Package 'gdverse'

Description

convert all discretized vectors to integer

Usage

all2int(x)

Arguments

Value

An integer vector.

Examples

all2int(factor(letters[1:3],levels = c('b','a','c')))
all2int(letters[1:3])

Description

Function for determining the optimal spatial data discretization based on SPADE q-statistics.

Usage

cpsd_disc(
  formula,
  data,
  wt,
  discnum = 3:8,
  discmethod = "quantile",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

Value

A list.

x

discretization variable name

k

optimal number of spatial data discreteization

method

optimal spatial data discretization method

disc

the result of optimal spatial data discretization

Note

When the discmethod is configured to robust, it will operate at a significantly reduced speed. Consequently, the use of robust discretization is not advised.

References

Yongze Song & Peng Wu (2021) An interactive detector for spatial associations, International Journal of Geographical Information Science, 35:8, 1676-1701, DOI:10.1080/13658816.2021.1882680

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
cpsd_disc(y ~ xa + xb + xc, data = sim, wt = wt)

Description

Function for calculate compensated power of spatial determinant Q_s.

Usage

cpsd_spade(yobs, xobs, xdisc, wt)

Arguments

Details

The power of compensated spatial determinant formula is

$Q_s = \frac{q_s}{q_{s_{inforkep}}} = \frac{1 - \frac{\sum_{h=1}^L N_h \Gamma_{kdep}}{N \Gamma_{totaldep}}}{1 - \frac{\sum_{h=1}^L N_h \Gamma_{hind}}{N \Gamma_{totalind}}}$

Value

A value of compensated power of spatial determinant Q_s.

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
xa = sim$xa
xa_disc = sdsfun::discretize_vector(xa,5)
cpsd_spade(sim$y,xa,xa_disc,wt)

Description

Compare the effects of two factors $X_1$ and $X_2$ on the spatial distribution of the attribute $Y$.

Usage

ecological_detector(y, x1, x2, alpha = 0.95)

Arguments

Value

A list.

F-statistic

the result of F statistic for ecological detector

P-value

the result of P value for ecological detector

Ecological

is there a significant difference between the two factors $X_1$ and $X_2$ on the spatial distribution of the attribute $Y$

Examples

ecological_detector(y = 1:7,
                    x1 = c('x',rep('y',3),rep('z',3)),
                    x2 = c(rep('a',2),rep('b',2),rep('c',3)))

Description

Function for measure information loss by shannon information entropy.

Usage

F_informationloss(xvar, xdisc)

Arguments

Details

The information loss measured by information entropy formula is $F = -\sum\limits_{i=1}^N p_{(i)}\log_2 p_{(i)} - \left(-\sum\limits_{h=1}^L p_{(h)}\log_2 p_{(h)}\right)$

Value

A numeric value of information loss measured by information entropy.

Examples

F_informationloss(1:7,c('x',rep('y',3),rep('z',3)))

Description

The factor detector q-statistic measures the spatial stratified heterogeneity of a variable Y, or the determinant power of a covariate X of Y.

Usage

factor_detector(y, x, confintv = FALSE, alpha = 0.95)

Arguments

Value

A list.

Q-statistic

the q statistic for factor detector

P-value

the p value for factor detector

CIL

the confidence interval lower bound

CIU

the confidence interval upper bound

Examples

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

Description

native geographical detector(GD) model

Usage

gd(formula, data, type = "factor", alpha = 0.95)

Arguments

Value

A list.

factor

the result of factor detector

interaction

the result of interaction detector

risk

the result of risk detector

ecological

the result of ecological detector

References

Jin‐Feng Wang, Xin‐Hu Li, George Christakos, Yi‐Lan Liao, Tin Zhang, XueGu & Xiao‐Ying Zheng (2010) Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China, International Journal of Geographical Information Science, 24:1, 107-127, DOI: 10.1080/13658810802443457

