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我有一个名为“seoul032823”的 81 次观测的每小时 PM10 数据集。您可以从这里下载。我在这个数据集上执行了普通的克里金法，还得到了用于克里金法预测的空间图。我还可以在国家地图上显示观察数据点。但我不能在国家地图上重叠克里金空间预测图。

我想做什么：我想在韩国地图（不是整个韩国）上重叠我的空间预测地图。我感兴趣的区域是纬度 37.2N 到 37.7N 和经度 126.6E 到 127.2E。这意味着我需要从韩国地图上裁剪这个区域并将预测地图重叠在上面。我还需要根据浓度值显示原始观察数据点，这些数据点将遵循空间图的颜色。例如，我想要这种类型的地图： 在此处输入图像描述

我的克里金 R 代码，并在韩国地图上显示数据点：

library(sp)
library(gstat)
library(automap)
library(rgdal)
library(e1071)
library(dplyr)
library(lattice)

seoul032823 <- read.csv ("seoul032823.csv")

#plotting the pm10 data on Korea Map
library(ggplot2)
library(raster)

seoul032823 <- read.csv ("seoul032823.csv")
skorea<- getData("GADM", country= "KOR", level=1)
plot(skorea)

skorea<- fortify(skorea)
ggplot()+
  geom_map(data= skorea, map= skorea, aes(x=long,y=lat,map_id=id,group=group),
           fill=NA, colour="black") +
  geom_point(data=seoul032823, aes(x=LON, y=LAT), 
             colour= "red", alpha=0.7,na.rm=T) +
  #scale_size(range=c(2,4))+
  labs(title= "PM10 Concentration in Seoul Area at South Korea",
       x="Longitude", y= "Latitude", size="PM10(microgm/m3)")+
  theme(title= element_text(hjust = 0.5,vjust = 1,face= c("bold")))

# Reprojection
coordinates(seoul032823) <- ~LON+LAT
proj4string(seoul032823) <- "+proj=longlat +datum=WGS84" 
seoul032823 <- spTransform(seoul032823, CRS("+proj=utm +north +zone=52 +datum=WGS84"))

#Creating the grid for Kriging
LON.range <- range(as.integer(seoul032823@coords[,1 ])) + c(0,1)
LAT.range <- range(as.integer(seoul032823@coords[,2 ]))
seoul032823.grid <- expand.grid(LON = seq(from = LON.range[1], to = LON.range[2], by = 1500),
                                LAT = seq(from = LAT.range[1], to = LAT.range[2], by = 1500))
plot(seoul032823.grid)
points(seoul032823, pch= 16,col="red")
coordinates(seoul032823.grid)<- ~LON+LAT
gridded(seoul032823.grid)<- T
plot(seoul032823.grid)
points(seoul032823, pch= 16,col="red")

# kriging spatial prediction map
seoul032823_OK<- autoKrige(formula = PM10~1,input_data = seoul032823, new_data = seoul032823.grid )
pts.s <- list("sp.points", seoul032823, col = "red", pch = 16)
automapPlot(seoul032823_OK$krige_output, "var1.pred", asp = 1,
            sp.layout = list(pts.s), main = " Kriging Prediction")

我已经使用automap包进行克里金法和ggplot2绘制韩国地图。

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1 回答 1

6

我对空间分析不太熟悉，所以投影可能存在问题。

首先，根据引用Zev Ross的答案，ggplot2 与 data.frames 与空间对象相比效果更好。知道了这一点，我们可以从您的克里金空间对象中提取克里金预测。其余的相对简单。您可能必须修复经度/纬度轴标签并确保最终输出的尺寸正确。（如果你这样做，我可以编辑/附加答案以包括这些额外的步骤。）seoul032823_OK

# Reprojection of skorea into same coordinates as sp objects
# Not sure if this is appropriate
coordinates(skorea) <- ~long+lat  #{sp} Convert to sp object
proj4string(skorea) <- "+proj=longlat +datum=WGS84" #{sp} set projection attributes
#{sp} Transform to new coordinate reference system
skorea <- spTransform(skorea, CRS("+proj=utm +north +zone=52 +datum=WGS84")) 

#Convert spatial objects into data.frames for ggplot2
myPoints <- data.frame(seoul032823)
myKorea <- data.frame(skorea)
#Extract the kriging output data into a dataframe.  This is the MAIN PART!
myKrige <- data.frame(seoul032823_OK$krige_output@coords, 
                      pred = seoul032823_OK$krige_output@data$var1.pred)   
head(myKrige, 3)  #Preview the data
#     LON     LAT     pred
#1 290853 4120600 167.8167
#2 292353 4120600 167.5182
#3 293853 4120600 167.1047

