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r - 平滑ggplot2 map

以前的帖子

Cleaning up a map using geom_tile

Get boundaries to come through on states

问题/疑问

我正在尝试平滑一些数据以使用ggplot2进行映射。感谢@MrFlick和@hrbrmstr，我取得了许多进步，但是在获得我需要列出的状态的“渐变”效果时遇到了问题。

这是一个示例，可让您大致了解我要查找的内容：

****这正是我要实现的目标。

http://nrelscience.org/2013/05/30/this-is-how-i-did-it-mapping-in-r-with-ggplot2/

（1）如何利用数据充分利用ggplot2？

（2）有没有更好的方法来达到渐变效果？

目标

我要从这个赏金中实现的目标是：

（1）内插数据以构建栅格对象，然后使用ggplot2进行绘制

（或者，如果可以对当前绘图执行更多操作，并且栅格对象不是一个好的策略）

（2）使用ggplot2构建更好的地图

当前结果

我一直在玩很多这些不同的情节，但由于两个原因，我仍然对结果不满意：（1）梯度的显示不尽如人意； （2）尽管我不确定该如何做，但可以改进演示文稿。

正如@hrbrmstr所指出的，如果我对数据进行插值以生成更多数据，然后将其拟合到栅格对象中并使用ggplot2进行绘制，则可能会提供更好的结果。我认为这是我现在应该追求的目标，但是鉴于我拥有的数据，我不确定如何做到这一点。

我在下面列出了到目前为止我对结果所做的代码。我真的很感激此事。谢谢。

数据集

这是两个数据集：

（1）完整数据集（175 mb）：PRISM_1895_db_all.csv（不可用）

https://www.dropbox.com/s/uglvwufcr6e9oo6/PRISM_1895_db_all.csv?dl=0

（2）部分数据集（14 mb）：PRISM_1895_db.csv（不可用）

https://www.dropbox.com/s/0evuvrlm49ab9up/PRISM_1895_db.csv?dl=0

***编辑：对于那些感兴趣的人，数据集不可用，但是我在我的网站上发布了一篇文章，将该代码与http://johnwoodill.com/pages/r-code.html处的加利福尼亚数据子集连接起来

剧情1

PRISM_1895_db <- read.csv("/.../PRISM_1895_db.csv")

regions<- c("north dakota","south dakota","nebraska","kansas","oklahoma","texas","minnesota","iowa","missouri","arkansas", "illinois", "indiana", "wisconsin")

ggplot() +
  geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group)) +
  geom_point(data = PRISM_1895_db, aes(x = longitude, y = latitude, color = APPT), alpha = .5, size = 5) +
  geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group), color="white", fill=NA) +
  coord_equal()




剧情2

PRISM_1895_db 
regions<- c("north dakota","south dakota","nebraska","kansas","oklahoma","texas","minnesota","iowa","missouri","arkansas", "illinois", "indiana", "wisconsin")

ggplot() +
    geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group)) +
    geom_point(data = PRISM_1895_db, aes(x = longitude, y = latitude, color = APPT), alpha = .5, size = 5, shape = 15) +
    geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group), color="white", fill=NA) +
    coord_equal()




情节3

   PRISM_1895_db <- read.csv("/.../PRISM_1895_db.csv")

    regions<- c("north dakota","south dakota","nebraska","kansas","oklahoma","texas","minnesota","iowa","missouri","arkansas", "illinois", "indiana", "wisconsin")

ggplot() +
  geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group)) +
  stat_summary2d(data=PRISM_1895_db, aes(x = longitude, y = latitude, z = APPT)) +
  geom_polygon(data=subset(map_data("state"), region %in% regions), aes(x=long, y=lat, group=group), color="white", fill=NA)

最佳答案

CRAN spatial view让我开始学习“克里金”。下面的代码需要大约7分钟才能在笔记本电脑上运行。您可以尝试使用更简单的插值（例如某种样条线）。您也可以从高密度区域中删除一些位置。您不需要所有这些点即可获得相同的热图。据我所知，用ggplot2创建真正的渐变没有简单的方法（gridSVG有几个选项，但没有像花哨的SVG编辑器中那样的“网格渐变”）。



根据要求，这是使用样条线进行插值的速度（快得多）。许多代码取自Plotting contours on an irregular grid。



克里金法则代码：

library(data.table)
library(ggplot2)
library(automap)

# Data munging
states=c("AR","IL","MO")
regions=c("arkansas","illinois","missouri")
PRISM_1895_db = as.data.frame(fread("./Downloads/PRISM_1895_db.csv"))
sub_data = PRISM_1895_db[PRISM_1895_db$state %in% states,c("latitude","longitude","APPT")]
coord_vars = c("latitude","longitude")
data_vars = setdiff(colnames(sub_data), coord_vars)
sp_points = SpatialPoints(sub_data[,coord_vars])
sp_df = SpatialPointsDataFrame(sp_points, sub_data[,data_vars,drop=FALSE])

