ggplot2---绘制地图
文章目录
- sf包介绍
- 加载包
- 空间数据读取
- 数据操作
-
- gfc数据绑定
- gfc数据操作
- 坐标CRS数据
- 数据有效性
- 数据变形
- 可视化
-
- CRS变换效果
- 空间数据构造及绘图
- 图形微调
-
- 局部放大
- 南海诸岛小图
sf包介绍
主要使用sf包进行空间数据分析。
sf包主要控制simple feature对象，比之前的sp包从数据结构上看更容易懂，也能对空间数据进行大量的操作。
sf包中的对象主要有：
- sf 数据框
- sfc 列表列
- sfg sfc中的任一元素
- crs 坐标参考系统
加载包
library(sf)
library(tidyverse)
空间数据读取
以中国省级行政划分为例，数据来源于aliyun的datav。
china_dat <- read_sf("china_province.json")
attr(china_dat, "sf_column")
## [1] "geometry"
class(china_dat)
## [1] "sf" "tbl_df" "tbl" "data.frame"
数据操作
gfc数据绑定
sf对象中有一列（gfc）,专门存贮空间属性数据，在对sf对象数据进行操作时，默认是不能排除gfc列的。
china_dat %>% select(1) %>% knitr::kable()#绑定
| Name_Province | geometry |
|---|---|
| 安徽省 | POLYGON ((119.1702 31.2955,… |
| 澳门特别行政区 | MULTIPOLYGON (((113.5348 22… |
| 北京市 | POLYGON ((115.961 40.2643, … |
| 福建省 | MULTIPOLYGON (((119.4823 25… |
| 甘肃省 | POLYGON ((104.0702 33.67677… |
| 广东省 | MULTIPOLYGON (((113.6739 22… |
| 广西壮族自治区 | MULTIPOLYGON (((109.083 21… |
| 贵州省 | MULTIPOLYGON (((108.302 25… |
| 海南省 | MULTIPOLYGON (((110.2262 20… |
| 河北省 | MULTIPOLYGON (((117.667 39… |
| 河南省 | MULTIPOLYGON (((115.4948 35… |
| 黑龙江省 | POLYGON ((124.0832 50.54255… |
| 湖北省 | POLYGON ((109.951 31.47014,… |
| 湖南省 | MULTIPOLYGON (((112.8936 29… |
| 吉林省 | POLYGON ((129.0903 43.54665… |
| 江苏省 | MULTIPOLYGON (((118.8514 31… |
| 江西省 | POLYGON ((118.2324 28.92183… |
| 辽宁省 | MULTIPOLYGON (((122.7548 39… |
| 南海九段线 | MULTILINESTRING ((108.338 7… |
| 南海诸岛 | MULTIPOLYGON (((119.6371 23… |
| 内蒙古自治区 | POLYGON ((102.8302 42.10144… |
| 宁夏回族自治区 | MULTIPOLYGON (((106.0464 35… |
| 青海省 | POLYGON ((92.40531 39.03601… |
| 山东省 | MULTIPOLYGON (((120.1647 35… |
| 山西省 | POLYGON ((110.9079 38.7014,… |
| 陕西省 | POLYGON ((109.62 33.27277, … |
| 上海市 | MULTIPOLYGON (((121.5172 31… |
| 四川省 | POLYGON ((103.7797 28.52829… |
| 台湾省 | MULTIPOLYGON (((120.4145 24… |
| 天津市 | POLYGON ((116.8757 39.07094… |
| 西藏自治区 | POLYGON ((79.15081 32.48816… |
| 香港特别行政区 | MULTIPOLYGON (((114.1675 22… |
| 新疆维吾尔自治区 | POLYGON ((80.77494 43.29664… |
| 云南省 | POLYGON ((98.69413 28.21893… |
| 浙江省 | MULTIPOLYGON (((121.9817 29… |
| 重庆市 | POLYGON ((107.2248 30.22737… |
china_dat %>% as_tibble() %>% select(1) #绑定解除
## # A tibble: 36 x 1
## Name_Province
##
## 1 安徽省
## 2 澳门特别行政区
## 3 北京市
## 4 福建省
## 5 甘肃省
## 6 广东省
## 7 广西壮族自治区
## 8 贵州省
## 9 海南省
## 10 河北省
## # ... with 26 more rows
可见直接select操作中，gfc列仍然存在。
如果需要去除这种效应，需要把sf类型转换为tibble、或者data.frame类型。
gfc数据操作
china_dat %>% st_geometry() #提取geom列
## Geometry set for 36 features
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 73.50078 ymin: 3.553559 xmax: 134.7752 ymax: 53.56082
