Weights

Description

Get the number of minimum neighbors of spatial weights

Usage

min_neighbors(gda_w)

Arguments

Value

The number of minimum neighbors of spatial weights

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
min_neighbors(queen_w)

## End(Not run)

Natural Breaks (Jenks)

Description

Natural Breaks group data whose boundaries are set where there are relatively big differences.

Usage

natural_breaks(k, df)

Arguments

Value

A vector of numeric values of computed breaks

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
natural_breaks(k=5, guerry['Crm_prs'])

Local Neighbor Match Test

Description

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.

Usage

neighbor_match_test(
  df,
  k,
  scale_method = "standardize",
  distance_method = "euclidean",
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

Value

A data.frame with two columns "Cardinality" and "Probability".

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
nbr_test <- neighbor_match_test(data, 6)
nbr_test

p_GeoDa

Description

p_GeoDa class is a RefClass that wraps the C++ 'GeoDa' class. See C++ functions in rcpp_rgeoda.cpp

p_GeoDaTable

Description

p_GeoDaTable class is a RefClass that wraps the C++ 'GeoDaTable' class. See C++ functions in rcpp_rgeoda.cpp

p_GeoDaWeight

Description

p_GeoDaWeight class is a RefClass that wraps the C++ GeoDaWeight class. See C++ functions in rcpp_weights.cpp

p_LISA

Description

p_LISA class is a RefClass that wraps the C++ LISA class. See C++ functions in rcpp_lisa.cpp

Percentile Breaks

Description

Percentile breaks data into 6 groups: the lowest 1 10-50

Usage

percentile_breaks(df)

Arguments

Value

A vector of numeric values of computed breaks

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
percentile_breaks(guerry['Crm_prs'])

Quantile Breaks

Description

Quantile breaks data into groups that each have the same number of observations

Usage

quantile_breaks(k, df)

Arguments

Value

A vector of numeric values of computed breaks

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
quantile_breaks(k=5, guerry['Crm_prs'])

Queen Contiguity Spatial Weights

Description

Create a Queen contiguity weights with options of "order", "include lower order" and "precision threshold"

Usage

queen_weights(
  sf_obj,
  order = 1,
  include_lower_order = FALSE,
  precision_threshold = 0
)

Arguments

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
summary(queen_w)

Read a .GAL file

Description

Create a spatial weights object from a .GAL file

Usage

read_gal(file_path, id_vec = c())

Arguments

Value

A weights object

Read a .GWT file

Description

Create a spatial weights object from a .GWT file

Usage

read_gwt(file_path, id_vec = c())

Arguments

Value

A weights object

Read a .SWM file

Description

Create a spatial weights object from a .SWM file

Usage

read_swm(file_path, id_vec = numeric())

Arguments

Value

A weights object

Regionalization with dynamically constrained agglomerative clustering and partitioning

Description

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

Usage

redcap(
  k,
  w,
  df,
  method = "fullorder-averagelinkage",
  bound_variable = data.frame(),
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

Value

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".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
guerry_clusters <- redcap(4, queen_w, data, "fullorder-completelinkage")
guerry_clusters

## End(Not run)

Rook Contiguity Spatial Weights

Description

Create a Rook contiguity weights with options of "order", "include lower order" and "precision threshold"

Usage

rook_weights(
  sf_obj,
  order = 1,
  include_lower_order = FALSE,
  precision_threshold = 0
)

Arguments

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
rook_w <- rook_weights(guerry)
summary(rook_w)

Save Spatial Weights

Description

Save spatial weights to a file

Usage

save_weights(gda_w, id_variable, out_path, layer_name = "")

Arguments

Value

A boolean value indicates if save successfully or failed

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
save_weights(quen_w, guerry_df['CODE_DE'], out_path = '/path/Guerry_r.gal')

## End(Not run)

Spatially Constrained Hierarchical Clucstering (SCHC)

Description

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.

Usage

schc(
  k,
  w,
  df,
  method = "average",
  bound_variable = data.frame(),
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  rdist = numeric()
)

Arguments

Value

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".