Examples

data("NTDs")
g = gd(incidence ~ watershed + elevation + soiltype,
       data = NTDs,type = c('factor','interaction'))
g

Description

optimal univariate discretization based on geodetector q-statistic

Usage

gd_optunidisc(
  formula,
  data,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

Value

A list.

x

the name of the variable that needs to be discretized

k

optimal discretization number

method

optimal discretization method

qstatistic

optimal q-statistic

disc

optimal discretization results

Examples

data('sim')
gd_optunidisc(y ~ xa + xb + xc,
              data = sim,
              discnum = 3:6)

Description

generate permutations

Usage

gen_permutations(x, seed = 123456789)

Arguments

Value

A permutations vector.

Examples

gen_permutations(1:100,42)

Description

geographical detector

Usage

geodetector(formula, data, type = "factor", alpha = 0.95)

Arguments

Value

A list.

factor

the result of factor detector

interaction

the result of interaction detector

risk

the result of risk detector

ecological

the result of ecological detector

Note

Note that only one type of geodetector is supported at a time in geodetector().

Examples

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))))

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))),
   type = 'interaction')

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))),
   type = 'risk',alpha = 0.95)

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))),
   type = 'ecological',alpha = 0.95)

Description

geographically optimal zones-based heterogeneity(GOZH) model

Usage

gozh(formula, data, cores = 1, type = "factor", alpha = 0.95, ...)

Arguments

Value

A list.

factor

the result of factor detector

interaction

the result of interaction detector

risk

the result of risk detector

ecological

the result of ecological detector

References

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., & Meng, L. (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009

Examples

data('ndvi')
g = gozh(NDVIchange ~ ., data = ndvi)
g

Description

Function for geographically optimal zones-based heterogeneity detector.

Usage

gozh_detector(formula, data, cores = 1, type = "factor", alpha = 0.95, ...)

Arguments

Value

A list of tibble with the corresponding result under different detector types.

factor

the result of factor detector

interaction

the result of interaction detector

risk

the result of risk detector

ecological

the result of ecological detector

Note

Only one type of detector is supported in a gozh_detector() run at a time.

References

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., & Meng, L. (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009

Examples

data('ndvi')
g = gozh_detector(NDVIchange ~ ., data = ndvi)
g

Description

interactive detector for spatial associations(IDSA) model

Usage

idsa(
  formula,
  data,
  wt = NULL,
  discnum = 3:8,
  discmethod = "quantile",
  overlay = "and",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  alpha = 0.95,
  ...
)

Arguments

Value

A list.

interaction

the interaction result of IDSA model

risk

whether values of the response variable between a pair of overlay zones are significantly different

number_individual_explanatory_variables

the number of individual explanatory variables used for examining the interaction effects

number_overlay_zones

the number of overlay zones

percentage_finely_divided_zones

the percentage of finely divided zones that are determined by the interaction of variables

Note

Please note that all variables in the IDSA model need to be continuous data.

The IDSA model requires at least $2^n-1$ calculations when has $n$ explanatory variables. When there are more than 10 explanatory variables, carefully consider the computational burden of this model. When there are a large number of explanatory variables, the data dimensionality reduction method can be used to ensure the trade-off between analysis results and calculation speed.

References

Yongze Song & Peng Wu (2021) An interactive detector for spatial associations, International Journal of Geographical Information Science, 35:8, 1676-1701, DOI:10.1080/13658816.2021.1882680

Examples

data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = idsa(y ~ ., data = sim1)
g

Description

Identify the interaction between different risk factors, that is, assess whether factors X1 and X2 together increase or decrease the explanatory power of the dependent variable Y, or whether the effects of these factors on Y are independent of each other.

Usage

interaction_detector(y, x1, x2)

Arguments

Value

A list.

