title: "thematic map"
author: "wintryheart"
date: "2019/8/16"
output:
html_document:
toc: true
number_sections: true
toc_depth: 1

## ----echo=FALSE----------------------------------------------------------
knitr::opts_chunk$set( warning = FALSE, message = FALSE, echo = TRUE)

knitr::knit_hooks$set(margin = function(before, options, envir){
  if (before){
    par(mar=c(0.1,0.1,1.3,0.1))
  } 
})

cartography專題地圖包簡介

• cartography包要配合sf包或sp包使用倘屹，即队询，cartography包調(diào)用sf或sp格式的空間數(shù)據(jù)摊册，生成專題地圖称簿。

• 由于cartography包的大部分內(nèi)在組件都依賴sf空間功能,所以首先空間對象是sf格式绰更。

• cartography包自帶martinique地圖（馬提尼克议蟆，拉丁美洲向風(fēng)群島中部法屬島嶼，首府法蘭西堡）和數(shù)據(jù)歹篓。

  • shp格式(shape/martinique.shp)瘫证，
  • 地理包gpkg格式(gpkg/mtq.gpkg)，包含空間數(shù)據(jù)和專題數(shù)據(jù)庄撮。
  • 其他數(shù)據(jù)csv格式(csv/mob.csv)
  • 示例文件(doc/cartography.html)

主函數(shù)類別

Symbology系列

• 每個函數(shù)代表一種專題背捌，例如，比例符號（proportional symbols）或 面量圖（等值線洞斯、地區(qū)分布圖）（choropleth）载萌，然后顯示在地圖上。

• 允許將每個專題展示作為一個圖層，并在同一地圖上疊加展示扭仁。

• 每個函數(shù)都有兩個主參數(shù)：

  • x垮衷，空間對象，一般是sf對象乖坠。
  • var, 映射的變量名搀突。

• sp對象可以通過spdf參數(shù)和df參數(shù)處理。

  • 如果變量包含進Spatial*DataFrame熊泵，使用spdf參數(shù)仰迁；
  • 如果變量在獨立的data.frame，需要并入Spatial*DataFrame顽分，則使用spdf, spdfid, df, dfid參數(shù)徐许。

• 還有很多參數(shù)用于調(diào)節(jié)專題地圖展示效果，這些參數(shù)也是GIS和自動制圖工具中常見的參數(shù)卒蘸。

  • classification and color palettes 用于面量圖（choropleth maps）
  • symbols sizes 用于比例符號地圖（ proportional symbols maps）

library(cartography)
library(sf)

# 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)

# Choropleth
# plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
choroLayer(x = mtq, var = "POP", method = "quantile", nclass = 8)

1.png

# Typology
# plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
typoLayer(x = mtq, var = "LIBGEO")

2.png

# Proportional Symbols
plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
propSymbolsLayer(x = mtq, var = "POP", inches = 0.1, symbols = "circle")
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)

3.png

# Colorized Proportional Symbols (relative data) 兩個連續(xù)變量
plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
propSymbolsChoroLayer(x = mtq, var = "POP", var2 = "MED")
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)

4.png

# Colorized Proportional Symbols (qualitative data) 一個連續(xù)一個分類變量
plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
propSymbolsTypoLayer(x = mtq, var = "POP", var2 = "STATUS")
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)

5.png

# Double Proportional Symbols
plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
propTrianglesLayer(x = mtq, var1 = "POP", var2 = "MED")
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)

6.png

# OpenStreetMap Basemap (see rosm package) 
tiles <- getTiles(x = mtq, type = "osm")
tilesLayer(tiles)

7.png

# Isopleth (see SpatialPosition package)
library(SpatialPosition)
smoothLayer(x = mtq, var = "POP", typefct = "exponential", span = 4000, beta = 2, mask = mtq)

