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comparison of size effects of spatial units based on OPGD

Source: R/sesu_opgd.R
sesu_opgd.Rd

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,
  ...
)

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().

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

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.

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

if (FALSE) { # \dontrun{
## 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)
} # }