## Geodetic CRS: WGS 84
## First 5 geometries:
## POLYGON ((119.1702 31.2955, 119.1528 31.29677, ...
## MULTIPOLYGON (((113.5348 22.19369, 113.5325 22....
## POLYGON ((115.961 40.2643, 115.9576 40.25636, 1...
## MULTIPOLYGON (((119.4823 25.35403, 119.4669 25....
## POLYGON ((104.0702 33.67677, 104.0789 33.67631,...
china_dat %>% st_geometry() %>% class() #sfc
## [1] "sfc_GEOMETRY" "sfc"
china_dat %>% st_geometry() %>% attributes() #sfc的属性
## $n_empty
## [1] 0
##
## $crs
## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]
##
## $class
## [1] "sfc_GEOMETRY" "sfc"
##
## $precision
## [1] 0
##
## $bbox
## xmin ymin xmax ymax
## 73.500775 3.553559 134.775196 53.560815
##
## $classes
## [1] "POLYGON" "MULTIPOLYGON" "POLYGON" "MULTIPOLYGON"
## [5] "POLYGON" "MULTIPOLYGON" "MULTIPOLYGON" "MULTIPOLYGON"
## [9] "MULTIPOLYGON" "MULTIPOLYGON" "MULTIPOLYGON" "POLYGON"
## [13] "POLYGON" "MULTIPOLYGON" "POLYGON" "MULTIPOLYGON"
## [17] "POLYGON" "MULTIPOLYGON" "MULTILINESTRING" "MULTIPOLYGON"
## [21] "POLYGON" "MULTIPOLYGON" "POLYGON" "MULTIPOLYGON"
## [25] "POLYGON" "POLYGON" "MULTIPOLYGON" "POLYGON"
## [29] "MULTIPOLYGON" "POLYGON" "POLYGON" "MULTIPOLYGON"
## [33] "POLYGON" "POLYGON" "MULTIPOLYGON" "POLYGON"
#china_dat %>% st_geometry() %>% .[[3]] #提取第3行，sfg对象
china_dat %>% st_geometry() %>% .[2] #提取第2行，sfc对象
## Geometry set for 1 feature
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 113.5325 ymin: 22.11169 xmax: 113.5983 ymax: 22.21531