Variable1 Q-statistics

Q-statistics for variable1

Variable2 Q-statistics

Q-statistics for variable2

Variable1 and Variable2 interact Q-statistics

Q-statistics for variable1 and variable2 interact

Interaction

the interact result type

Variable1 P-value

P-value of the Q-statistic for Variable1

Variable2 P-value

P-value of the Q-statistic for Variable2

Variable1 and Variable2 interact P-value

P-value of the Q-statistic for variable1 and variable2 interact

Examples

interaction_detector(y = 1:7,
                     x1 = c('x',rep('y',3),rep('z',3)),
                     x2 = c(rep('a',2),rep('b',2),rep('c',3)))

Description

locally explained stratified heterogeneity(LESH) model

Usage

lesh(formula, data, cores = 1, ...)

Arguments

Value

A list.

interaction

the interaction result of LESH model

spd_lesh

a tibble of the shap power of determinants

Note

The LESH model requires at least $2^n-1$ calculations when has $n$ explanatory variables. When there are more than 10 explanatory variables, carefully consider the computational burden of this model. When there are a large number of explanatory variables, the data dimensionality reduction method can be used to ensure the trade-off between analysis results and calculation speed.

References

Li, Y., Luo, P., Song, Y., Zhang, L., Qu, Y., & Hou, Z. (2023). A locally explained heterogeneity model for examining wetland disparity. International Journal of Digital Earth, 16(2), 4533–4552. https://doi.org/10.1080/17538947.2023.2271883

Examples

data('ndvi')
g = lesh(NDVIchange ~ ., data = ndvi)
g

Description

Function for determining optimal spatial data analysis scale based on locally estimated scatter plot smoothing (LOESS) model.

Usage

loess_optscale(qvec, spscalevec, increase_rate = 0.05)

Arguments

Value

A numeric vector about optimal number of spatial scale and the critical increase rate of q value.

Examples

## Not run: 
## The following code takes a long time to run:
library(tidyverse)
fvcpath = "https://github.com/SpatLyu/rdevdata/raw/main/FVC.tif"
fvc = terra::rast(paste0("/vsicurl/",fvcpath))
fvc1000 = fvc %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
fvc5000 = fvc %>%
  terra::aggregate(fact = 5) %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
qv1000 = factor_detector(fvc1000$fvc,
                         sdsfun::discretize_vector(fvc1000$premax,10,'quantile'))[[1]]
qv5000 = factor_detector(fvc5000$fvc,
                         sdsfun::discretize_vector(fvc5000$premax,10,'quantile'))[[1]]
loess_optscale(c(qv1000,qv5000),c(1000,5000))

## End(Not run)

Description

dataset of NDVI changes and its influencing factors, modified from GD package.

Usage

ndvi

Format

ndvi: A tibble with 713 rows and 7 variables

Author(s)

Yongze Song [email protected]

Description

The data were obtained by preprocessing use sf and tidyverse.

Usage

NTDs

Format

NTDs: A tibble with 185 rows and 4 variable columns and 2 location columns, modified from geodetector package.

Description

optimal parameters-based geographical detector(OPGD) model

Usage

opgd(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  type = "factor",
  alpha = 0.95,
  ...
)

Arguments

Value

A list.

opt_param

optimal discretization parameter

factor

the result of factor detector

interaction

the result of interaction detector

risk

the result of risk detector

ecological

the result of ecological detector

References

Song, Y., Wang, J., Ge, Y. & Xu, C. (2020) An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data, GIScience & Remote Sensing, 57(5), 593-610. doi: 10.1080/15481603.2020.1760434.

Examples

data('sim')
opgd(y ~ xa + xb + xc, data = sim,
     discvar = paste0('x',letters[1:3]),
     discnum = 3:6)

Description

IDSA Q-saistics PID

Usage

pid_idsa(formula, rawdata, discdata, wt, overlaymethod = "and")

Arguments

Details

$Q_{IDSA} = \frac{\theta_r}{\phi}$

Value

The value of IDSA Q-saistics PID.

