Title:	Spatial Data Science Complementary Features
Version:	0.8.0
Description:	Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.
License:	GPL-3
Encoding:	UTF-8
URL:	https://stscl.github.io/sdsfun/, https://github.com/stscl/sdsfun
BugReports:	https://github.com/stscl/sdsfun/issues
RoxygenNote:	7.3.2
Depends:	R (≥ 4.1.0)
LinkingTo:	Rcpp, RcppArmadillo
Imports:	dplyr, geosphere, magrittr, pander, purrr, sf, spdep, stats, tibble, utils
Suggests:	ggplot2, Rcpp, RcppArmadillo, terra, testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2025-05-12 14:21:14 UTC; 31809
Author:	Wenbo Lv [aut, cre, cph]
Maintainer:	Wenbo Lv <lyu.geosocial@gmail.com>
Repository:	CRAN
Date/Publication:	2025-05-12 14:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Value

NULL (this is the magrittr pipe operator)

check for NA values in a tibble

Description

check for NA values in a tibble

Usage

check_tbl_na(tbl)

Arguments

Value

A logical value.

Examples

demotbl = tibble::tibble(x = c(1,2,3,NA,1),
                         y = c(NA,NA,1:3),
                         z = 1:5)
demotbl
check_tbl_na(demotbl)

(partial) correlation test

Description

(partial) correlation test

Usage

cor_test(x, y, z = NULL, level = 0.05)

Arguments

Value

A numeric vector

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
cor_test(gzma$PS_Score,gzma$EL_Score)

discretization

Description

discretization

Usage

discretize_vector(
  x,
  n,
  method = "natural",
  breakpoint = NULL,
  sampleprob = 0.15,
  thr = 0.4,
  seed = 123456789
)

Arguments

Value

A discretized integer vector

Examples

xvar = c(22361, 9573, 4836, 5309, 10384, 4359, 11016, 4414, 3327, 3408,
         17816, 6909, 6936, 7990, 3758, 3569, 21965, 3605, 2181, 1892,
         2459, 2934, 6399, 8578, 8537, 4840, 12132, 3734, 4372, 9073,
         7508, 5203)
discretize_vector(xvar, n = 5, method = 'natural')

transforming a category tibble into the corresponding dummy variable tibble

Description

transforming a category tibble into the corresponding dummy variable tibble

Usage

dummy_tbl(tbl)

Arguments

Value

A tibble

Examples

a = tibble::tibble(x = 1:3,y = 4:6)
dummy_tbl(a)

transforming a categorical variable into dummy variables

Description

transforming a categorical variable into dummy variables

Usage

dummy_vec(x)

Arguments

Value

A matrix.

Examples

dummy_vec(c(1,1,3,2,4,6))

get variable names in a formula and data

Description

get variable names in a formula and data

Usage

formula_varname(formula, data)

Arguments

Value

A list.

yname

Independent variable name

xname

Dependent variable names

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
formula_varname(PS_Score ~ EL_Score + OH_Score, gzma)
formula_varname(PS_Score ~ ., gzma)

spatial fuzzy overlay

Description

spatial fuzzy overlay

Usage

fuzzyoverlay(formula, data, method = "and")

Arguments

Value

A numeric vector.

Note

Independent variables in the data provided to fuzzyoverlay() must be discretized variables, and dependent variable are continuous variable.

Examples

set.seed(42)
sim = tibble::tibble(y = stats::runif(7,0,10),
                     x1 = c(1,rep(2,3),rep(3,3)),
                     x2 = c(rep(1,2),rep(2,2),rep(3,3)))
fo1 = fuzzyoverlay(y~x1+x2,data = sim, method = 'and')
fo1
fo2 = fuzzyoverlay(y~x1+x2,data = sim, method = 'or')
fo2

generate subsets of a set

Description

generate subsets of a set

Usage

generate_subsets(set, empty = TRUE, self = TRUE)

Arguments

Value

A list.

Examples

generate_subsets(letters[1:3])
generate_subsets(letters[1:3],empty = FALSE)
generate_subsets(letters[1:3],self = FALSE)
generate_subsets(letters[1:3],empty = FALSE,self = FALSE)

only geodetector q-value

Description

only geodetector q-value

Usage

geodetector_q(y, hs)

Arguments

Value

A numeric value

Examples

geodetector_q(y = 1:7, hs = c('x',rep('y',3),rep('z',3)))

hierarchical clustering with spatial soft constraints

Description

hierarchical clustering with spatial soft constraints

Usage

hclustgeo_disc(
  data,
  n,
  alpha = 0.5,
  D1 = NULL,
  hclustm = "ward.D2",
  scale = TRUE,
  wt = NULL,
  ...
)

Arguments

Value

The grouped membership: a vector if n is a scalar, a matrix (columns correspond to elements of n) if not.

Note

This is a ⁠C++⁠ enhanced implementation of the hclustgeo function in ClustGeo package.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma$group = hclustgeo_disc(gzma,5,alpha = 0.75)
plot(gzma["group"])

construct inverse distance weight

Description

Function for constructing inverse distance weight.

