R创建地区热力图

一、行政区域数据

地图文件可取自 GADM database (www.gadm.org)。

二、R代码

setwd("D:/gis/shap/CHN_adm_shp")

library("rgdal")

#china_map_adm2 <- readShapePoly("CHN_adm2.shp")
china_map_adm2 <- readOGR("CHN_adm2.shp",use_icOnv= TRUE, encoding="UTF-8")

OGR data source with driver: ESRI Shapefile 
Source: "CHN_adm2.shp", layer: "CHN_adm2"
with 344 features
It has 14 fields
Integer64 fields read as strings:  ID_0 ID_1 ID_2 CCN_2 

guangdong_map <- subset(china_map_adm2,NAME_1=="Guangdong")
summary(guangdong_map)

Object of class SpatialPolygonsDataFrame
Coordinates:
        min      max
x 109.66402 117.3107
y  20.22264  25.5203
Is projected: FALSE 
proj4string :
[+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0]
Data attributes:
 ID_0     ISO       NAME_0        ID_1          NAME_1        ID_2          NAME_2    HASC_2  
 49:21   CHN:21   China:21   6      :21   Guangdong:21   43     : 1   Chaozhou : 1   NA's:21  
                             1      : 0   Anhui    : 0   44     : 1   Dongguan : 1            
                             10     : 0   Beijing  : 0   45     : 1   Foshan   : 1            
                             11     : 0   Chongqing: 0   46     : 1   Guangzhou: 1            
                             12     : 0   Fujian   : 0   47     : 1   Heyuan   : 1            
                             13     : 0   Gansu    : 0   48     : 1   Huizhou  : 1            
                             (Other): 0   (Other)  : 0   (Other):15   (Other)  :15            
 CCN_2   CCA_2          TYPE_2                   ENGTYPE_2    NL_NAME_2      VARNAME_2 
 0:21   NA's:21   Dìjíshì  :21   Autonomous Prefecture: 0   潮州市 : 1   Cháozhōu : 1  
                  Dìqu     : 0   League               : 0   东莞市 : 1   Dōngguǎn : 1  
                  Méng     : 0   Municipality         : 0   佛山市 : 1   Fóshān   : 1  
                  Zhíxiáshì: 0   Prefecture           : 0   广州市 : 1   Guǎngzhōu: 1  
                  Zìzhìzhou: 0   Prefecture City      :21   河源市 : 1   Héyuán   : 1  
                                                            惠州市 : 1   Huìzhōu  : 1  
                                                            (Other):15   (Other)  :15  

str(guangdong_map,max.level=2)

Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
  ..@ data       :'data.frame': 21 obs. of  14 variables:
  ..@ polygons   :List of 21
  ..@ plotOrder  : int [1:21] 11 14 10 5 19 18 6 9 7 17 ...
  ..@ bbox       : num [1:2, 1:2] 109.7 20.2 117.3 25.5
  .. ..- attr(*, "dimnames")=List of 2
  ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot

#新增一列id，值为行名称
guangdong_map@data$id <- rownames(guangdong_map@data)

guangdongdata <- guangdong_map@data
head(guangdongdata)
   ID_0 ISO NAME_0 ID_1    NAME_1 ID_2    NAME_2 HASC_2 CCN_2 CCA_2  TYPE_2       ENGTYPE_2 NL_NAME_2 VARNAME_2 id  zhibiao
42   49 CHN  China    6 Guangdong   43  Chaozhou        0   Dìjíshì Prefecture City    潮州市  Cháozhōu 42 26.44414
43   49 CHN  China    6 Guangdong   44  Dongguan        0   Dìjíshì Prefecture City    东莞市  Dōngguǎn 43 17.47148
44   49 CHN  China    6 Guangdong   45    Foshan        0   Dìjíshì Prefecture City    佛山市    Fóshān 44 18.22510
45   49 CHN  China    6 Guangdong   46 Guangzhou        0   Dìjíshì Prefecture City    广州市 Guǎngzhōu 45 28.24686
46   49 CHN  China    6 Guangdong   47    Heyuan        0   Dìjíshì Prefecture City    河源市    Héyuán 46 20.83134
47   49 CHN  China    6 Guangdong   48   Huizhou        0   Dìjíshì Prefecture City    惠州市   Huìzhōu 47 28.36303

library(broom)
#将空间数据转换为数据框
#guangdongmapdata<- fortify(guangdong_map,region="id")
#guangdongmapdata <- broom::tidy(guangdong_map,region="id")
guangdongmapdata <- broom::tidy(guangdong_map)

Regions defined for each Polygons

dim(guangdongdata)
[1] 21 16

#虚拟一个指标。
guangdongdata$zhibiao<-runif(dim(guangdongdata)[1],10,30)
#将其与几何映射层进行合并：
guangdongnewmapdata<-merge(guangdongmapdata[,c(-4,-5)],guangdongdata[,c("id","ID_2","NL_NAME_2","zhibiao")],by.x="id",by.y="id")

head(guangdongnewmapdata)
  id     long      lat order group ID_2 NL_NAME_2  zhibiao
1 42 116.9841 24.16766     1  42.1   43    潮州市 26.44414
2 42 116.9797 24.16324     2  42.1   43    潮州市 26.44414
3 42 116.9762 24.15879     3  42.1   43    潮州市 26.44414
4 42 116.9694 24.15356     4  42.1   43    潮州市 26.44414
5 42 116.9595 24.15051     5  42.1   43    潮州市 26.44414
6 42 116.9541 24.14843     6  42.1   43    潮州市 26.44414

library("ggplot2")
library("ggthemes")
library("mapproj")

# 绘制地图
ggplot()+
geom_polygon(data=guangdongnewmapdata,aes(x=long,y=lat,group=group,fill=zhibiao),col="grey95")+
scale_fill_gradient(low="white",high="steelblue") +
coord_map("polyconic") +
theme_map()

#添加城市名标签
midpos <- function(x) mean(range(x,na.rm=TRUE))

library("plyr")
centres <- ddply(guangdongnewmapdata,.(NL_NAME_2),colwise(midpos,.(long,lat)))

# centres <- aggregate(cbind(long, lat) ~ NL_NAME_2, data=guangdongnewmapdata, 
#                    FUN=function(x)mean(range(x)))

ggplot(guangdongnewmapdata,aes(long,lat)) + 
geom_polygon(aes(group=group,fill=zhibiao),colour="black") +
scale_fill_gradient(low="white",high="steelblue") +
geom_text(aes(long, lat, label=NL_NAME_2),data=centres) +
theme( 
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
)

参考资料

[1] R笔记2：ggplot绘制商务图表--省分市的热力地图. 刘万祥. http://chuansong.me/n/1448600

[2] R语言可视化——关于ggplot所支持的数据地图素材类型. 杜雨. https://zhuanlan.zhihu.com/p/27360411

[3] Making Maps in R. Kevin Johnson. http://www.kevjohnson.org/making-maps-in-r/

[4] Using R — Working with Geospatial Data (and ggplot2). Bethany Yollin. http://mazamascience.com/WorkingWithData/?p=1494