8.png

typefct參數(shù)：空間交互作用函數(shù)（spatial interaction function）雌隅，有兩個選項"pareto" (means power law帕累托冪律分布) or "exponential". 如果是"pareto"分布，交互作用函數(shù)為: (1 + alpha * mDistance) ^ (-beta)缸沃。如果是 "exponential"分布恰起， 交互作用函數(shù)為 : exp(- alpha * mDistance ^ beta) 。alpha參數(shù)由用戶給出的span和beta參數(shù)計算出趾牧。

span參數(shù)： 空間相互作用函數(shù)的概率密度為0.5的距離
beta參數(shù)： 空間相互作用函數(shù)的阻尼因子

如果有var2,那么將計算var1(分子)和var2（分母）的比率检盼。

對于中國地圖，建議取span=200000,beta=2

# Discontinuities
# Get a SpatialLinesDataFrame of countries borders 獲取邊界線數(shù)據(jù)框
mtq.contig <- getBorders(mtq)
# plot municipalities  畫區(qū)劃圖
plot(st_geometry(mtq))
# Plot the population with custom breaks  畫面量圖
choroLayer(x = mtq, var = "POP", legend.pos = "topright")
# Plot discontinuities畫
discLayer(x = mtq.contig, df = mtq, var = "POP",  method = "geom",  nclass = 3, threshold = 0.4)

9.png

10.png

# Flows包含三種：
# gradLinkLayer():  Graduated Links Layer 繪制一個分級鏈接層翘单。鏈接是根據(jù)離散的寬度類別繪制的吨枉。
# gradLinkTypoLayer():  Graduated and Colored Links Layer繪制一個彩色和漸變鏈接層。鏈接是根據(jù)離散類別的寬度繪制的哄芜。顏色依賴于離散變量的類別貌亭。
# propLinkLayer(): Proportional Links Layer繪制一個比例鏈接層。鏈接寬度與變量的值成正比忠烛。

mtq <- st_read(system.file("gpkg/mtq.gpkg", package="cartography"))
mob <- read.csv(system.file("csv/mob.csv", package="cartography"))
# Create a link layer - work mobilities to Fort-de-France (97209)
mob.sf <- getLinkLayer(x = mtq, df = mob[mob$j==97209,], dfid = c("i", "j"))
# Plot the links - Work mobility
plot(st_geometry(mtq), col = "grey60",border = "grey20")
propLinkLayer(x = mob.sf, df = mob, maxlwd = 10, legend.pos = "topright", var = "fij",
              col = "#92000090", add = TRUE)

11.png

# Dot Density
plot(st_geometry(mtq), col = NA, border = NA, bg = "#aadaff")
dotDensityLayer(x = mtq, var = "POP")
plot(st_geometry(mtq), lwd = 0.5, border = "grey20", add = TRUE, lty = 3)

# Label
labelLayer(x = mtq, txt = "POP",halo = TRUE, overlap = FALSE)

12.png

Transformations系列

• 這組函數(shù)用于創(chuàng)建或轉(zhuǎn)換空間對象属提，例如邊界提取权逗、網(wǎng)格或鏈接創(chuàng)建（borders extraction, grid or links creation）美尸。

• 提供這些功能是為了方便創(chuàng)建一些通常需要地理處理的更高級的地圖。

Map Layout系列

• 配合其它cartography函數(shù)使用斟薇，用于布局設(shè)計(layout design)师坎，例如，自定義比例尺堪滨，指北箭頭胯陋，標(biāo)題，來源或作者信息等（ customizable scale bar, north arrow, title, sources or author information…)。

Color Palettes系列

• cartography包封裝16種原色調(diào)色板。這些調(diào)色板可以定制和組合盟萨。

• carto.pal(pal1 = "blue.pal", n1 = 5, pal2 = "sand.pal", n2 = 3)

• 調(diào)色板方案參見cheatsheet凉翻。

Legends系列

• 默認(rèn)圖例伴隨制圖層顯示。

• 更多的參數(shù)可以通過legend*()函數(shù)定義捻激。

Classification系列

• 數(shù)據(jù)分類制轰、地圖分級（Classification）通常用于Choropleth Map（面量圖）的制作中，是專題制圖中最初胞谭，也是最基本的一個步驟垃杖。

• getBreaks() 用于連續(xù)變量進行分類，允許訪問大多數(shù)用于數(shù)據(jù)綁定的分類方法丈屹。

• 語法：getBreaks(v, nclass = NULL, method = "quantile", k = 1, middle = FALSE)

• 可用的method包括: quantile, equal, q6, fisher-jenks, mean-sd, sd, geometric progression...