#OP's original plot code, adapted here to include kriging data as geom_tile
ggplot()+ theme_minimal() +
  geom_tile(data = myKrige, aes(x= LON, y= LAT, fill = pred)) +
  scale_fill_gradient2(name=bquote(atop("PM10", mu*g~m^-3)), 
                       high="red", mid= "plum3", low="blue", 
                       space="Lab", midpoint = median(myKrige$pred))  + 
  geom_map(data= myKorea, map= myKorea, aes(x=long,y=lat,map_id=id,group=group),
           fill=NA, colour="black") +
  geom_point(data=myPoints, aes(x=LON, y=LAT, fill=PM10), 
             shape=21, alpha=1,na.rm=T, size=3) +
  coord_cartesian(xlim= LON.range, ylim= LAT.range) +
  #scale_size(range=c(2,4))+
  labs(title= "PM10 Concentration in Seoul Area at South Korea",
       x="Longitude", y= "Latitude")+
  theme(title= element_text(hjust = 0.5,vjust = 1,face= c("bold")))

克里金法叠加在地图上

编辑： OP 要求映射到相同色阶的点，而不是fill="yellow"在geom_point(). 从视觉上看，这并没有添加任何内容，因为这些点与克里格背景融为一体，但代码是根据要求添加的。

Edit2：如果要在原始经纬度坐标中绘图，则需要将不同的图层转换为相同的坐标系。但是这种转换可能会导致不规则的网格不适用于geom_tile. 解决方案 1：stat_summary_2d对不规则网格上的数据进行分箱和平均或解决方案 2：绘制大正方形点。

#Reproject the krige data
myKrige1 <- myKrige
coordinates(myKrige1) <- ~LON+LAT 
proj4string(myKrige1) <-"+proj=utm +north +zone=52 +datum=WGS84" 
myKrige_new <- spTransform(myKrige1, CRS("+proj=longlat")) 
myKrige_new <-  data.frame(myKrige_new@coords, pred = myKrige_new@data$pred) 
LON.range.new <- range(myKrige_new$LON) 
LAT.range.new <- range(myKrige_new$LAT)

#Original seoul data have correct lat/lon data
seoul <- read.csv ("seoul032823.csv")   #Reload seoul032823 data

#Original skorea data transformed the same was as myKrige_new
skorea1 <- getData("GADM", country= "KOR", level=1)
#Convert SpatialPolygonsDataFrame to dataframe (deprecated.  see `broom`)
skorea1 <- fortify(skorea1)  
coordinates(skorea1) <- ~long+lat  #{sp} Convert to sp object
proj4string(skorea1) <- "+proj=longlat +datum=WGS84" #{sp} set projection attributes 1
#{sp} Transform to new coordinate reference system
myKorea1 <- spTransform(skorea1, CRS("+proj=longlat")) 
myKorea1 <- data.frame(myKorea1)  #Convert spatial object to data.frame for ggplot

ggplot()+ theme_minimal() +
  #SOLUTION 1:
  stat_summary_2d(data=myKrige_new, aes(x = LON, y = LAT, z = pred),
                  binwidth = c(0.02,0.02)) +
  #SOLUTION 2: Uncomment the line(s) below:
  #geom_point(data = myKrige_new, aes(x= LON, y= LAT, fill = pred),
  #           shape=22, size=8, colour=NA) + 
  scale_fill_gradient2(name=bquote(atop("PM10", mu*g~m^-3)), 
                       high="red", mid= "plum3", low="blue", 
                       space="Lab", midpoint = median(myKrige_new$pred)) + 
  geom_map(data= myKorea1, map= myKorea1, aes(x=long,y=lat,map_id=id,group=group),
           fill=NA, colour="black") +
  geom_point(data= seoul, aes(x=LON, y=LAT, fill=PM10), 
             shape=21, alpha=1,na.rm=T, size=3) +
  coord_cartesian(xlim= LON.range.new, ylim= LAT.range.new) +
  #scale_size(range=c(2,4))+
  labs(title= "PM10 Concentration in Seoul Area at South Korea",
       x="Longitude", y= "Latitude")+
  theme(title= element_text(hjust = 0.5,vjust = 1,face= c("bold")))

克里格覆盖地图与原始纬度

于 2016-04-09T09:02:29.190 回答