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04-Maps.Rmd
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04-Maps.Rmd
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# Maps
地图毫无疑问是展示地理信息数据时最直观的工具,尤其是当地图和统计量结合时,其功效则会进一步加强。地理信息系统(GIS)已经成为研究空间和地理数据的热门工具,地图的应用也是屡见不鲜。地图的本质是多边形,而多边形的边界则由地理经纬度数据确定。
## maps包
R中的附加包 maps 是目前比较完善的地图程序包之一,maps包中核心的函数为`map()`,该函数的两个主要参数为地图数据库**database**和地图区域**region**,地图数据库中包含了所有区域的经纬度数据以及相应的区域名称,在指定一个数据库和一系列区域名称之后,这些区域的地图便可由`map()`生成。
```{r,eval=FALSE, message=TRUE}
library(maps)
usage(map, w = 0.8)
map(
database = "world", regions = ".", exact = FALSE,
boundary = TRUE, interior = TRUE, projection = "", parameters = NULL,
orientation = NULL, fill = FALSE, col = 1, plot = TRUE,
add = FALSE, namesonly = FALSE, xlim = NULL, ylim = NULL,
wrap = FALSE, resolution = if (plot) 1 else 0, type = "l",
bg = par("bg"), mar = c(4.1, 4.1, par("mar")[3], 0.1),
border = 0.01, ...
)
```
### 世界地图
通过运行如下代码得到世界地图。maps包里面还包括了美国、新西兰、意大利等国的地图。
```{r}
library(maps)
par(family = "STKaiti")
map("world", fill = TRUE, col = topo.colors(10), ylim = c(-60, 90), mar = c(0, 0, 0, 0))
title("世界地图") # 添加标题
```
### 美国地图
```{r}
map("state",
fill = TRUE, col = heat.colors(10),
mar = c(0, 0, 2, 0)
)
par(family = "STKaiti")
title("美国地图")
```
可以根据需要绘制某国地图或者美国某几个州的地图,只需在map()函数中添加选项例如`region = c('new york', 'new jersey', 'penn')` 即可。
```{r}
map("state",
region = c("new york", "new jersey", "penn"),
fill = TRUE, col = terrain.colors(3), mar = c(2, 3, 4, 3)
)
par(family = "STKaiti")
title("美国三州地图")
```
### 中国地图
在国家基础地理信息中心的网站上提供了免费的GIS数据下载,里面包括了国界与省界数据,使用R的maptools包的`readShapePoly()`或rgdal包的`readOGR()`可以读取shp文件。
```{r results='hide', message=FALSE, warning=FALSE}
library(maptools)
library(rgdal)
china <- readOGR("china/bou2_4p.shp")
china@data$NAME <- iconv(china@data$NAME, "GBK", "UTF-8")
```
该数据包含了中国925个地区的的面积、周长、编号、行政区名称等信息。
```{r}
str(china@data)
```
在绘制地图时,每一个省市自治区或岛屿都是用一个多边形来表示的。GIS数据提供了每一个行政区的多边形逐点的坐标,然后R通过顺次连接这些坐标,就绘制出了一个多边形区域。
```{r}
plot(china)
par(family = "STKaiti")
title("中国地图")
```
plot命令中的col参数在本例中应该是一个长度为 925 的向量,其第 i 个分量的取值就代表了地图中第 i 个多边形的颜色。
```{r}
plot(china, col = gray(924:0 / 924))