Usage

inverse_distance_swm(sfj, power = 1, bandwidth = NULL)

Arguments

Details

The inverse distance weight formula is w_{ij} = 1 / d_{ij}^\alpha

Value

A inverse distance weight matrices with class of matrix.

Examples

library(sf)
pts = read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
wt = inverse_distance_swm(pts)
wt[1:5,1:5]

determine optimal spatial data discretization for individual variables

Description

Function for determining optimal spatial data discretization for individual variables based on locally estimated scatterplot smoothing (LOESS) model.

Usage

loess_optnum(qvec, discnumvec, increase_rate = 0.05)

Arguments

Value

A two element numeric vector.

discnum

optimal number of spatial data discretization

increase_rate

the critical increase rate of the number of discretization

Note

When increase_rate is not satisfied by the calculation, the discrete number corresponding to the highest ⁠q statistic⁠ is selected as a return.

Note that sdsfun sorts discnumvec from smallest to largest and keeps qvec in one-to-one correspondence with discnumvec.

Examples

qv = c(0.26045642,0.64120405,0.43938704,0.95165535,0.46347836,
       0.25385338,0.78778726,0.95938330,0.83247885,0.09285196)
loess_optnum(qv,3:12)

global spatial autocorrelation test

Description

global spatial autocorrelation test

Usage

moran_test(sfj, wt = NULL, alternative = "greater", symmetrize = FALSE)

Arguments

Value

A list utilizing a result tibble to store the following information for each variable:

MoranI

observed value of the Moran coefficient

EI

expected value of Moran's I

VarI

variance of Moran's I (under normality)

ZI

standardized Moran coefficient

PI

p-value of the test statistic

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
moran_test(gzma)

normalization

Description

normalization

Usage

normalize_vector(x, to_left = 0, to_right = 1)

Arguments

Value

A continuous vector which has normalized.

Examples

normalize_vector(c(-5,1,5,0.01,0.99))

remove variable linear trend based on covariate

Description

remove variable linear trend based on covariate

Usage

rm_lineartrend(formula, data, method = c("cpp", "r"))

Arguments

Value

A numeric vector.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
rm_lineartrend(PS_Score ~ ., gzma)
rm_lineartrend(PS_Score ~ ., gzma, method = "r")

extract locations

Description

Extract locations of sf objects.

Usage

sf_coordinates(sfj)

Arguments

Value

A matrix.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
sf_coordinates(pts)

generates distance matrix

Description

Generates distance matrix for sf object

Usage

sf_distance_matrix(sfj)

Arguments

Value

A matrix.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
pts_distm = sf_distance_matrix(pts)
pts_distm[1:5,1:5]

sf object geometry column name

Description

Get the geometry column name of an sf object

Usage

sf_geometry_name(sfj)

Arguments

Value

A character.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_geometry_name(gzma)

sf object geometry type

Description

Get the geometry type of an sf object

Usage

sf_geometry_type(sfj)

Arguments

Value

A lowercase character vector

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_geometry_type(gzma)

generates cgcs2000 Gauss-Kruger projection epsg coding character

Description

Generates a Gauss-Kruger projection epsg coding character corresponding to an sfj object under the CGCS2000 spatial reference.

Usage

sf_gk_proj_cgcs2000(sfj, degree = 6L)

Arguments

Value

A character.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun')) |>
  sf::st_transform(4490)
sf_gk_proj_cgcs2000(gzma,3)
sf_gk_proj_cgcs2000(gzma,6)

generates wgs84 utm projection epsg coding character

Description

Generates a utm projection epsg coding character corresponding to an sfj object under the WGS84 spatial reference.

Usage

sf_utm_proj_wgs84(sfj)

Arguments

Value

A character.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_utm_proj_wgs84(gzma)

generates voronoi diagram

Description

Generates Voronoi diagram (Thiessen polygons) for sf object

Usage

sf_voronoi_diagram(sfj)

Arguments

Value

An sf object of polygon geometry type or can be converted to this by sf::st_as_sf().

Note

Only sf objects of (multi-)point type are supported to generate voronoi diagram and the returned result includes only the geometry column.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
pts_v = sf_voronoi_diagram(pts)

library(ggplot2)
ggplot() +
  geom_sf(data = pts_v, color = 'red',
          fill = 'transparent') +
  geom_sf(data = pts, color = 'blue', size = 1.25) +
  theme_void()

only spade power of spatial determinant

Description

only spade power of spatial determinant

Usage

spade_psd(y, hs, wt)

Arguments

Value

A numeric value

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = inverse_distance_swm(gzma)
spade_psd(y = gzma$PS_Score,
          hs = discretize_vector(gzma$PS_Score,5),
          wt = wt1)

constructs spatial weight matrices based on contiguity

Description

Constructs spatial weight matrices based on contiguity via spdep package.

