干货分享 | 地理探测器R语言包使用方法（中文版）

使用“GD”包，请引用:

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

1

关于“GD”包

地理探测器可解决以下问题：

从空间异质性角度探索潜在因素或解释变量。

探索地理变量的潜在交互影响。

从潜在的解释变量中识别高风险或低风险区域。

GD包可快速实现以下分析：

它包含有多种监督和非监督空间数据离散化方法，以及连续变量的最优空间离散化一步到位的解决方案；

它包含地理探测器的四个主要功能，包括因子探测器，风险探测器，交互探测器和生态探测器；

可用于比较空间单元的尺度影响；

提供空间分析结果的多种可视化函数；

包含地理探测器每个空间分析步骤中详细的显著性分析结果。

一步到位的地理探测器分析函数 gdm (推荐)

“GD”提供了一个一步到位的空间数据离散化和地理探测器分析的函数gdm。该函数的输出为所有计算步骤的结果数据和全部可视化结果。

例如：

## install and library the pacakge
install.packages("GD")
library("GD")

## Example 1
## NDVI: ndvi_40
## set optional parameters of optimal discretization
## optional methods: equal, natural, quantile, geometric, sd and manual
discmethod <- c("equal","natural","quantile")
discitv <- c(4:6)
## "gdm" function
## In this case, Climatezone and Mining are categorical variables,
## and Tempchange and GDP are continuous variables.
ndvigdm <- gdm(NDVIchange ~ Climatezone + Mining + Tempchange + GDP,continuous_variable = c("Tempchange", "GDP"),
           data = ndvi_40,
           discmethod = discmethod, discitv = discitv) # ~3s
ndvigdm
plot(ndvigdm)

## Example 2
## H1N1: h1n1_100
## set optional parameters of optimal discretization
discmethod<-c("equal","natural","quantile","geometric","sd")
discitv <- c(3:7)
continuous_variable<-colnames(h1n1_100)[-c(1,11)]
## "gdm" function
h1n1gdm <- gdm(H1N1 ~ .,
       continuous_variable = continuous_variable,
       data = h1n1_100,
       discmethod = discmethod, discitv = discitv)
h1n1gdm
plot(h1n1gdm)

2

地理探测器模型集

地理探测器可以解决空间分层异质性问题 (Wang et al. 2010, Wang et al. 2016)。

图 1. 地理探测器应用趋势综述 (总引用数截至2020年6月). (Song et al. 2020)

图 2. 空间分析过程和“GD”包中函数的关系 (Song et al. 2020)

更多关于“GD”包的内容，请参考GD: Optimal Parameters-based Geographical Detectors for Assessing Spatial Factors。

更多关于地理探测器的应用，请参考Geodetector website.

3

 空间数据离散化

在地理探测器中，连续变量需要通过空间数据离散化的方式转化为类别变量。“GD”提供了两种解决方案。第一，使用已知的离散化方法和数量，用disc函数离散化。第二种方法是用optidisc对一系列备选的离散化方法和数量，通过对比的方式，选择最优的离散化方法和数量的组合。

下面以“GD”中提供的ndvi_40数据为例说明这两种方法如何使用。

install.packages("GD")
library("GD")
data("ndvi_40")
head(ndvi_40)[1:3,]

根据已知参数离散化: disc

## discretization methods: equal, natural, quantile (default), geometric, sd and manual ds1 <- disc(ndvi_40$Tempchange, 4)
ds1
plot(ds1)

最优空间离散化: optidisc

## set optional discretization methods and numbers of intervals discmethod <- c("equal","natural","quantile","geometric","sd")
discitv <- c(4:7)
## optimal discretization
odc1<-optidisc(NDVIchange~ Tempchange, data = ndvi_40,discmethod, discitv)
odc1
plot(odc1)

图 3. 最优空间数据离散化过程和方法

4

地理探测器

因子探测器: gd

## a categorical explanatory variable
g1 <- gd(NDVIchange ~ Climatezone, data = ndvi_40)
g1

## multiple categorical explanatory variables
g2 <- gd(NDVIchange ~ ., data = ndvi_40[,1:3])
g2
plot(g2)

## multiple variables including continuous variables
discmethod<-c("equal","natural","quantile","geometric","sd")
discitv <- c(3:7)
data.ndvi <- ndvi_40

data.continuous <- data.ndvi[, c(1, 4:7)]
odc1 <- optidisc(NDVIchange ~ ., data = data.continuous, discmethod, discitv) # ~14s
data.continuous <- do.call(cbind, lapply(1:4, function(x)
 data.frame(cut(data.continuous[, -1][, x], unique(odc1[[x]]$itv), include.lowest = TRUE))))
   # add stratified data to explanatory variables
data.ndvi[, 4:7] <- data.continuous

g3 <- gd(NDVIchange ~ ., data = data.ndvi)
g3
plot(g3)

图 4. 因子探测器结果

风险探测器: riskmean 和 gdrisk

解释变量的风险均值:

## categorical explanatory variables
rm1 <- riskmean(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
rm1
plot(rm1)
## multiple variables inclusing continuous variables
rm2 <- riskmean(NDVIchange ~ ., data = data.ndvi)
rm2
plot(rm2)

风险矩阵:

## categorical explanatory variables
gr1 <- gdrisk(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
gr1
plot(gr1)
## multiple variables inclusing continuous variables
gr2 <- gdrisk(NDVIchange ~ ., data = data.ndvi)
gr2
plot(gr2)

图 5. 风险探测器结果

交互探测器: gdinteract

## categorical explanatory variables
gi1 <- gdinteract(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
gi1
## multiple variables inclusing continuous variables
gi2 <- gdinteract(NDVIchange ~ ., data = data.ndvi)
gi2
plot(gi2)

图 6. 交互探测器结果

生态探测器: gdeco

## categorical explanatory variables
ge1 <- gdeco(NDVIchange ~ Climatezone + Mining, data = ndvi_40)
ge1
## multiple variables inclusing continuous variables
gd3 <- gdeco(NDVIchange ~ ., data = data.ndvi)
gd3
plot(gd3)

图 7. 生态探测器结果

5. 空间尺度影响分析

ndvilist <- list(ndvi_20, ndvi_30, ndvi_40, ndvi_50)
su <- c(20,30,40,50) ## sizes of spatial units
## "gdm" function
gdlist <- lapply(ndvilist, function(x){
 gdm(NDVIchange ~ Climatezone + Mining + Tempchange + GDP, continuous_variable = c("Tempchange", "GDP"),
   data = x, discmethod = "quantile", discitv = 6)
})
sesu(gdlist, su) ## size effects of spatial units

图 8. 空间尺度影响分析结果

参考文献

Song Y, Wang J, Ge Y and 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), pp. 593-610. doi: 10.1080/15481603.2020.1760434.

Wang J, Li X, Christakos G, Liao Y, Zhang T, Gu X and Zheng X (2010). “Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China.” International Journal of Geographical Information Science, 24(1), pp. 107-127. doi: 10.1080/13658810802443457.

Wang J, Zhang T and Fu B (2016). “A measure of spatial stratified heterogeneity.” Ecological Indicators,67,pp.250-256.doi: 10.1016/j.ecolind.2016.02.052.

排版：张永红

责任编辑：王佳雯

审编：鲁嘉颐

终审： 顾伟男   田巍   梁龙武

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