```
也可以通过查找相应的行政区对应的行名对col参数进行赋值,对相应地区进行着色:
```{r}
getColor <- function(mapdata, provname, provcol, othercol) {
f <- function(x, y) ifelse(x %in% y, which(y == x), 0)
colIndex <- sapply(mapdata@data$NAME, f, provname)
col <- c(othercol, provcol)[colIndex + 1]
return(col)
}
```
其中**mapdata**是存放地图数据的变量,**provname**是需要改变颜色的地区的名称,**provcol**是对应于**provname**的代表颜色的向量,**othercol**是其它地区的颜色。举例如下:
```{r}
provname <- c("北京市", "上海市", "重庆市", "福建省")
provcol <- c("red", "green", "yellow", "purple")
plot(china, col = getColor(china, provname, provcol, "white"))
```
利用类似的方法就可以根据自己的需要对不同的区域进行着色。从国家统计局获取我国各地区的人口数据,然后根据人口的多少对各省份进行着色。
```{r}
data_population <- read.csv("data/population.csv")
head(data_population[,2:3])
```
```{r}
provname <- data_population$provname
pop <- data_population$pop
provcol <- rgb(red = 1 - pop / max(pop) / 2, green = 1 - pop / max(pop) / 2, blue = 0)
plot(china, col = getColor(china, provname, provcol, "white"), xlab = "", ylab = "")
```
此外,还可以利用这个参数画出国内某一部分的地图,例如绘制福建、浙江、江西和广东四个东南地区省份的地图如下:
```{r}
southeast <- c("福建省", "浙江省", "江西省", "广东省")
plot(china,
col = getColor(china, southeast, rep("blue", 4), "white"), border = "white",
xlab = "", ylab = ""
)
```
还可以人工将各省会的信息在地图上标记出来
```{r}
par(mar = rep(0, 4))
par(family = "STKaiti")
dat <- read.csv("data/city.csv")
plot(china, col = "lightgray", ylim = c(18, 54), panel.first = grid())
points(dat$jd, dat$wd, pch = 19, col = rgb(0, 0, 0, 0.5), cex = 0.6)
text(dat$jd, dat$wd, dat[, 2],
cex = 0.7, col = rgb(0, 0, 0, 0.7),
pos = c(
2, 4, 4, 4, 3, 4, 2, 3, 4, 2, 4, 2, 2,
4, 3, 2, 1, 3, 1, 1, 2, 3, 2, 2, 1, 2, 4, 3, 1, 2, 2, 4, 4, 2
)
)
axis(1, lwd = 0)
axis(2, lwd = 0)
axis(3, lwd = 0)
axis(4, lwd = 0)
```
## REmap包
REmap是一个基于Echarts的R语言程序包,为使用者提供了一个简便的、可交互的地图数据可视化工具。由于REmap目前托管在GitHub上,需要使用devtools包下载。
``` {.{r,eval=false}}
install.packages('devtools')
library(devtools)
install_github('lchiffon/REmap')
```
### 获取经纬度信息
REmap是基于百度地图构建的,因此需要在百度地图开放平台创建项目获取AK,配置到REmap中。
```{r}
library(REmap)
options(remap.ak = "ZAIIlGEpPKoBVF5M4GLEuN6G5T6AQpCn") # 配置百度地图API
place <- c("中央财经大学学院南路校区", "中央财经大学沙河校区")
get_geo_position(place)
```
### 获取各省(市)的市(区)级行政单位名称
问:北京市究竟下辖哪几个区呢?
