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Optimal Parameters-based GeographicalDetector(OPGD)

Load data and package

library(gdverse)data("ndvi")head(ndvi)## # A tibble: 6 × 7##   NDVIchange Climatezone Mining Tempchange Precipitation   GDP Popdensity##        <dbl> <chr>       <fct>       <dbl>         <dbl> <dbl>      <dbl>## 1    0.116   Bwk         low         0.256          237. 12.6      1.45## 2    0.0178  Bwk         low         0.273          214.  2.69     0.801## 3    0.138   Bsk         low         0.302          449. 20.1     11.5## 4    0.00439 Bwk         low         0.383          213.  0        0.0462## 5    0.00316 Bwk         low         0.357          205.  0        0.0748## 6    0.00838 Bwk         low         0.338          201.  0        0.549

Determine optimal discretization parameters

Climatezone andMining is the discretecategory variables in thendvi data, we need to discretizeothers. We can usegd_optunidisc() to discretize them basedon geodetector q-statistic.

tictoc::tic()g=gd_optunidisc(NDVIchange~ .,data = dplyr::select(ndvi,-c(Climatezone,Mining)),discnum =3:15,cores =6)tictoc::toc()## 3.32 sec elapsed
g## $x## [1] "GDP"           "Popdensity"    "Precipitation" "Tempchange"#### $k## [1] 15 15 14 14#### $method## [1] "quantile" "quantile" "natural"  "equal"#### $qstatistic## [1] 0.1096213 0.2100134 0.8809405 0.3478433#### $disc## # A tibble: 713 × 4##      GDP Popdensity Precipitation Tempchange##    <int>      <int>         <int>      <int>##  1     8          6             5          3##  2     6          4             4          3##  3     8         11            10          3##  4     1          1             4          4##  5     1          2             4          3##  6     1          4             4          3##  7     8          6             4          3##  8     9          9             5          3##  9    12         12             7          3## 10     9         10             8          3## # ℹ 703 more rowsnew.ndvi= g$discnew.ndvi## # A tibble: 713 × 4##      GDP Popdensity Precipitation Tempchange##    <int>      <int>         <int>      <int>##  1     8          6             5          3##  2     6          4             4          3##  3     8         11            10          3##  4     1          1             4          4##  5     1          2             4          3##  6     1          4             4          3##  7     8          6             4          3##  8     9          9             5          3##  9    12         12             7          3## 10     9         10             8          3## # ℹ 703 more rows

Thenew.ndvi is the discreted result of the optimaldiscretization parameter based on the Q statistic of the geographicaldetector (factor detector), we can combine it with other columns inndvi tibble now.

new.ndvi= dplyr::bind_cols(dplyr::select(ndvi,NDVIchange,Climatezone,Mining),new.ndvi)new.ndvi## # A tibble: 713 × 7##    NDVIchange Climatezone Mining   GDP Popdensity Precipitation Tempchange##         <dbl> <chr>       <fct>  <int>      <int>         <int>      <int>##  1    0.116   Bwk         low        8          6             5          3##  2    0.0178  Bwk         low        6          4             4          3##  3    0.138   Bsk         low        8         11            10          3##  4    0.00439 Bwk         low        1          1             4          4##  5    0.00316 Bwk         low        1          2             4          3##  6    0.00838 Bwk         low        1          4             4          3##  7    0.0335  Bwk         low        8          6             4          3##  8    0.0387  Bwk         low        9          9             5          3##  9    0.0882  Bsk         low       12         12             7          3## 10    0.0690  Bsk         low        9         10             8          3## # ℹ 703 more rows

Run geodetector

Then, we can run geodetector model bygd() function.