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
guerry_clusters <- schc(4, queen_w, data, "complete")
guerry_clusters

Set neighbors of an observation

Description

Set neighbors for idx-th observation, idx starts from 1

Usage

set_neighbors(gda_w, idx, nbrs)

Arguments

Examples

## Not run: 
new_w <- create_weights(10)
set_neighbors(new_w, 1, c(2,3))
update_weights(new_w)

## End(Not run)

Set neighbors and weights values of an observation

Description

Set neighbors and the associated weights values for idx-th observation, idx starts from 1

Usage

set_neighbors_with_weights(gda_w, idx, nbrs, wvals)

Arguments

Examples

## Not run: 
new_w <- create_weights(10)
set_neighbors(new_w, 1, c(2,3))
update_weights(new_w)

## End(Not run)

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

Description

Create an instance of geoda-class from a 'sf' object returned from 'st_read()' function. NOTE: The table content is NOT used to create an instance of geoda-class.

Usage

sf_to_geoda(sf_obj, with_table = TRUE)

Arguments

Value

An instance of geoda-class

Spatial C(K)luster Analysis by Tree Edge Removal

Description

SKATER forms clusters by spatially partitioning data that has similar values for features of interest.

Usage

skater(
  k,
  w,
  df,
  bound_variable = data.frame(),
  min_bound = 0,
  scale_method = "standardize",
  distance_method = "euclidean",
  random_seed = 123456789,
  cpu_threads = 6,
  rdist = numeric()
)

Arguments

Value

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".

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
guerry_clusters <- skater(4, queen_w, data)
guerry_clusters

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

Description

Create an instance of geoda-class from a 'sp' object. NOTE: The table content is NOT used to create an instance of geoda-class.

Usage

sp_to_geoda(sp_obj, with_table = TRUE)

Arguments

Value

An instance of geoda-class

Spatial Lag

Description

Compute the spatial lag for idx-th observation using selected variable and current weights matrix

Usage

spatial_lag(gda_w, df)

Arguments

Value

A data.frame with one column "Spatial Lag"

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
crm_lag <- spatial_lag(queen_w, guerry["Crm_prs"])
crm_lag

## End(Not run)

Spatial Validation

Description

Spatial validation provides a collection of validation measures including 1. fragmentations (entropy, simpson), 2. join count ratio, 3. compactness (isoperimeter quotient) and 4. diameter.

Usage

spatial_validation(sf_obj, clusters, w)

Arguments

Value

A list with names "Is Spatially Constrained", "Fragmentation", "Join Count Ratio", "Compactness", and "Diameter".

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
data <- guerry[c('Crm_prs','Crm_prp','Litercy','Donatns','Infants','Suicids')]
clusters <- skater(5, queen_w, data)
results <- spatial_validation(guerry, clusters, queen_w)
results

## End(Not run)

Standard Deviation Breaks

Description

Standard deviation breaks first transforms data to standard deviation units (mean=0, stddev=1), and then divide the range of values into 6 groups.

Usage

stddev_breaks(df)

Arguments

Value

A vector of numeric values of computed breaks

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
stddev_breaks(guerry['Crm_prs'])

Summary of Spatial Weights

Description

Override the summary() function for spatial weights

Usage

## S3 method for class 'Weight'
summary(object, ...)

Arguments

Value

A summary description of an instance of Weight-class

Examples

## Not run: 
library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
summary(queen_w)

## End(Not run)

Update meta data of a spatial weights

Description

Update meta data of a spatial weights. This function can be used after calling 'set_neighbor()' function .

Usage

update_weights(gda_w)

Arguments

Examples

## Not run: 
new_w <- create_weights(10)
set_neighbors(new_w, 1, c(2,3))
update_weights(new_w)

## End(Not run)

Sparsity of Spatial Weights

Description

Get sparsity (

Usage

weights_sparsity(gda_w)

Arguments

Value

A numeric value of spatial weights sparsity

Examples

## Not run: 
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
queen_w <- queen_weights(guerry)
weights_sparsity(queen_w)

## End(Not run)