```{r}
mapNames("北京")
```
### 地图可视化
#### remap函数绘制航迹图
```{r,eval=F}
origin <- rep("北京", 10)
destination <- c(
"上海", "广州", "大连", "南宁", "南昌",
"拉萨", "长春", "包头", "重庆", "常州"
)
flight <- data.frame(origin, destination)
remap(flight, title = "航迹图", theme = get_theme("Dark"))
```
```{=html}
<script src="./js/echarts.js"></script>
<script src = "./js/echarts-all.js"></script>
<div id="remap" style="width: 700px; height:466px;"></div>
<script>
var myChart = echarts.init(document.getElementById("remap"));
var option =
{
backgroundColor: '#1b1b1b',
color: ['gold','aqua','lime'],
title : {
text: '航迹图',
subtext:'theme:Dark',
x:'center',
textStyle : {
color: '#fff'
}
},
tooltip : {
trigger: 'item',
formatter: '{b}'
},
toolbox: {
show : true,
orient : 'vertical',
x: 'right',
y: 'center',
feature : {
mark : {show: true},
dataView : {show: true, readOnly: false},
restore : {show: true},
saveAsImage : {show: true}
}
},
dataRange: {
min : 0,
show: false,
max : 100,
y: '60%',
calculable : true,
color: ['#ff3333', 'orange', 'yellow','lime','aqua']
},
series : [
{
type:'map',
itemStyle:{
normal:{
borderColor:'rgba(100,149,237,1)',
borderWidth: 0.5,
areaStyle:{
color: '#1b1b1b'
}
}
},
data:[],
geoCoord: {'北京': [116.413554,39.911013],
'上海': [121.480237,31.236305],
'广州': [113.270793,23.135308],
'大连': [121.621391,38.919345],
'南宁': [108.373351,22.823037],
'南昌': [115.864528,28.687675],
'拉萨': [91.121025,29.650088],
'长春': [125.33017,43.82178],
'包头': [109.846755,40.663636],
'重庆': [106.557165,29.570997],
'常州': [119.58,31.47]},
markLine : {
smooth:true,
effect : {
show: true,
scaleSize: 1,
period: 30,
color: '#fff',
shadowBlur: 10
},
itemStyle : {
color: 'red',
normal: {
borderWidth:1,
lineStyle: {
type: 'solid',
shadowBlur: 10
},
label:{show:false}
}
},
data : [
[{name:'北京'}, {name:'上海',value: 90}],
[{name:'北京'}, {name:'广州',value: 70}],
[{name:'北京'}, {name:'大连',value: 70}],
[{name:'北京'}, {name:'南宁',value: 20}],
[{name:'北京'}, {name:'南昌',value: 80}],
[{name:'北京'}, {name:'拉萨',value: 80}],
[{name:'北京'}, {name:'长春',value: 20}],
[{name:'北京'}, {name:'包头',value: 30}],
[{name:'北京'}, {name:'重庆',value: 20}],
[{name:'北京'}, {name:'常州',value:100}]
]
},
markPoint : {
symbol:'emptyCircle',
symbolSize : function (v){
return 10 + v/10
},
effect : {
show: true,
shadowBlur : 0
},
itemStyle:{
normal:{
label:{show:true}
}
},
data : [
{name:'上海',value: 90},
{name:'广州',value: 70},
{name:'大连',value: 70},
{name:'南宁',value: 20},
{name:'南昌',value: 80},
{name:'拉萨',value: 80},
{name:'长春',value: 20},
{name:'包头',value: 30},
{name:'重庆',value: 20},
{name:'常州',value:100}
]
}
}
]
}
;
myChart.setOption(option);
</script>
```
图中的颜色可以通过`get_theme()`来设置,几个常用的参数:\
**Theme:theme**是总体配色选项,共有'Dark','Bright','Sky','None'四种选项。选定该选项(除'None'外)后,其它参数无需再设置。\
**lineColor**: lineColor为线条颜色,默认是随机,也可自行设置,如'red'。\
**BackgroundColor**:地图外背景颜色\
**RegionColor**:地图中各区域颜色
```{r,eval=FALSE, message=TRUE}
get_theme(
theme = "Dark",
lineColor = "Random",
backgroundColor = "#1b1b1b",
titleColor = "#fff",
borderColor = "rgba(100,149,237,1)",