gd(NDVIchange~ .,data = new.ndvi,type ='factor')##                 Factor Detector#### |   variable    | Q-statistic | P-value  |## |:-------------:|:-----------:|:--------:|## | Precipitation |  0.8809405  | 9.18e-10 |## |  Climatezone  |  0.8218335  | 7.34e-10 |## |  Tempchange   |  0.3478433  | 2.41e-10 |## |  Popdensity   |  0.2100134  | 1.61e-10 |## |    Mining     |  0.1411154  | 6.73e-10 |## |      GDP      |  0.1096213  | 4.18e-10 |gd(NDVIchange~ .,data = new.ndvi,type ='interaction')##                 Interaction Detector#### |    Interactive variable     |    Interaction     |## |:---------------------------:|:------------------:|## |    Climatezone ∩ Mining     |    Enhance, bi-    |## |      Climatezone ∩ GDP      |    Enhance, bi-    |## |  Climatezone ∩ Popdensity   |    Enhance, bi-    |## | Climatezone ∩ Precipitation |    Enhance, bi-    |## |  Climatezone ∩ Tempchange   |    Enhance, bi-    |## |        Mining ∩ GDP         | Enhance, nonlinear |## |     Mining ∩ Popdensity     | Enhance, nonlinear |## |   Mining ∩ Precipitation    |    Enhance, bi-    |## |     Mining ∩ Tempchange     |    Enhance, bi-    |## |      GDP ∩ Popdensity       |    Enhance, bi-    |## |     GDP ∩ Precipitation     |    Enhance, bi-    |## |      GDP ∩ Tempchange       | Enhance, nonlinear |## | Popdensity ∩ Precipitation  |    Enhance, bi-    |## |   Popdensity ∩ Tempchange   |    Enhance, bi-    |## | Precipitation ∩ Tempchange  |    Enhance, bi-    |

You can also useopgd() in one time to get resultabove.

ndvi_opgd=opgd(NDVIchange~ .,data = ndvi,discnum =3:15,discvar =names(dplyr::select(ndvi,-c(NDVIchange,Climatezone,Mining))),cores =6,type =c('factor','interaction'))ndvi_opgd## ***   Optimal Parameters-based Geographical Detector##                 Factor Detector#### |   variable    | Q-statistic | P-value  |## |:-------------:|:-----------:|:--------:|## | Precipitation |  0.8809405  | 9.18e-10 |## |  Climatezone  |  0.8218335  | 7.34e-10 |## |  Tempchange   |  0.3478433  | 2.41e-10 |## |  Popdensity   |  0.2100134  | 1.61e-10 |## |    Mining     |  0.1411154  | 6.73e-10 |## |      GDP      |  0.1096213  | 4.18e-10 |####                 Interaction Detector#### |    Interactive variable     |    Interaction     |## |:---------------------------:|:------------------:|## |    Climatezone ∩ Mining     |    Enhance, bi-    |## |      Climatezone ∩ GDP      |    Enhance, bi-    |## |  Climatezone ∩ Popdensity   |    Enhance, bi-    |## | Climatezone ∩ Precipitation |    Enhance, bi-    |## |  Climatezone ∩ Tempchange   |    Enhance, bi-    |## |        Mining ∩ GDP         | Enhance, nonlinear |## |     Mining ∩ Popdensity     | Enhance, nonlinear |## |   Mining ∩ Precipitation    |    Enhance, bi-    |## |     Mining ∩ Tempchange     |    Enhance, bi-    |## |      GDP ∩ Popdensity       |    Enhance, bi-    |## |     GDP ∩ Precipitation     |    Enhance, bi-    |## |      GDP ∩ Tempchange       | Enhance, nonlinear |## | Popdensity ∩ Precipitation  |    Enhance, bi-    |## |   Popdensity ∩ Tempchange   |    Enhance, bi-    |## | Precipitation ∩ Tempchange  |    Enhance, bi-    |

You can useplot() to visualize the result.

plot(ndvi_opgd)

Use$opt_param to get the optimal discretizationparameter in OPGD model:

ndvi_opgd$opt_param## # A tibble: 4 × 3##   varibale      discnum method##   <chr>           <int> <chr>## 1 GDP                15 quantile## 2 Popdensity         15 quantile## 3 Precipitation      14 natural## 4 Tempchange         14 equal
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