Z - R 中多边形(shapefile)的值 [英] Z - Values for polygon (shapefile) in R

问题描述

我的目标是在 R 中创建一个 3D 可视化.我有一个柏林市区 (Ortsteile) 的 shapefile，并希望将值 (居民/平方公里) 突出显示为 z 值.我已经将 shapefile 实现到 R 中，并为 desnity(Einwohnerd")的值着色如下:

my goal is to create a 3D-Visualization in R. I have a shapefile of urban districts (Ortsteile) in Berlin and want to highlight the value (inhabitants/km²) as a z-value. I have implemented the shapefile into R and coloured the value for desnity ("Einwohnerd") as followed:

library(rgdal)
library(sp)

berlin=readOGR(dsn="C...etc.", layer="Ortsteile")

berlin@data

col <- rainbow(length(levels(berlin@data$Name)))
spplot(berlin, "Einwohnerd", col.regions=col, main="Ortsteil Berlins", sub="Datensatz der Stadt Berlin", lwd=.8, col="black")

如何将某个多边形(市区)引用到 z 值(居民/平方公里)以及如何突出显示该 z 值?

How it is posible to refer a certain polygon (urban district) to a z-value (inhabitant/km²) and how can I highlight this z-value?

希望有人能给出答案！最好的问候SB

Hope that someone will have an answer! Best regars SB

感谢您的回答，但我仍在努力寻找使用密度作为 z 值的最佳方法，以便我可以创建 3D 模型.我发现无法使用形状的多边形，但可以栅格化多边形并使用矩阵进行不同的透视和旋转.

Thanks for the answer, but I am still on my wy to find out the best to use the density as z-value so that I can create a 3D Model. I found out that it is not possible to use the polygons of the shape but that it is possible to rasterize the polygon and to use a matrix for a different perspective and rotation.

这是代码，但最终的 3D 可视化看起来不够清晰和不够好.也许最好以另一种方式计算 z 值，这样第一个值就不会那么高，或者使用多边形的中心，而不是在 z 方向上绘制一列:

Here is the code but the final 3D visualization looks not sharp and good enough. Maybe it would be better to calculate the the z-value in anther way so that the first values did not start so high or to use the center of the polygon and than to draw a column in z-direction:

library(rgdal)
library(sp)

setwd("C:\...")
berlin=readOGR(dsn="C:\...\Ortsteile", layer="Ortsteile") 

col <- rainbow(length(levels(berlin@data$Name)))  
spplot(berlin, "Einwohnerd", col.regions=col, main="Ortsteil Berlins",                 
sub="Datensatz    der Stadt Berlin", lwd=.8, col="black")

library(raster)

raster <- raster(nrows=100, ncols=200, extent(berlin)) 

test <- rasterize(berlin, raster, field="Einwohnerd")

persp(test, theta = 40, phi = 40, col = "gold", border = NA, shade = 0.5)  

for(i in seq(0,90,10)){     
persp(test, theta = 40, phi = i, col = "gold", border = NA, shade = 0.5)
}

library(rgl)         
library(colorRamps)
mat <- matrix(test[], nrow=test@nrows, byrow=TRUE)
image(mat)
persp3d(z = mat, clab = "m")
persp3d(z = mat, col = rainbow(10),border = "black")
persp3d(z = mat, facets = FALSE, curtain = TRUE)

推荐答案

这是你的想法吗?

library(ggplot2)
library(rgdal)           # for readOGR(...) and spTransform(...)
library(RColorBrewer)    # for brewer.pal(...)

setwd("<directory with shapefile>")
map <- readOGR(dsn=".",layer="Ortsteile")
map <- spTransform(map,CRS=CRS("+init=epsg:4839"))
map.data <- data.frame(id=rownames(map@data), map@data)
map.df   <- fortify(map)
map.df   <- merge(map.df,map.data,by="id")
ggplot(map.df, aes(x=long, y=lat, group=group))+
  geom_polygon(aes(fill=Einwohnerd))+
  geom_path(colour="grey")+
  scale_fill_gradientn(colours=rev(brewer.pal(10,"Spectral")))+
  theme(axis.text=element_blank())+
  labs(title="Berlin Ortsteile", x="", y="")+
  coord_fixed()

说明

这是一个很好的问题，因为它提供了一个在 R 中使用 ggplot 的非常基本的等值线图示例.

This is a great question, in that it provides an example of a very basic choropleth map using ggplot in R.

Shapefile 可以使用 readOGR(...) 读入 R，生成 SpatialDataFrame 对象.后者基本上有两个部分:包含多边形边界坐标的 polygons 部分和包含来自 shapefile 中的属性表的信息的 data 部分.这些可以分别引用为 map@polygons 和 map@data.

Shapefiles can be read into R using readOGR(...), producing SpatialDataFrame objects. The latter have basically two sections: a polygons section containing the coordinates of the polygon boundaries, and a data section containing information from the attributes table in the shapefile. These can be referenced, respectively, as map@polygons and map@data.

上面的代码读取 shapefile 并将坐标转换为 epsg:4839.然后我们在 map@data 中的其他信息前面添加多边形 id(存储在 rownames 中)，创建 map.data.然后我们使用 ggplot 中的 fortify(...) 函数将多边形转换为适合绘图的数据框(map.df).这个数据框有一个 id 列，它对应于 map.data 中的 id 列.然后我们根据id列将属性信息(map.data)合并到map.df中.

The code above reads the shapefile and transforms the coordinates to epsg:4839. Then we prepend the polygon ids (stored in the rownames) to the other information in map@data, creating map.data. Then we use the fortify(...) function in ggplot to convert the polygons to a dataframe suitable for plotting (map.df). This dataframe has a column id which corresponds to the id column in map.data. Then we merge the attribute information (map.data) into map.df based on the id column.

ggplot 调用创建地图图层并渲染地图，如下所示:

The ggplot calls create the map layers and render the map, as follows:

ggplot:       set the default dataset to map.df; identify x- and y-axis columns
geom_polygon: identify column for fill (color of polygon)
geom_path:    polygon boundaries
theme:        turn off axis text
labs:         title, turn off x- and y-axis labels
coord_fixed:  ensures that the map is not distorted

关于scale_fill_gradientn(...) 的说明:此函数通过插入colours= 参数中提供的调色板来为填充值分配颜色.这里我们使用来自 www.colorbrewer.org 的光谱调色板.不幸的是，这个调色板的颜色受到尊重(蓝色 - 红色)，所以我们使用 rev(...) 来反转颜色顺序(高 = 红色，低 = 蓝色).如果您更喜欢 matlab 中常见的更高饱和度的颜色，请使用 library(colorRamps) 并将对 scale_fill_gradientn(...) 的调用替换为:

A note on scale_fill_gradientn(...): this function assigns colors to the fill values by interpolating a color palette provided in the colours= parameter. Here we use the Spectral palette from www.colorbrewer.org. Unfotrunately, this palette has the colors revered (blue - red), so we use rev(...) to reverse the color order (high=red, low=blue). If you prefer the more highly saturated colors common in matlab, use library(colorRamps) and replace the call to scale_fill_gradientn(...) with:

  scale_fill_gradientn(colours=matlab.like(10))+

这篇关于Z - R 中多边形(shapefile)的值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！