regionColor = "#1b1b1b",
labelShow = T,
pointShow = F,
pointColor = "gold"
)
```
例如将颜色改为亮色:
```{r,eval=F}
remap(flight, title = "亮色航迹图", theme = get_theme("Bright"))
```
```{=html}
<div id="main2" style="width: 700px; height:466px;"></div>
<script src="http://echarts.baidu.com/build/dist/echarts.js"></script>
<script src = "http://echarts.baidu.com/build/dist/echarts-all.js"></script>
<script>
var myChart = echarts.init(document.getElementById("main2"));
var optionID_20211223000944_48477 =
{
backgroundColor: '#D9D9D9',
color: ['gold','aqua','lime'],
title : {
text: '亮色航迹图',
subtext:'',
x:'center',
textStyle : {
color: '#1b1b1b'
}
},
tooltip : {
trigger: 'item',
formatter: '{b}'
},
toolbox: {
show : true,
orient : 'vertical',
x: 'right',
y: 'center',
feature : {
mark : {show: true},
dataView : {show: true, readOnly: false},
restore : {show: true},
saveAsImage : {show: true}
}
},
dataRange: {
min : 0,
show: false,
max : 100,
y: '60%',
calculable : true,
color: ['#ff3333', 'orange', 'yellow','lime','aqua']
},
series : [
{
type:'map',
itemStyle:{
normal:{
borderColor:'rgba(100,149,237,1)',
borderWidth: 0.5,
areaStyle:{
color: '#fff'
}
}
},
data:[],
geoCoord: {'北京': [116.413554,39.911013],
'上海': [121.480237,31.236305],
'广州': [113.270793,23.135308],
'大连': [121.621391,38.919345],
'南宁': [108.373351,22.823037],
'南昌': [115.864528,28.687675],
'拉萨': [91.121025,29.650088],
'长春': [125.33017,43.82178],
'包头': [109.846755,40.663636],
'重庆': [106.557165,29.570997],
'常州': [119.58,31.47]},
markLine : {
smooth:true,
effect : {
show: true,
scaleSize: 1,
period: 30,
color: '#fff',
shadowBlur: 10
},
itemStyle : {
color: 'red',
normal: {
borderWidth:1,
lineStyle: {
type: 'solid',
shadowBlur: 10
},
label:{show:false}
}
},
data : [
[{name:'北京'}, {name:'上海',value: 10}],
[{name:'北京'}, {name:'广州',value: 50}],
[{name:'北京'}, {name:'大连',value: 10}],
[{name:'北京'}, {name:'南宁',value: 90}],
[{name:'北京'}, {name:'南昌',value:100}],
[{name:'北京'}, {name:'拉萨',value: 80}],
[{name:'北京'}, {name:'长春',value: 50}],
[{name:'北京'}, {name:'包头',value: 80}],
[{name:'北京'}, {name:'重庆',value: 0}],
[{name:'北京'}, {name:'常州',value:100}]
]
},
markPoint : {
symbol:'emptyCircle',
symbolSize : function (v){
return 10 + v/10
},
effect : {
show: true,
shadowBlur : 0
},
itemStyle:{
normal:{
label:{show:true}
}
},
data : [
{name:'上海',value: 10},
{name:'广州',value: 50},
{name:'大连',value: 10},
{name:'南宁',value: 90},
{name:'南昌',value:100},
{name:'拉萨',value: 80},
{name:'长春',value: 50},
{name:'包头',value: 80},
{name:'重庆',value: 0},
{name:'常州',value:100}
]
}
}
]
}
;
myChart.setOption(optionID_20211223000944_48477);
</script>
```
#### remapH绘制热力效果图
这个函数的特点是可以做中心辐射的热力图,这种热力图在气象、人口密度、海拔测绘领域有诸多运用,当然也可以用在商务场合------特别是跟地理信息有关的数据呈现方面。\
`remapH()`的各参数如下:
``` {.{r,eval=false}}
remapH(data,
maptype = 'china',
theme = get_theme("Dark"),
blurSize = 30,
color = c('blue'),
minAlpha = 0.05,
opacity = 1,
)
```
