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

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.
cores: (optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
strategy: (optional) Calculation strategies of Q statistics at different scales. Default is 2L, see details for more contents.
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 rpart_disc().

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

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.

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

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_gozh(fvc ~ .,
          datalist = list(fvc1000,fvc5000),
          su = c(1000,5000),
          cores = 6)
} # }