interactive detector for spatial associations(IDSA) model

Function for interactive detector for spatial associations model.

Usage

idsa(
  formula,
  data,
  wt = NULL,
  discnum = 3:8,
  discmethod = "quantile",
  overlay = "and",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  alpha = 0.95,
  ...
)

Arguments

formula: A formula of IDSA model.
data: A data.frame, tibble or sf object of observation data.
wt: (optional) The spatial weight matrix. When data is not an sf object, must provide wt.
discnum: (optional) Number of multilevel discretization. Default will use 3:8.
discmethod: (optional) The discretization methods. Default all use quantile. Noted that robust will use robust_disc(); rpart will use rpart_disc(); Others use sdsfun::discretize_vector().
overlay: (optional) Spatial overlay method. One of and, or, intersection. Default is and.
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) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
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.

interaction: the interaction result of IDSA model
risk: whether values of the response variable between a pair of overlay zones are significantly different
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

Please note that all variables in the IDSA model need to be continuous data.

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.

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

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = idsa(y ~ ., data = sim1)
g
#> ***     Interactive Detector For Spatial Associations 
#> 
#> |   variable   |    PID    |
#> |:------------:|:---------:|
#> |   xa ∩ xb    | 0.5540009 |
#> |   xb ∩ xc    | 0.5467294 |
#> |      xc      | 0.5411351 |
#> |   xa ∩ xc    | 0.4515003 |
#> | xa ∩ xb ∩ xc | 0.4275805 |
#> 
#>  --------- IDSA model performance evaluation: --------
#>  * Number of overlay zones :  9 
#>  * Percentage of finely divided zones :  0 
#>  * Number of individual explanatory variables :  2 
#>  
#>  ## Different of response variable between a pair of overlay zones:
#> 
#> | zone1st  | zone2nd  | Risk |
#> |:--------:|:--------:|:----:|
#> | zonexa_1 | zonexa_2 |  No  |
#> | zonexa_1 | zonexa_3 | Yes  |
#> | zonexa_1 | zonexa_4 |  No  |
#> | zonexa_1 | zonexb_1 | Yes  |
#> | zonexa_1 | zonexb_2 |  No  |
#> 
#>  #### Only the first five pairs of interactions and overlay zones are displayed! ####