**data**为要传入的数据,数据为三列,第一列为lon(经度),第二列为lat(维度),第三列为prob(密度/概率)\
**maptype**为要绘制的地图类型,可选有:"china","world"或中国各省份名字\
**theme**为绘制的地图主题类型,可由get_theme函数传入,get_theme在下面会详解\
**blurSize**为热力效果的泛化范围,可调整热力点中心的扩散程度\
**color**为热力的渐变颜色\
**minAlpha**为热力点的展示阈值,对应data中的prob列,作图时各点密度会对比minAlpha,以凸显不同密度所展示的不同热力分布\
**opacity**为透明度,调整热力图的透明度
获取200个城市的PM2.5指数及经纬度,绘制热力图如下:
```{r}
air <- read.csv(file = "data/air.csv", header = T)
head(air)
```
```{r,eval=F}
theme1 <- get_theme(
theme = "none",
lineColor = "white",
backgroundColor = "white",
titleColor = "#fff",
borderColor = "blue",
regionColor = "grey",
labelShow = T,
pointShow = F,
pointColor = "gold"
)
remapH(air,
maptype = "china",
theme = theme1,
blurSize = 35,
color = "red",
minAlpha = 0.3,
opacity = 1,
)
```
```{=html}
<div id="main3" style="width: 700px; height:466px;"></div>
<script src="http://echarts.baidu.com/build/dist/echarts.js"></script>
<script src = "http://echarts.baidu.com/build/dist/echarts-all.js"></script>
<script>
var myChart = echarts.init(document.getElementById("main3"));
var heatData = [[121.3856774,41.05954614,0.361844359],
[113.499,22.285925,0.176389739],
[119.957365,36.78975,0.305614096],
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var optionID_20211223001405_74556 =
{
backgroundColor: 'white',
color: ['gold','aqua','lime'],
title : {
text: '',
subtext:'',
x:'center',
textStyle : {
color: '#fff'
}
},
tooltip : {
trigger: 'item',
formatter: function (v) {
if(v[2].tooltipValue!=null){
return v[2].tooltipvalue;
}else{
return v[1];
}
}},
dataRange: {
show: false,
min : 0,
max : 100,
calculable : true,
color: ['#ff3333', 'orange', 'yellow','lime','aqua'],
textStyle:{
color:'#fff'
}
},
legend: {
show:false,
orient: 'vertical',
x:'left',
data:['Data'],
textStyle:{color:'backgroundColorData'}
},
toolbox: {
show : true,
orient : 'vertical',
x: 'right',
y: 'center',
feature : {
mark : {show: true},
dataView : {show: true, readOnly: false},
restore : {show: true},
saveAsImage : {show: true}
}
},
series : [
{
name:'Data',
type:'map',
mapType: 'china',
roam: true,
itemStyle:{
normal:{
borderColor:'blue',
borderWidth: 0.5,
label:{show:true,textStyle:{color:'#fff'}},
areaStyle: {color: 'backgroundColorData'}
},
emphasis:{label:{show:true,textStyle:{color:'#fff'}}}
},
data:[]
,heatmap: {
blurSize:35 ,
colors:['red', 'white'] ,
minAlpha:0.3 ,
opacity:1 ,
data: heatData}
}]
}
;
myChart.setOption(optionID_20211223001405_74556);
</script>
```
#### remapC绘制填充地图
`remapC()`允许我们同时在一张地图上制作填充图和点图、线图。
```{r,eval=FALSE}
remapC(data,
maptype = "china",
markLineData = NULL,
markPointData = NULL,
color = c("#1e90ff", "#f0ffff"),
theme = get_theme("Bright"),
title = "",
subtitle = "",
markLineTheme = markLineControl(),
markPointTheme = markPointControl(),
geoData = NA,
mindata = NA,
maxdata = NA
)
```
主要参数:\
**data**为需要的原始数据框格式数据,共2列,第一列为省份或者城市名称,第二列为对应的数值,数值大小将决定地图颜色深浅。\
**maptype**为地图类型设置,可选world、china或者中国省份名称\
**color**为热力图渐变颜色,默认为2个颜色,第一个为终止色,第二个为初始色\
**mindata**与**maxdata**可以设置颜色极端点对应数据的上下限(默认是使用data中的数据最大值最小值作为两端极值)。
```{r,eval=F}
province <- mapNames("china") # 全国省份
