idsa {gdverse}R Documentation

interactive detector for spatial associations(IDSA) model

Description

Function for interactive detector for spatial associations model.

Usage

idsa(
  formula,
  data,
  wt = NULL,
  overlaymethod = "and",
  locations = NULL,
  discnum = NULL,
  discmethod = NULL,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  alpha = 0.95,
  ...
)

Arguments

formula

A formula of IDSA model.

data

A data.frame or tibble of observation data.

wt

(optional) The spatial weight matrix. When wt is not provided, must provide locations. And gdverse will use locations columns to construct spatial weight use inverse_distance_weight().

overlaymethod

(optional) Spatial overlay method. One of and, or, intersection. Default is and.

locations

(optional) The spatial location coordinate columns name in data. Useful and must provided when wt is not provided. When wt is provided, locations is not need.

discnum

(optional) Number of multilevel discretization. Default will use 3:22.

discmethod

(optional) The discretization methods. Default all use quantile. Noted that robust will use robust_disc(); rpart will use rpart_disc(); Others use st_unidisc(). You can try unidisc_methods() to see supported methods in st_unidisc().

strategy

(optional) Discretization strategy. When strategy is 1L, choose the highest SPADE model q-statistics to determinate optimal spatial data discretization parameters. When strategy is 2L, The optimal discrete parameters of spatial data are selected by combining LOESS model.

increase_rate

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

cores

(optional) A positive integer(default is 1). If cores > 1, a 'parallel' package cluster with that many cores is created and used. You can also supply a cluster object.

seed

(optional) Random number seed, default is 123456789.

alpha

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

...

(optional) Other arguments passed to cpsd_disc().

Value

A list with PID values tibble under different spatial overlays and performance evaluation indicators.

interaction

the interaction result of IDSA model

risk1

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

risk2

risk detection result of the input data

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

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.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

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')
g = idsa(y ~ ., data = sim,
         locations = c('lo','la'),
         discvar = c("xa","xb","xc"))
g


[Package gdverse version 1.0.0 Index]