sesu_opgd {gdverse} | R Documentation |
comparison of size effects of spatial units based on OPGD
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
formula |
A formula of comparison of size effects of spatial units. |
datalist |
A list of |
su |
A vector of sizes of spatial units. |
discvar |
Name of continuous variable columns that need to be discretized.Noted that
when |
discnum |
(optional) A vector of number of classes for discretization. Default is |
discmethod |
(optional) A vector of methods for discretization, default is using
|
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 |
alpha |
(optional) Specifies the size of confidence level. Default is |
... |
(optional) Other arguments passed to |
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
Author(s)
Wenbo Lv lyu.geosocial@gmail.com
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)