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
sim1 = dplyr::mutate(sim,dplyr::across(xa:xc,\(.x) sdsfun::discretize_vector(.x,5)))
pid_idsa(y ~ xa + xb + xc, rawdata = sim,
         discdata = sim1, wt = wt)

Description

S3 method to plot output for ecological detector in geodetector().

Usage

## S3 method for class 'ecological_detector'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for factor detector in geodetector().

Usage

## S3 method for class 'factor_detector'
plot(x, slicenum = 2, alpha = 0.95, keep = TRUE, qlabelsize = 3.88, ...)

Arguments

Value

A ggplot2 layer.

Description

S3 method to plot output for GD model result in gd().

Usage

## S3 method for class 'gd_result'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for GOZH model result in gozh().

Usage

## S3 method for class 'gozh_result'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for IDSA risk result in idsa().

Usage

## S3 method for class 'idsa_result'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for interaction detector in geodetector().

Usage

## S3 method for class 'interaction_detector'
plot(x, alpha = 1, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for LESH model interaction result in lesh().

Usage

## S3 method for class 'lesh_result'
plot(
  x,
  pie = TRUE,
  scatter = FALSE,
  scatter_alpha = 1,
  pieradius_factor = 15,
  pielegend_x = 0.99,
  pielegend_y = 0.1,
  pielegend_num = 3,
  ...
)

Arguments

Value

A ggplot2 layer.

Note

When both scatter and pie are set to TRUE in RStudio, enlarge the drawing frame for normal display.

Description

S3 method to plot output for OPGD model result in opgd().

Usage

## S3 method for class 'opgd_result'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for RGD model result in rgd().

Usage

## S3 method for class 'rgd_result'
plot(x, slicenum = 2, alpha = 0.95, keep = TRUE, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for RID model from rid().

Usage

## S3 method for class 'rid_result'
plot(x, alpha = 1, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for risk detector in geodetector().

Usage

## S3 method for class 'risk_detector'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for gozh sesu in sesu_gozh().

Usage

## S3 method for class 'sesu_gozh'
plot(x, ...)

Arguments

Value

A ggplot2 layer.

Description

S3 method to plot output for opgd sesu in sesu_opgd().

Usage

## S3 method for class 'sesu_opgd'
plot(x, ...)

Arguments

Value

A ggplot2 layer.

Description

S3 method to plot output for SPADE power of spatial and multilevel discretization determinant from spade().

Usage

## S3 method for class 'spade_result'
plot(x, slicenum = 2, alpha = 0.95, keep = TRUE, ...)

Arguments

Value

A ggplot2 layer.

Description

S3 method to plot output for spatial rough set-based ecological detector in srsgd().

Usage

## S3 method for class 'srs_ecological_detector'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for spatial rough set-based factor detector in srsgd().

Usage

## S3 method for class 'srs_factor_detector'
plot(x, slicenum = 2, ...)

Arguments

Value

A ggplot2 layer.

Description

S3 method to plot output for spatial rough set-based interaction detector in srsgd().

Usage

## S3 method for class 'srs_interaction_detector'
plot(x, alpha = 1, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to plot output for SRSGD model result in srsgd().

Usage

## S3 method for class 'srsgd_result'
plot(x, ...)

Arguments

Value

A ggplot2 layer

Description

S3 method to format output for ecological detector in geodetector().

Usage

## S3 method for class 'ecological_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for factor detector in geodetector().

Usage

## S3 method for class 'factor_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for GD model from gd().

Usage

## S3 method for class 'gd_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for GOZH model from gozh().

Usage

## S3 method for class 'gozh_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for IDSA model from idsa().

Usage

## S3 method for class 'idsa_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for interaction detector in geodetector().

Usage

## S3 method for class 'interaction_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for LESH model interaction result in lesh().

