gdverse (version 1.3-1)

sesu_opgd: comparison of size effects of spatial units based on OPGD

Description

Function for comparison of size effects of spatial units in spatial heterogeneity analysis based on optimal parameters geographical detector(OPGD) model.

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,
  ...
)

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

Arguments

formula

A formula of comparison of size effects of spatial units.

datalist

A list of data.frame or tibble.

su

A vector of sizes of spatial units.

discvar

Name of continuous variable columns that need to be discretized.Noted that when formula has discvar, data must have these columns.

discnum

(optional) A vector of number of classes for discretization. Default is 3:8.

discmethod

(optional) A vector of methods for discretization, default is using c("sd","equal","geometric","quantile","natural") by invoking sdsfun.

cores

(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.

increase_rate

(optional) The critical increase rate of the number of discretization. Default is 5%.

alpha

(optional) Specifies the size of confidence level. Default is 0.95.

...

(optional) Other arguments passed to gd_bestunidisc().

Author

Wenbo Lv

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.

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

if (FALSE) {
## 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)
}