## Geodetic CRS: WGS 84
## MULTIPOLYGON (((113.5348 22.19369, 113.5325 22....
china_dat %>% st_geometry() %>% sapply(length) # 每一行中的多边形数量
## [1] 2 4 1 45 2 55 5 2 9 4 3 1 3 2 1 4 1 15 15 96 1 2 1 10 1
## [26] 1 10 1 14 3 1 11 1 1 81 1
坐标CRS数据
一般空间数据读取会自带坐标CRS参数,否则需要制定CRS。CRS操作主要有3类：
- st_crs 获取CRS
- st_set_crs 设置CRS，gfc数据不变，但st_crs返回的参数发生改变
- st_transform 转换CRS，gfc数据发生变化
china_dat %>% st_geometry()%>% attributes()
## $n_empty
## [1] 0
##
## $crs
## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]
##
## $class
## [1] "sfc_GEOMETRY" "sfc"
##
## $precision
## [1] 0
##
## $bbox
## xmin ymin xmax ymax
## 73.500775 3.553559 134.775196 53.560815
##
## $classes
## [1] "POLYGON" "MULTIPOLYGON" "POLYGON" "MULTIPOLYGON"
## [5] "POLYGON" "MULTIPOLYGON" "MULTIPOLYGON" "MULTIPOLYGON"
## [9] "MULTIPOLYGON" "MULTIPOLYGON" "MULTIPOLYGON" "POLYGON"
## [13] "POLYGON" "MULTIPOLYGON" "POLYGON" "MULTIPOLYGON"
## [17] "POLYGON" "MULTIPOLYGON" "MULTILINESTRING" "MULTIPOLYGON"
## [21] "POLYGON" "MULTIPOLYGON" "POLYGON" "MULTIPOLYGON"
## [25] "POLYGON" "POLYGON" "MULTIPOLYGON" "POLYGON"
## [29] "MULTIPOLYGON" "POLYGON" "POLYGON" "MULTIPOLYGON"
## [33] "POLYGON" "POLYGON" "MULTIPOLYGON" "POLYGON"
china_dat %>% st_crs() #默认EPSG4326
## Coordinate Reference System:
## User input: WGS 84
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4326]]
china_dat %>% st_set_crs(3857) #更改CRS属性，不计算，gfc不变
## Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
## that
## Simple feature collection with 36 features and 2 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 73.50078 ymin: 3.553559 xmax: 134.7752 ymax: 53.56082
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 36 x 3
## Name_Province Code_Province geometry
## *
## 1 安徽省 34 POLYGON ((119.1702 31.2955, 119.1528 31.29677, ~
## 2 澳门特别行政区 82 MULTIPOLYGON (((113.5348 22.19369, 113.5325 22.~
## 3 北京市 11 POLYGON ((115.961 40.2643, 115.9576 40.25636, 1~
## 4 福建省 35 MULTIPOLYGON (((119.4823 25.35403, 119.4669 25.~
## 5 甘肃省 62 POLYGON ((104.0702 33.67677, 104.0789 33.67631,~
## 6 广东省 44 MULTIPOLYGON (((113.6739 22.66379, 113.6655 22.~
## 7 广西壮族自治区 45 MULTIPOLYGON (((109.083 21.0469, 109.0866 21.03~
## 8 贵州省 52 MULTIPOLYGON (((108.302 25.51802, 108.3148 25.5~
## 9 海南省 46 MULTIPOLYGON (((110.2262 20.06673, 110.2322 20.~
## 10 河北省 13 MULTIPOLYGON (((117.667 39.38564, 117.663 39.41~
## # ... with 26 more rows
china_dat %>% st_transform(3857) #重新投影，gfc变化
## Simple feature collection with 36 features and 2 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 8182069 ymin: 395834.3 xmax: 15003110 ymax: 7087415
## Projected CRS: WGS 84 / Pseudo-Mercator
## # A tibble: 36 x 3
## Name_Province Code_Province geometry
## *
## 1 安徽省 34 POLYGON ((13265965 3671186, 13264033 3671350, 1~
## 2 澳门特别行政区 82 MULTIPOLYGON (((12638632 2534796, 12638384 2533~
## 3 北京市 11 POLYGON ((12908715 4904424, 12908338 4903267, 1~
## 4 福建省 35 MULTIPOLYGON (((13300705 2919292, 13298992 2918~
## 5 甘肃省 62 POLYGON ((11585041 3985482, 11586011 3985421, 1~
## 6 广东省 44 MULTIPOLYGON (((12654123 2591410, 12653183 2593~
## 7 广西壮族自治区 45 MULTIPOLYGON (((12143065 2397471, 12143470 2395~
## 8 贵州省 52 MULTIPOLYGON (((12056128 2939507, 12057548 2941~
## 9 海南省 46 MULTIPOLYGON (((12270323 2280938, 12270989 2280~
## 10 河北省 13 MULTIPOLYGON (((13098626 4777063, 13098181 4780~
## # ... with 26 more rows
st_transform转换后gfc数据发生变化，则可视化地图也会发生改变。