Usage

## S3 method for class 'lesh_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for OPGD model from opgd().

Usage

## S3 method for class 'opgd_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for RGD model from rgd().

Usage

## S3 method for class 'rgd_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for RID model from rid().

Usage

## S3 method for class 'rid_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for risk detector in geodetector().

Usage

## S3 method for class 'risk_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for gozh sesu from sesu_gozh().

Usage

## S3 method for class 'sesu_gozh'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for opgd sesu from sesu_opgd().

Usage

## S3 method for class 'sesu_opgd'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for SPADE power of spatial and multilevel discretization determinant from spade().

Usage

## S3 method for class 'spade_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for spatial rough set-based ecological detector in srsgd().

Usage

## S3 method for class 'srs_ecological_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for spatial rough set-based factor detector in srsgd().

Usage

## S3 method for class 'srs_factor_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for spatial rough set-based interaction detector in srsgd().

Usage

## S3 method for class 'srs_interaction_detector'
print(x, ...)

Arguments

Value

Formatted string output

Description

S3 method to format output for SRSGD model from srsgd().

Usage

## S3 method for class 'srsgd_result'
print(x, ...)

Arguments

Value

Formatted string output

Description

PSD of an interaction of explanatory variables (PSD-IEV)

Usage

psd_iev(discdata, spzone, wt)

Arguments

Details

$\phi = 1 - \frac{\sum_{i=1}^m \sum_{k=1}^{n_i}N_{i,k}\tau_{i,k}}{\sum_{i=1}^m N_i \tau_i}$

Value

The Value of PSD-IEV

References

Yongze Song & Peng Wu (2021) An interactive detector for spatial associations, International Journal of Geographical Information Science, 35:8, 1676-1701, DOI:10.1080/13658816.2021.1882680

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
sim1 = dplyr::mutate(sim,dplyr::across(xa:xc,\(.x) sdsfun::discretize_vector(.x,5)))
sz = sdsfun::fuzzyoverlay(y ~ xa + xb + xc, data = sim1)
psd_iev(dplyr::select(sim1,xa:xc),sz,wt)

Description

Function for calculate power of spatial determinant $q_s$.

Usage

psd_pseudop(y, x, wt, cores = 1, seed = 123456789, permutations = 0)

Arguments

Details

The power of spatial determinant formula is $q_s = 1 - \frac{\sum_{h=1}^L N_h \Gamma_h}{N \Gamma}$

Value

A tibble of power of spatial determinant and the corresponding pseudo-p value.

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')),
                                  power = 2)
psd_pseudop(sim$y,sdsfun::discretize_vector(sim$xa,5),wt)

Description

Function for calculate power of spatial determinant q_s

Usage

psd_spade(y, x, wt)

Arguments

Details

The power of spatial determinant formula is

$q_s = 1 - \frac{\sum_{h=1}^L N_h \Gamma_h}{N \Gamma}$

Value

A value of power of spatial determinant q_s.

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')),
                                  power = 2)
psd_spade(sim$y,sdsfun::discretize_vector(sim$xa,5),wt)

Description

Function for calculate power of spatial and multilevel discretization determinant and the corresponding pseudo-p value.

Usage

psmd_pseudop(
  yobs,
  xobs,
  wt,
  discnum = 3:8,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  permutations = 0,
  ...
)

Arguments

Details

The power of spatial and multilevel discretization determinant formula is $PSMDQ_s = MEAN(Q_s)$

Value

A tibble of power of spatial and multilevel discretization determinant and the corresponding pseudo-p value.

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
psmd_pseudop(sim$y,sim$xa,wt)

Description

Function for calculate power of spatial and multilevel discretization determinant PSMDQ_s.

Usage

psmd_spade(
  yobs,
  xobs,
  wt,
  discnum = 3:8,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

Details

The power of spatial and multilevel discretization determinant formula is $PSMDQ_s = MEAN(Q_s)$

Value

A value of power of spatial and multilevel discretization determinant PSMDQ_s.

