地图可视化绘制 | R-cartography 艺术地图绘制

DataCharm

发布于 2021-02-22 11:49:21

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发布于 2021-02-22 11:49:21

文章被收录于专栏：数据学术商业新闻数据学术商业新闻

本期推文我们介绍一个可以绘制颇具“艺术”风格地图的可视化包-cartography，主要涉及的内容如下：

R-cartography 简介
R-cartography 实例应用

R-cartography 简介

说到cartography包，用Python绘图的小伙伴可能会想到cartopy(Basemap的下一代地图可视化绘制包)，下面就简单介绍下cartography。

官网介绍

cartography官网如下：http://riatelab.github.io/cartography/docs/articles/cartography.html （可点击cartography官网）官方介绍如下：cartography包的目的是获得具有经典制图或GIS软件构建的主题图的视觉质量的主题图。用户可能属于以下两类之一：使用R的制图师或愿意创建地图的使用者。制图使用sf或sp对象生成基本图形。由于程序包的大多数内部结构都依赖于sf功能，因此空间对象的首选格式是sf。(官方直译的哈)

通过介绍我们可以知道，cartography主要基于sf对象进行绘图，所以我们在绘制之前需将数据(地图数据或者点数据)转换成sf对象。

可视化专题图介绍

cartography包官网提供了多种优秀的地图可视化绘制专题，这类可视化作品和一般的地图作品有些不一样，透露出一种“艺术 ”气息。这里我们列举几个比较样例供大家参考，更多样例可参看官网哦！

样例1

library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
path_to_gpkg <- system.file("gpkg/mtq.gpkg", package="cartography")
# import to an sf object
mtq <- st_read(dsn = path_to_gpkg, quiet = TRUE)
# download osm tiles
mtq.osm <- getTiles(
  x = mtq,
  type = "OpenStreetMap",
  zoom = 11,
  crop = TRUE
)
# plot osm tiles
tilesLayer(x = mtq.osm)
# plot municipalities (only borders are plotted)
plot(st_geometry(mtq), col = NA, border = "grey", add=TRUE)
# plot population
propSymbolsLayer(
  x = mtq,
  var = "POP",
  inches = 0.25,
  col = "brown4",
  legend.pos = "topright",
  legend.title.txt = "Total population"
)
# layout
layoutLayer(title = "Population Distribution in Martinique",
            sources = "Sources: Insee and IGN, 2018\n© OpenStreetMap contributors.\nTiles style under CC BY-SA, www.openstreetmap.org/copyright.",
            author = paste0("cartography ", packageVersion("cartography")),
            frame = FALSE, north = FALSE, tabtitle = TRUE)
# north arrow
north(pos = "topleft")

可视化结果如下：

地图散点图

样例2

library(sf)
library(cartography)
# path to the geopackage file embedded in cartography
path_to_gpkg <- system.file("gpkg/mtq.gpkg", package="cartography")
# import to an sf object
mtq <- st_read(dsn = path_to_gpkg, quiet = TRUE)
# transform municipality multipolygons to (multi)linestrings
mtq_pencil <- getPencilLayer(
  x = mtq,
  size = 400,
  lefthanded = F
)
# plot municipalities (only the backgroung color is plotted)
plot(st_geometry(mtq), col = "white", border = NA, bg = "lightblue1")
# plot administrative status
typoLayer(
  x = mtq_pencil,
  var="STATUS",
  col = c("aquamarine4", "yellow3","wheat"),
  lwd = .7,
  legend.values.order = c("Prefecture",
                          "Sub-prefecture",
                          "Simple municipality"),
  legend.pos = "topright",
  legend.title.txt = "",
  add = TRUE
)
#  plot municipalities
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)
# labels for a few  municipalities
labelLayer(x = mtq[mtq$STATUS != "Simple municipality",], txt = "LIBGEO",
           cex = 0.9, halo = TRUE, r = 0.15)
# title, source, author
layoutLayer(title = "Administrative Status",
            sources = "Sources: Insee and IGN, 2018",
            author = paste0("cartography ", packageVersion("cartography")),
            north = FALSE, tabtitle = TRUE, postitle = "right",
            col = "white", coltitle = "black")
# north arrow
north(pos = "topleft")

可视化结果如下：

铅笔风格主题地图

R-cartography 实例应用

我们使用之前空间插值系列的数据进行不同主题地图的绘制，首先，我们将所使用数据转换成sf对象，代码如下：

library(sf)
library(cartography)
library(openxlsx) # 读取Excel数据

jiangsu_shp <- "江苏省.json"
jiangsu <- sf::read_sf(jiangsu_shp)

file <- "pmdata.xlsx"
scatter_df <- read.xlsx(file)

scatter_sf <- st_as_sf(scatter_df,coords = c("lon", "lat"),crs = 4326)

接下来，我们进行部分样例的可视化绘制：

演示-1

plot(sf::st_geometry(jiangsu),col="#f2efe9", border="#b38e43", bg = "#aad3df",lwd = 0.5)
# plot PM2.5
propSymbolsLayer(
  x = scatter_sf,
  var = "PM2.5",
  #inches = 0.18,
  col = "brown4",
  legend.pos = "topright",
  legend.title.txt = "PM2.5"
)
# layout
layoutLayer(title = "PM2.5 Values in NanJing",
            author = paste0("cartography ", packageVersion("cartography"),"\nVisualization by DataCharm"),
            frame = FALSE, north = FALSE, tabtitle = TRUE)
# north arrow
north(pos = "topleft")

可视化结果如下：

当然，我们还可以添加类别(label)属性进行绘制：

演示-2

#par(mar = c(0.5,1,0.5,0.5))
# Plot the municipalities
pdf("G:\\DataCharm\\可视化包介绍(绘制)\\空间相关\\cartography_02.pdf")
plot(st_geometry(jiangsu), col="#f2efe9", border="#b38e43", bg = "#aad3df",
     lwd = 0.5)
# Plot symbols with choropleth coloration
propSymbolsTypoLayer(
  x = scatter_sf,
  var = "PM2.5",
  inches = 0.25,
  symbols = "square",
  border = "white",
  lwd = .5,
  legend.var.pos = "topright",
  legend.var.title.txt = "PM2.5",
  var2 = "label",
  legend.var2.values.order = c("1", "2","3","4"),
  col = carto.pal(pal1 = "multi.pal", n1 = 4),
  legend.var2.pos = c(117, 32.5),
  legend.var2.title.txt = "Scatter Class"
)
# layout
layoutLayer(title="PM2.5 Values in NanJing",
            author = paste0("cartography ", packageVersion("cartography"),"\nVisualization by DataCharm"),
            scale = 5, frame = FALSE, north = FALSE, tabtitle = TRUE)
# north arrow
north(pos = "topleft")
dev.off()

可视化结果如下：