数据有效性
st_is_valid检查gfc数据是否有效，否则可以用st_make_valid进行修正。
st_is_valid(china_dat)
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE
china_dat %>% st_is_valid(reason=T)
## [1] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [5] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [9] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [13] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [17] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [21] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [25] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [29] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
## [33] "Valid Geometry" "Valid Geometry" "Valid Geometry" "Valid Geometry"
数据变形
基于gfc数据计算距离、面积等。
st_distance(china_dat[c(1,5,10),],china_dat[c(1,8,14,30),]) #求距离
## Units: [m]
## [,1] [,2] [,3] [,4]
## [1,] 0.0 667571.5 221686.7 440083.9
## [2,] 627042.3 434023.6 505780.6 760186.8
## [3,] 185372.5 954865.4 701016.8 0.0
st_area(china_dat[1,]) #求面积
## 140266534479 [m^2]
st_centroid(china_dat[15,]) #计算重心
## Warning in st_centroid.sf(china_dat[15, ]): st_centroid assumes attributes are
## constant over geometries of x
## Simple feature collection with 1 feature and 2 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 126.2348 ymin: 43.66413 xmax: 126.2348 ymax: 43.66413
## Geodetic CRS: WGS 84
## # A tibble: 1 x 3
## Name_Province Code_Province geometry
## *
## 1 吉林省 22 (126.2348 43.66413)
可视化
ggplot结合geom_sf进行绘图。
china_dat %>% ggplot()+geom_sf()
CRS变换效果
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.1.1
p1=china_dat %>% ggplot()+geom_sf()+labs(title="EPSG4326")
p2=china_dat %>% st_transform(4508) %>% ggplot()+geom_sf()+labs(title="EPSG4508") #CGCS2000中国地图投影效果，这也是大多数纸质出版物地图的国家标准
p1+p2
空间数据构造及绘图
st_point(c(105,30)) %>% st_sfc() %>% st_set_crs(4326) #坐标点
## Geometry set for 1 feature
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 105 ymin: 30 xmax: 105 ymax: 30
## Geodetic CRS: WGS 84
## POINT (105 30)
aa=data.frame(long=seq(80,130,by=10),lat=seq(10,60,by=10))
bb=aa %>% st_as_sf(coords=c("long","lat"),crs=4326) #构造经纬度点
st_crs(bb)
## Coordinate Reference System:
## User input: EPSG:4326
## wkt:
## GEOGCRS["WGS 84",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["World."],
## BBOX[-90,-180,90,180]],
## ID["EPSG",4326]]
china_dat %>% ggplot()+geom_sf()+
geom_sf(data=bb,aes(color="red"))
图形微调
局部放大
#p1为主图
sub_china<-p1+
coord_sf(xlim=c(105,125),ylim=c(3,30))+
labs(x="",y="",title="")+
theme_void()+
theme(panel.border = element_rect(fill="NA"),legend.position='none')
sub_china
南海诸岛小图
main_china<-p1+
coord_sf(ylim=c(18,55),xlim=c(70,140))+
labs(x="Longitude",y="Latitude",fill="Pops")
main_china
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main_china + inset_element(sub_china,0.7, 0.02, 0.99, 0.45)
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1为主图
sub_china<-p1+
coord_sf(xlim=c(105,125),ylim=c(3,30))+
labs(x="",y="",title="")+
theme_void()+
theme(panel.border = element_rect(fill=“NA”),legend.position=‘none’)
sub_china
[外链图片转存中...(img-hJQkZmhV-1640245754581)]
### 南海诸岛小图
```r
main_china<-p1+
coord_sf(ylim=c(18,55),xlim=c(70,140))+
labs(x="Longitude",y="Latitude",fill="Pops")
main_china + inset_element(sub_china,0.7, 0.02, 0.99, 0.45)