value <- rnorm(34, 100, 30) # 随机生成分省值
mydata <- data.frame(province, value) # 合成数据框作图数据
remapC(mydata,
maptype = "China", color = c("yellow", "red"),
title = "全国分省图"
) # 制作等级图
```
```{=html}
<div id="main4" style="width: 700px; height:466px;"></div>
<script src="http://echarts.baidu.com/build/dist/echarts.js"></script>
<script src = "http://echarts.baidu.com/build/dist/echarts-all.js"></script>
<script>
var myChart = echarts.init(document.getElementById("main4"));
var optionID_20211223002124_118480 =
{
backgroundColor: '#D9D9D9',
color: ['gold','aqua','lime'],
title : {
text: '全国分省图',
subtext:'',
x:'center',
textStyle : {
color: '#1b1b1b'
}
},
tooltip : {
trigger: 'item',
formatter: function (v) {
if(typeof(v[2])=='number'){
return(v[1]+': '+v[2])
}else if(v[2].tooltipValue!=null){
return v[2].tooltipValue;
}else{
return v[1];
}
}},
legend: {
show:false,
orient: 'vertical',
x:'left',
data:['Data'],
textStyle:{color:'#D9D9D9'}
},
toolbox: {
show : true,
orient : 'vertical',
x: 'right',
y: 'center',
feature : {
mark : {show: true},
dataView : {show: true, readOnly: false},
restore : {show: true},
saveAsImage : {show: true}
}
},
dataRange: {
min : 22,
max : 209,
calculable : true,
color: ['yellow', 'red'],
textStyle:{
color:'#1b1b1b'
}
},
series : [
{
name:'Data',
type:'map',
mapType: 'china',
itemStyle:{
normal:{
borderColor:'rgba(100,149,237,1)',
borderWidth: 0.5,
label:{show:true,textStyle:{color:'#1b1b1b'}},
},
emphasis:{label:{show:true,textStyle:{color:'#1b1b1b'}}}
},
data:[{name:'新疆',value: 99.16015},
{name:'西藏',value: 96.01056},
{name:'内蒙古',value:122.37526},
{name:'青海',value: 96.69881},
{name:'四川',value:116.71160},
{name:'黑龙江',value:125.68131},
{name:'甘肃',value: 32.68334},
{name:'云南',value: 73.88564},
{name:'广西',value:197.66872},
{name:'湖南',value: 68.46319},
{name:'陕西',value: 98.68463},
{name:'广东',value:134.47487},
{name:'吉林',value:127.04263},
{name:'河北',value: 70.04408},
{name:'湖北',value:117.02832},
{name:'贵州',value: 93.24028},
{name:'山东',value: 77.15386},
{name:'江西',value:132.52203},
{name:'河南',value: 58.84273},
{name:'辽宁',value:119.98721},
{name:'山西',value: 54.37361},
{name:'安徽',value:125.20147},
{name:'福建',value: 47.02378},
{name:'浙江',value: 63.84055},
{name:'江苏',value: 61.34687},
{name:'重庆',value:100.89941},
{name:'宁夏',value: 92.69678},
{name:'海南',value:155.51616},
{name:'台湾',value: 71.73297},
{name:'北京',value: 60.77722},
{name:'天津',value:116.54781},
{name:'上海',value:115.86285},
{name:'香港',value:128.03343},
{name:'澳门',value:130.82322}]
}]
}
;
myChart.setOption(optionID_20211223002124_118480);
</script>
```
模拟出广东省回流各省的人口数,使用填色地图表示人流规模,使用动态流向线表示回流最大的前10个省份。
```{r, eval=F, warning=FALSE}
province <- mapNames("china") # 全国省份
value <- round(rnorm(34, 1000, 30), 0) # 随机生成分省值
mydata <- data.frame(province, value) # 合并数据
lableper <- mydata[order(mydata[, "value"], decreasing = T), ][1:10, ]
origin <- rep("广州", length(lableper))
destination <- lableper$province
line_data <- data.frame(origin, destination)
remapC(mydata,
title = "广东省人口迁徙地图",