# Create a fine grid
pixels_per_side = 200
bottom.left = apply(sp_points@coords,2,min)
top.right = apply(sp_points@coords,2,max)
margin = abs((top.right-bottom.left))/10
bottom.left = bottom.left-margin
top.right = top.right+margin
pixel.size = abs(top.right-bottom.left)/pixels_per_side
g = GridTopology(cellcentre.offset=bottom.left,
             cellsize=pixel.size,
             cells.dim=c(pixels_per_side,pixels_per_side))

# Clip the grid to the state regions
map_base_data = subset(map_data("state"), region %in% regions)
colnames(map_base_data)[match(c("long","lat"),colnames(map_base_data))] = c("longitude","latitude")
foo = function(x) {
  state = unique(x$region)
  print(state)
  Polygons(list(Polygon(x[,c("latitude","longitude")])),ID=state)
}
state_pg = SpatialPolygons(dlply(map_base_data, .(region), foo))
grid_points = SpatialPoints(g)
in_points = !is.na(over(grid_points,state_pg))
fit_points = SpatialPoints(as.data.frame(grid_points)[in_points,])

# Do kriging
krig = autoKrige(APPT~1, sp_df, new_data=fit_points)
interp_data = as.data.frame(krig$krige_output)
colnames(interp_data) = c("latitude","longitude","APPT_pred","APPT_var","APPT_stdev")

# Set up map plot
map_base_aesthetics = aes(x=longitude, y=latitude, group=group)
map_base = geom_polygon(data=map_base_data, map_base_aesthetics)
borders = geom_polygon(data=map_base_data, map_base_aesthetics, color="black", fill=NA)

nbin=20
ggplot(data=interp_data, aes(x=longitude, y=latitude)) +
  geom_tile(aes(fill=APPT_pred),color=NA) +
  stat_contour(aes(z=APPT_pred), bins=nbin, color="#999999") +
  scale_fill_gradient2(low="blue",mid="white",high="red", midpoint=mean(interp_data$APPT_pred)) +
  borders +
  coord_equal() +
  geom_point(data=sub_data,color="black",size=0.3)


样条插补代码：

library(data.table)
library(ggplot2)
library(automap)
library(plyr)
library(akima)

# Data munging
sub_data = as.data.frame(fread("./Downloads/PRISM_1895_db_all.csv"))
coord_vars = c("latitude","longitude")
data_vars = setdiff(colnames(sub_data), coord_vars)
sp_points = SpatialPoints(sub_data[,coord_vars])
sp_df = SpatialPointsDataFrame(sp_points, sub_data[,data_vars,drop=FALSE])

# Clip the grid to the state regions
regions<- c("north dakota","south dakota","nebraska","kansas","oklahoma","texas",
            "minnesota","iowa","missouri","arkansas", "illinois", "indiana", "wisconsin")
map_base_data = subset(map_data("state"), region %in% regions)
colnames(map_base_data)[match(c("long","lat"),colnames(map_base_data))] = c("longitude","latitude")
foo = function(x) {
  state = unique(x$region)
  print(state)
  Polygons(list(Polygon(x[,c("latitude","longitude")])),ID=state)
}
state_pg = SpatialPolygons(dlply(map_base_data, .(region), foo))

# Set up map plot
map_base_aesthetics = aes(x=longitude, y=latitude, group=group)
map_base = geom_polygon(data=map_base_data, map_base_aesthetics)
borders = geom_polygon(data=map_base_data, map_base_aesthetics, color="black", fill=NA)

# Do spline interpolation with the akima package
fld = with(sub_data, interp(x = longitude, y = latitude, z = APPT, duplicate="median",
                            xo=seq(min(map_base_data$longitude), max(map_base_data$longitude), length = 100),
                            yo=seq(min(map_base_data$latitude), max(map_base_data$latitude), length = 100),
                            extrap=TRUE, linear=FALSE))
melt_x = rep(fld$x, times=length(fld$y))
melt_y = rep(fld$y, each=length(fld$x))
melt_z = as.vector(fld$z)
level_data = data.frame(longitude=melt_x, latitude=melt_y, APPT=melt_z)
interp_data = na.omit(level_data)
grid_points = SpatialPoints(interp_data[,2:1])
in_points = !is.na(over(grid_points,state_pg))
inside_points = interp_data[in_points, ]

ggplot(data=inside_points, aes(x=longitude, y=latitude)) +
  geom_tile(aes(fill=APPT)) +
  stat_contour(aes(z=APPT)) +
  coord_equal() +
  scale_fill_gradient2(low="blue",mid="white",high="red", midpoint=mean(inside_points$APPT)) +
  borders

关于r - 平滑ggplot2 map ，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/28059182/

10-13 07:01
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