  • "sd", "equal", "quantile" and "fisher-jenks" are classIntervals methods.
    • Jenks and Fisher-Jenks algorithms are based on the same principle and give quite similar results but Fisher-Jenks is much faster.
  • The "q6" method uses the following quantile probabilities: 0, 0.05, 0.275, 0.5, 0.725, 0.95, 1.基于特定的分位數(shù)概率调俘。
  • The "geom" method is based on a geometric progression along the variable values. 基于幾何級數(shù)（等比級數(shù)）
  • The "arith" method is based on an arithmetic progression along the variable values. 基于算術(shù)級數(shù)（等差級數(shù)）
  • The "em" method is based on nested averages computation. 基于嵌套的平均值計算
  • The "msd" method is based on the mean and the standard deviation of a numeric vector. 基于數(shù)值向量的均值和標(biāo)準(zhǔn)差
    • The nclass parameter is not relevant, use k and middle instead.
    • k indicates the extent of each class in share of standard deviation. 每個類別在標(biāo)準(zhǔn)差中所占份額的程度
    • If middle=TRUE then the mean value is the center of a class else the mean is a break value.

• 分類方法的詳細(xì)的中文說明參見數(shù)據(jù)分類那些事兒

bks1 <- getBreaks(v = mtq$POP, nclass = 6, method = "quantile")  
bks2 <- getBreaks(v = mtq$POP, nclass = 6, method = "fisher-jenks")  
pal <- carto.pal("green.pal",3, "wine.pal", 3)  
hist(mtq$POP, breaks = bks1, col = pal)  
hist(mtq$POP, breaks = bks2, col = pal)  

13.png

14.png

setwd("d:/province")
chinamap <- st_read("province.shp")
#點密度圖
plot(st_geometry(chinamap))
dotDensityLayer(x=chinamap, var="POPU",pch=20, col="red4", n=50)
#平滑數(shù)據(jù)圖
smoothLayer(x = chinamap, var = 'POPU',
             span = 200000, beta = 2,
             mask = chinamap, border = NA,
             col = carto.pal(pal1 = 'wine.pal', n1 = 8),
             legend.title.txt = "Population\nPotential",
             legend.pos = "topright", legend.values.rnd = 0)
propSymbolsLayer(x = chinamap, var = "POPU",   col = NA, border = "#ffffff50")

15.png

16.png

library(ggplot2)
library(cartography)
library(tidyverse) 
 
older <- read.csv("c:/users/wintryheart/desktop/oldermarriage.csv")
chinamap <- read_sf("d:/province/province.shp")
chinamap2 <- left_join(chinamap, older, by=c("NAME"="province"))
chinamap2 <- select(chinamap2, "NAME","unmar","married","divorce","widow")

choroLayer(x=chinamap2,var="unmar", method = "fisher-jenks", nclass = 4, legend.pos = "right") 
layoutLayer(title="中國老年人未婚狀況(2010)",sources = "數(shù)據(jù)來源：中國第六次人口普查", author = "制圖：李亮")

choroLayer(x=chinamap2,var="married", method = "fisher-jenks", nclass = 4, legend.pos = "right") 
layoutLayer(title="中國老年人有配偶狀況(2010)",sources = "數(shù)據(jù)來源：中國第六次人口普查", author = "制圖：李亮")


choroLayer(x=chinamap2,var="divorce", method = "fisher-jenks",nclass=4, legend.pos = "right") 
layoutLayer(title="中國老年人離婚狀況(2010)",sources = "數(shù)據(jù)來源：中國第六次人口普查", author = "制圖：李亮")

choroLayer(x=chinamap2,var="widow", method = "fisher-jenks",nclass=4, legend.pos = "right") 
layoutLayer(title="中國老年人喪偶狀況(2010)",sources = "數(shù)據(jù)來源：中國第六次人口普查", author = "制圖：李亮")

17.png

18.png

19.png

20.png