Package 'geocomplexity' reference manual

Title:	Mitigating Spatial Bias Through Geographical Complexity
Description:	The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.
Authors:	Wenbo Lv [aut, cre, cph] , Yongze Song [aut] , Zehua Zhang [aut]
Maintainer:	Wenbo Lv <[email protected]>
License:	GPL-3
Version:	0.3.0
Built:	2024-11-11 12:25:40 UTC
Source:	https://github.com/ausgis/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")
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)

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", ...)
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]

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
)
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()

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")
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)

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", ...)
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]

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
)
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()

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"
)
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

## 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

Package 'geocomplexity'

Help Index

geocomplexity for spatial raster data based on spatial dependence

Description

Usage

Arguments

Value

Note

References

Examples

constructing spatial weight matrix based on geocomplexity with spatial dependence

Description

Usage

Arguments

Value

Examples

geocomplexity for spatial vector data based on spatial dependence

Description

Usage

Arguments

Value

Note

Examples

geocomplexity for spatial raster data based on geographical similarity

Description

Usage

Arguments

Value

Note

Examples

constructing spatial weight matrix based on geocomplexity with similar geographical configurations

Description

Usage

Arguments

Value

Examples

geocomplexity for spatial vector data based on geographical similarity

Description

Usage

Arguments

Value

Examples

geographical complexity-geographically weighted regression

Description

Usage

Arguments

Value

Examples