Function for geographically optimal zones-based heterogeneity detector.
Arguments
- formula
A formula of GOZH detector.
- data
A data.frame or tibble of observation data.
- 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.
- type
(optional) The type of geographical detector,which must be one of
factor
(default),interaction
,risk
,ecological
.- alpha
(optional) Confidence level of the interval,default is
0.95
.- ...
(optional) Other arguments passed to
rpart_disc()
.
Value
A list of tibble with the corresponding result under different detector types.
factor
the result of factor detector
interaction
the result of interaction detector
risk
the result of risk detector
ecological
the result of ecological detector
References
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
data('ndvi')
g = gozh_detector(NDVIchange ~ ., data = ndvi)
g
#> Factor Detector
#>
#> | variable | Q-statistic | P-value |
#> |:-------------:|:-----------:|:--------:|
#> | Precipitation | 0.87255056 | 4.52e-10 |
#> | Climatezone | 0.82129550 | 2.50e-10 |
#> | Tempchange | 0.33324945 | 1.12e-10 |
#> | Popdensity | 0.22321863 | 3.00e-10 |
#> | Mining | 0.13982859 | 6.00e-11 |
#> | GDP | 0.09170153 | 3.96e-10 |