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
psmd_spade(sim$y,sim$xa,wt)

Description

robust geographical detector(RGD) model

Usage

rgd(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

Value

A list.

factor

robust power of determinant

opt_disc

optimal robust discrete results

allfactor

factor detection results corresponding to different number of robust discreteizations

alldisc

all robust discrete results

References

Zhang, Z., Song, Y.*, & Wu, P., 2022. Robust geographical detector. International Journal of Applied Earth Observation and Geoinformation. 109, 102782. DOI: 10.1016/j.jag.2022.102782.

Examples

data('sim')

tryCatch({
    g = rgd(y ~ .,
           data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
           discnum = 3:6, cores = 1)
    g
  }, error = \(e) message("Skipping Python-dependent example: ", e$message))

Description

robust interaction detector(RID) model

Usage

rid(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

Value

A list.

interaction

the result of RID model

References

Zhang, Z., Song, Y., Karunaratne, L., & Wu, P. (2024). Robust interaction detector: A case of road life expectancy analysis. Spatial Statistics, 59(100814), 100814. https://doi.org/10.1016/j.spasta.2024.100814

Examples

data('sim')

tryCatch({
  g = rid(y ~ .,
          data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
          discnum = 3:6, cores = 1)
  g
}, error = \(e) message("Skipping Python-dependent example: ", e$message))

Description

Determine whether there is a significant difference between the attribute means of two sub regions.

Usage

risk_detector(y, x, alpha = 0.95)

Arguments

Value

A tibble.

Examples

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

Description

Determines discretization interval breaks using an optimization algorithm for variance-based change point detection.

Usage

robust_disc(formula, data, discnum, minsize = 1, cores = 1)

Arguments

Value

A tibble.

Examples

data('sim')

tryCatch({
  robust_disc(y ~ xa, data = sim, discnum = 5)
  robust_disc(y ~ .,
              data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
              discnum = 5, cores = 3)
}, error = \(e) message("Skipping Python-dependent example: ", e$message))

Description

discretization of variables based on recursive partitioning

Usage

rpart_disc(formula, data, ...)

Arguments

Value

A vector that being discretized.

References

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., & Meng, L. (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009

Examples

data('ndvi')
rpart_disc(NDVIchange ~ ., data = ndvi)

Description

comparison of size effects of spatial units based on GOZH

Usage

sesu_gozh(
  formula,
  datalist,
  su,
  cores = 1,
  strategy = 2L,
  increase_rate = 0.05,
  alpha = 0.95,
  ...
)

Arguments

Details

When strategy is 1, use the same process as sesu_opgd().If not, all explanatory variables are used to generate a unique Q statistic corresponding to the data in the datalist based on rpart_disc() and gd(), and then loess_optscale()is used to determine the optimal analysis scale.

Value

A list.

sesu

a tibble representing size effects of spatial units

optsu

optimal spatial unit

strategy

the optimal analytical scale selection strategy

increase_rate

the critical increase rate of q value

References

Song, Y., Wang, J., Ge, Y. & Xu, C. (2020) An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data, GIScience & Remote Sensing, 57(5), 593-610. doi: 10.1080/15481603.2020.1760434.

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., & Meng, L. (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009

Examples

## Not run: 
## The following code takes a long time to run:
library(tidyverse)
fvcpath = "https://github.com/SpatLyu/rdevdata/raw/main/FVC.tif"
fvc = terra::rast(paste0("/vsicurl/",fvcpath))
fvc1000 = fvc %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
fvc5000 = fvc %>%
  terra::aggregate(fact = 5) %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
sesu_gozh(fvc ~ .,
          datalist = list(fvc1000,fvc5000),
          su = c(1000,5000),
          cores = 6)

## End(Not run)

Description

comparison of size effects of spatial units based on OPGD

Usage

sesu_opgd(
  formula,
  datalist,
  su,
  discvar,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  increase_rate = 0.05,
  alpha = 0.95,
  ...
)

Arguments

Details

Firstly, the OPGD model is executed for each data in the datalist (all significant Q statistic of each data are averaged to represent the spatial association strength under this spatial unit), and then the loess_optscale function is used to select the optimal spatial analysis scale.

