Package 'geocomplexity'

geocomplexity for spatial raster data based on spatial dependence

Description

This function calculates geocomplexity for spatial raster data based on spatial dependence.

Usage

geocd_raster(r, order = 1, normalize = TRUE, method = "moran")

Arguments

r	SpatRaster object or can be converted to SpatRaster by terra::rast().
order	(optional) The order of the adjacency object. Default is 1.
normalize	(optional) Whether to further normalizes the calculated geocomplexity. Default is TRUE.
method	(optional) In instances where the method is moran, geocomplexity is determined using local moran measure method. Conversely, when the method is spvar, the spatial variance of attribute data serves to characterize geocomplexity. For all other methods, the shannon information entropy of attribute data is employed to represent geocomplexity. The selection of the method can be made from any one of the three options: moran, spvar or entropy. Default is moran.

Value

A SpatRaster object

Note

In contrast to the geocd_vector() function, the geocd_raster() performs operations internally on raster data based on neighborhood operations(focal) without providing additional wt object.

References

Zehua Zhang, Yongze Song, Peng Luo & Peng Wu (2023) Geocomplexity explains spatial errors, International Journal of Geographical Information Science, 37:7, 1449-1469, DOI: 10.1080/13658816.2023.2203212

Anselin, L. (2019). A local indicator of multivariate spatial association: Extending geary’s c. Geographical Analysis, 51(2), 133–150. https://doi.org/10.1111/gean.12164

Examples

library(terra)
m = matrix(c(3,3,3,3,1,3,
             3,3,3,2,1,2,
             3,3,3,1,2,1,
             1,3,2,2,2,2,
             2,2,2,1,1,2,
             1,2,1,1,1,1),
           nrow = 6,
           byrow = TRUE)
m = rast(m)
names(m) = 'sim'
plot(m, col = c("#d2eaac", "#a3dae1", "#8cc1e1"))
gc1 = geocd_raster(m,1)
gc2 = geocd_raster(m,2)
gc1
plot(gc1)
gc2
plot(gc2)

---

constructing spatial weight matrix based on geocomplexity with spatial dependence

Description

constructing spatial weight matrix based on geocomplexity with spatial dependence

Usage

geocd_swm(sfj, wt = NULL, style = "B", ...)

Arguments

sfj	An sf object or spatial vector object that can be converted to sf by sf::st_as_sf().
wt	(optional) Spatial weight matrix based on spatial adjacency or spatial distance relationships.
style	(optional) A character that can be B,W,C. More to see spdep::nb2mat(). Default is B.
...	(optional) Other parameters passed to geocomplexity::geocd_vector().

Value

A matrix

Examples

econineq = sf::read_sf(system.file('extdata/econineq.gpkg',package = 'geocomplexity'))
wt_gc = geocd_swm(econineq)
wt_gc[1:5,1:5]

---

geocomplexity for spatial vector data based on spatial dependence

Description

This function calculates geocomplexity for spatial vector data based on spatial dependence.

Usage

geocd_vector(
  sfj,
  wt = NULL,
  method = "moran",
  normalize = TRUE,
  returnsf = TRUE
)

Arguments

sfj	An sf object or spatial vector object that can be converted to sf by sf::st_as_sf().
wt	(optional) Spatial weight matrix. Must be a matrix class.
method	(optional) In instances where the method is moran, geocomplexity is determined using local moran measure method. Conversely, when the method is spvar, the spatial variance of attribute data serves to characterize geocomplexity. For all other methods, the shannon information entropy of attribute data is employed to represent geocomplexity. The selection of the method can be made from any one of the three options: moran, spvar or entropy. Default is moran.
normalize	(optional) Whether to further normalizes the calculated geocomplexity. Default is TRUE.
returnsf	(optional) When returnsf is TRUE, return an sf object, otherwise a tibble. Default is TRUE.

Value

A tibble (returnsf is FALSE) or an sf object (returnsf is TRUE)

Note

If wt is not provided, for polygon vector data, geocomplexity will use a first-order queen adjacency binary matrix; for point vector data, the six nearest points are used as adjacency objects to generate an adjacency binary matrix.

Examples

econineq = sf::read_sf(system.file('extdata/econineq.gpkg',package = 'geocomplexity'))
gc = geocd_vector(econineq)
gc

library(ggplot2)
library(viridis)
ggplot(gc) +
   geom_sf(aes(fill = GC_Gini)) +
   scale_fill_viridis(option = "mako", direction = -1) +
   theme_bw()

---

geocomplexity for spatial raster data based on geographical similarity

Description

This function calculates geocomplexity for spatial raster data based on geographical similarity.

Usage

geocs_raster(r, order = 1, normalize = TRUE, similarity = 1, method = "spvar")

Arguments

r	SpatRaster object or can be converted to SpatRaster by terra::rast().
order	(optional) The order of the adjacency object. Default is 1.
normalize	(optional) Whether to further normalizes the calculated geocomplexity. Default is TRUE.
similarity	(optional) When similarity is 1, the similarity is calculated using geographical configuration similarity, otherwise the cosine similarity is calculated. Default is 1.
method	(optional) When method is spvar, variation of the similarity vector is represented using spatial variance, otherwise shannon information entropy is used. Default is spvar.