Usage

spdep_contiguity_swm(
  sfj,
  queen = TRUE,
  k = NULL,
  order = 1L,
  cumulate = TRUE,
  style = "W",
  zero.policy = TRUE
)

Arguments

Value

A matrix

Note

When k is set to a positive value, using K-Nearest Neighbor Weights.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = spdep_contiguity_swm(gzma, k = 6, style = 'B')
wt2 = spdep_contiguity_swm(gzma, queen = TRUE, style = 'B')
wt3 = spdep_contiguity_swm(gzma, queen = FALSE, order = 2, style = 'B')

constructs spatial weight matrices based on distance

Description

Constructs spatial weight matrices based on distance via spdep package.

Usage

spdep_distance_swm(
  sfj,
  kernel = NULL,
  k = NULL,
  bandwidth = NULL,
  power = 1,
  style = "W",
  zero.policy = TRUE
)

Arguments

Details

five different kernel weight functions:

• uniform: K_{(z)} = 1/2,for \lvert z \rvert < 1

• triangular K_{(z)} = 1 - \lvert z \rvert,for \lvert z \rvert < 1

• quadratic (epanechnikov) K_{(z)} = \frac{3}{4} \left( 1 - z^2 \right),for \lvert z \rvert < 1

• quartic K_{(z)} = \frac{15}{16} {\left( 1 - z^2 \right)}^2,for \lvert z \rvert < 1

• gaussian K_{(z)} = \frac{1}{\sqrt{2 \pi}} e^{- \frac{z^2}{2}}

For the equation above, z = d_{ij} / h_i where h_i is the bandwidth

Value

A matrix

Note

When kernel is setting, using distance weight based on kernel function, Otherwise the inverse distance weight will be used.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
wt1 = spdep_distance_swm(pts, style = 'B')
wt2 = spdep_distance_swm(pts, kernel = 'gaussian')
wt3 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian')
wt4 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian', bandwidth = 10000)

spatial linear models selection

Description

spatial linear models selection

Usage

spdep_lmtest(formula, data, listw = NULL)

Arguments

Value

A list

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
spdep_lmtest(PS_Score ~ ., gzma)

construct neighbours list

Description

construct neighbours list

Usage

spdep_nb(sfj, queen = TRUE, k = NULL, order = 1L, cumulate = TRUE)

Arguments

Value

A neighbours list with class nb

Note

When k is set to a positive value, using K-Nearest Neighbor

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
nb1 = spdep_nb(pts, k = 6)
nb2 = spdep_nb(pts, queen = TRUE)
nb3 = spdep_nb(pts, queen = FALSE, order = 2)

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

spdep_skater(sfj, k = 6, nb = NULL, ini = 5, ...)

Arguments

Value

A numeric vector of clusters.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma_c = spdep_skater(gzma,8)
gzma$group = gzma_c
plot(gzma["group"])

spatial variance

Description

spatial variance

Usage

spvar(x, wt, method = c("cpp", "r"))

Arguments

Details

The spatial variance formula is \Gamma = \frac{\sum_i \sum_{j \neq i} \omega_{ij}\frac{(y_i-y_j)^2}{2}}{\sum_i \sum_{j \neq i} \omega_{ij}}

Value

A numerical value.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = inverse_distance_swm(gzma)
spvar(gzma$PS_Score,wt1)

spatial stratified heterogeneity test

Description

spatial stratified heterogeneity test

Usage

ssh_test(y, hs)

Arguments

Value

A tibble

Examples

ssh_test(y = 1:7, hs = c('x',rep('y',3),rep('z',3)))

standardization

Description

To calculate the Z-score using variance normalization, the formula is as follows:

Z = \frac{(x - mean(x))}{sd(x)}

Usage

standardize_vector(x)

Arguments

Value

A standardized numeric vector

Examples

standardize_vector(1:10)

convert discrete variables in a tibble to integers

Description

convert discrete variables in a tibble to integers

Usage

tbl_all2int(tbl)

Arguments

Value

A converted tibble,data.frame or sf object.

Examples

demotbl = tibble::tibble(x = c(1,2,3,3,1),
                         y = letters[1:5],
                         z = c(1L,1L,2L,2L,3L),
                         m = factor(letters[1:5],levels = letters[5:1]))
tbl_all2int(demotbl)

convert xyz tbl to matrix

Description

convert xyz tbl to matrix

Usage

tbl_xyz2mat(tbl, x = 1, y = 2, z = 3)

Arguments

Value

A list.

z_attrs_matrix

A matrix with attribute information.

x_coords_matrix

A matrix with the x-axis coordinates.

y_coords_matrix

A matrix with the y-axis coordinates.

Examples

set.seed(42)
lon = rep(1:3,each = 3)
lat = rep(1:3,times = 3)
zattr = rnorm(9, mean = 10, sd = 1)
demodf = data.frame(x = lon, y = lat, z = zattr)
demodf
tbl_xyz2mat(demodf)