Value

A list.

sesu

a tibble representing size effects of spatial units

optsu

optimal spatial unit

increase_rate

the critical increase rate of q value

References

Song, Y., Wang, J., Ge, Y. & Xu, C. (2020) An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data, GIScience & Remote Sensing, 57(5), 593-610. doi: 10.1080/15481603.2020.1760434.

Examples

## Not run: 
## The following code takes a long time to run:
library(tidyverse)
fvcpath = "https://github.com/SpatLyu/rdevdata/raw/main/FVC.tif"
fvc = terra::rast(paste0("/vsicurl/",fvcpath))
fvc1000 = fvc %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
fvc5000 = fvc %>%
  terra::aggregate(fact = 5) %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
sesu_opgd(fvc ~ .,
          datalist = list(fvc1000,fvc5000),
          su = c(1000,5000),
          discvar = names(select(fvc5000,-c(fvc,lulc))),
          cores = 6)

## End(Not run)

Description

Simulation data.

Usage

sim

Format

sim: A tibble with 80 rows and 6 variables, modified from IDSA package.

Author(s)

Yongze Song [email protected]

Description

spatial association detector (SPADE) model

Usage

spade(
  formula,
  data,
  wt = NULL,
  discvar = NULL,
  discnum = 3:8,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  permutations = 0,
  ...
)

Arguments

Value

A list.

factor

the result of SPADE model

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = spade(y ~ ., data = sim1)
g

Description

Function for calculate shap power of determinants $SPD$.

Usage

spd_lesh(formula, data, cores = 1, ...)

Arguments

Details

The power of shap power of determinants formula is

$\theta_{x_j} \left( S \right) = \sum\limits_{s \in M \setminus \{x_j\}} \frac{|S|! \left(|M| - |S| - 1\right)!}{|M|!}\left(v \left(S \cup \left\{x_j\right\} \right) - v\left(S\right)\right)$.

shap power of determinants (SPD) is the contribution of variable $x_j$ to the power of determinants.

Value

A tibble with variable and its corresponding $SPD$ value.

Note

The shap power of determinants (SPD) requires at least $2^n-1$ calculations when has $n$ explanatory variables. When there are more than 10 explanatory variables, carefully consider the computational burden of this model. When there are a large number of explanatory variables, the data dimensionality reduction method can be used to ensure the trade-off between analysis results and calculation speed.

References

Li, Y., Luo, P., Song, Y., Zhang, L., Qu, Y., & Hou, Z. (2023). A locally explained heterogeneity model for examining wetland disparity. International Journal of Digital Earth, 16(2), 4533–4552. https://doi.org/10.1080/17538947.2023.2271883

Examples

data('ndvi')
g = spd_lesh(NDVIchange ~ ., data = ndvi)
g

Description

spatial rough set-based ecological detector

Usage

srs_ecological_detector(y, x1, x2, wt, alpha = 0.95)

Arguments

Value

A list.

T-statistic

the result of T statistic for spatial rough set-based ecological detector

P-value

the result of P value for spatial rough set-based ecological detector

Ecological

does one spatial feature $X_1$ play a more important role than $X_2$

References

Bai, H., Li, D., Ge, Y., Wang, J., & Cao, F. (2022). Spatial rough set-based geographical detectors for nominal target variables. Information Sciences, 586, 525–539. https://doi.org/10.1016/j.ins.2021.12.019

Examples

data('srs_table')
data('srs_wt')
srs_ecological_detector(srs_table$d,srs_table$a1,srs_table$a2,srs_wt)

Description

spatial rough set-based factor detector

Usage

srs_factor_detector(y, x, wt)

Arguments

Value

A list.