Value

A SpatRaster object

Note

In contrast to the geocs_vector() function, the geocs_raster() performs operations internally on raster data without providing additional wt object.

Examples

library(terra)
m1 = matrix(c(3,3,3,3,1,3,
              3,3,3,2,1,2,
              3,3,3,1,2,1,
              1,3,2,2,2,2,
              2,2,2,1,1,2,
              1,2,1,1,1,1),
           nrow = 6,
           byrow = TRUE)
m1 = rast(m1)
names(m1) = 'sim1'
m2 = m1
set.seed(123456789)
values(m2) = values(m1) + runif(ncell(m1),-1,1)
names(m2) = 'sim2'
m = c(m1,m2)
gc1 = geocs_raster(m,1)
gc2 = geocs_raster(m,2)
gc1
plot(gc1)
gc2
plot(gc2)

---

constructing spatial weight matrix based on geocomplexity with similar geographical configurations

Description

constructing spatial weight matrix based on geocomplexity with similar geographical configurations

Usage

geocs_swm(sfj, wt = NULL, style = "B", ...)

Arguments

sfj	An sf object or spatial vector object that can be converted to sf by sf::st_as_sf().
wt	(optional) Spatial weight matrix based on spatial adjacency or spatial distance relationships.
style	(optional) A character that can be B,W,C. More to see spdep::nb2mat(). Default is B.
...	(optional) Other parameters passed to geocomplexity::geocs_vector().

Value

A matrix

Examples

econineq = sf::read_sf(system.file('extdata/econineq.gpkg',package = 'geocomplexity'))
wt_gc = geocs_swm(econineq)
wt_gc[1:5,1:5]

---

geocomplexity for spatial vector data based on geographical similarity

Description

This function calculates geocomplexity for in spatial vector data based on geographical similarity.

Usage

geocs_vector(
  sfj,
  wt = NULL,
  method = "spvar",
  similarity = 1,
  normalize = TRUE,
  returnsf = TRUE
)

Arguments

sfj	An sf object or spatial vector object that can be converted to sf by sf::st_as_sf().
wt	(optional) Spatial weight matrix. Must be a matrix class. If wt is not provided, geocomplexity will use a first-order inverse distance weight matrix via sdsfun::inverse_distance_swm() function.
method	(optional) When method is spvar, variation of the similarity vector is represented using spatial variance, otherwise shannon information entropy is used. Default is spvar.
similarity	(optional) When similarity is 1, the similarity is calculated using geographical configuration similarity, otherwise the cosine similarity is calculated. Default is 1.
normalize	(optional) Whether to further normalizes the calculated geocomplexity. Default is TRUE.
returnsf	(optional) When returnsf is TRUE, return an sf object, otherwise a tibble. Default is TRUE.

Value

A tibble (returnsf is FALSE) or an sf object (returnsf is TRUE)

Examples

econineq = sf::read_sf(system.file('extdata/econineq.gpkg',package = 'geocomplexity'))
gc = geocs_vector(dplyr::select(econineq,-Gini))
gc

library(ggplot2)
library(viridis)
ggplot(gc) +
   geom_sf(aes(fill = GC)) +
   scale_fill_viridis(option = "mako", direction = -1) +
   theme_bw()

---

geographical complexity-geographically weighted regression

Description

geographical complexity-geographically weighted regression

Usage

gwr_geoc(
  formula,
  data,
  gcs = NULL,
  alpha = seq(0.05, 1, 0.05),
  bw = "RMSE",
  adaptive = TRUE,
  kernel = "gaussian"
)

Arguments

formula	A formula of GCGWR model.
data	An sf object or spatial vector object that can be converted to sf by sf::st_as_sf().
gcs	(optional) The geocomplexity matrix corresponding to each variable, which is calculated by default using geocd_vector().
alpha	(optional) Balancing the weights of attribute similarity matrix and geographic distance matrix.
bw	(optional) The bandwidth used in selecting models. The optimal bandwidth can be selected using one of three methods: RMSE, AIC, and AICc. Default will use RMSE.
adaptive	(optional) Whether the bandwidth value is adaptive or not. Default is TRUE.
kernel	(optional) Kernel function. Default is gaussian.

Value

A list with GCGWR results.

SDF

an sf tibble with coefficients, standard errors and t values

diagnostic

goodness of fit indicators

args

some key parameters

Examples

## The following code takes a long time to run:
econineq = sf::read_sf(system.file('extdata/econineq.gpkg',package = 'geocomplexity'))
g = gwr_geoc(formula = Gini ~ ., data = econineq,
             alpha = 0.5, bw = "AIC", adaptive = TRUE)
g

---