PD

the average local explanatory power

SE_PD

the degree of spatial heterogeneity of the local explanatory power

References

Bai, H., Li, D., Ge, Y., Wang, J., & Cao, F. (2022). Spatial rough set-based geographical detectors for nominal target variables. Information Sciences, 586, 525–539. https://doi.org/10.1016/j.ins.2021.12.019

Examples

data('srs_table')
data('srs_wt')
srs_factor_detector(srs_table$d,srs_table$a1,srs_wt)

Description

spatial rough set-based geographical detector

Usage

srs_geodetector(formula, data, wt = NULL, type = "factor", alpha = 0.95)

Arguments

Value

A list.

factor

the result of spatial rough set-based factor detector

interaction

the result of spatial rough set-based interaction detector

ecological

the result of spatial rough set-based ecological detector

Examples

data('srs_table')
data('srs_wt')
srs_geodetector(d ~ a1 + a2 + a3, data = srs_table, wt = srs_wt)
srs_geodetector(d ~ a1 + a2 + a3, data = srs_table,
                wt = srs_wt, type = 'interaction')
srs_geodetector(d ~ a1 + a2 + a3, data = srs_table,
                wt = srs_wt, type = 'ecological')

Description

spatial rough set-based interaction detector

Usage

srs_interaction_detector(y, x1, x2, wt)

Arguments

Value

A list.

Variable1 PD

the average local explanatory power for variable1

Variable2 PD

the average local explanatory power for variable2

Variable1 and Variable2 interact PD

the average local explanatory power for variable1 and variable2 interact

Variable1 SE_PD

the degree of spatial heterogeneity of the local explanatory power for variable1

Variable2 SE_PD

the degree of spatial heterogeneity of the local explanatory power for variable2

Variable1 and Variable2 SE_PD

the degree of spatial heterogeneity of the local explanatory power for variable1 and variable2 interact

Interaction

the interact result type

References

Bai, H., Li, D., Ge, Y., Wang, J., & Cao, F. (2022). Spatial rough set-based geographical detectors for nominal target variables. Information Sciences, 586, 525–539. https://doi.org/10.1016/j.ins.2021.12.019

Examples

data('srs_table')
data('srs_wt')
srs_interaction_detector(srs_table$d,srs_table$a1,srs_table$a2,srs_wt)

Description

example of spatial information system table

Usage

srs_table

Format

srs_table: A tibble with 11 rows and 5 variables(one ID column).

Description

example of spatial information system spatial adjacency matrix

Usage

srs_wt

Format

srs_wt: A matrix with 11rows and 11cols.

Description

spatial rough set-based geographical detector(SRSGD) model

Usage

srsgd(formula, data, wt = NULL, type = "factor", alpha = 0.95)

Arguments

Value

A list.

factor

the result of spatial rough set-based factor detector

interaction

the result of spatial rough set-based interaction detector

ecological

the result of spatial rough set-based ecological detector

Note

The Spatial Rough Set-based Geographical Detector Model (SRSGD) conducts spatial hierarchical heterogeneity analysis utilizing a geographical detector for data where the dependent variable is discrete. Given the complementary relationship between SRSGD and the native version of geographical detector, I strive to maintain consistency with gd() function when establishing srsgd() function. This implies that all input variable data in srsgd must be discretized prior to use.

References

Bai, H., Li, D., Ge, Y., Wang, J., & Cao, F. (2022). Spatial rough set-based geographical detectors for nominal target variables. Information Sciences, 586, 525–539. https://doi.org/10.1016/j.ins.2021.12.019

Examples

data('srs_table')
data('srs_wt')
srsgd(d ~ a1 + a2 + a3, data = srs_table, wt = srs_wt,
      type = c('factor','interaction','ecological'))

Description

assign values by weight

Usage

weight_assign(x, w, list = FALSE)

Arguments

Value

A numeric Vector.

Examples

weight_assign(0.875,1:3)