R语言绘制州市地图进行可视化分析

约定不等于承诺〃 2023-07-21 06:24 13阅读 0赞

## 目的 ##

将经纬度数据对应的数值先进行聚类，然后将聚类的结果和经纬度数据进行合并，将不同的聚类结果在地图上进行热力板块展示。type是聚类的类型结果

## 数据展示![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70] ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 3]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 4]  
聚类结果判断图，聚成4类。

## 聚类包加载 ##

install.packages("factoextra")

## 数据读取 ##

data=read.csv("C:\\Users\\Administrator\\Desktop\\Rap\\annual_conc_by_monitor_20181.csv",header=T)
    
    ## 得出有效数据：筛选列数：根据确定要研究的变量指标名称只需要选择5列："Latitude","Longitude","Parameter_Name","Arithmetic_Mean","County_Name"
    head(data)
    
    subdata1<-data[,c("Latitude","Longitude","Parameter_Name","Arithmetic_Mean","State_Name")]
    
    ## 得出分析数据：筛选符合条件的数据：筛选城市、筛选研究指标
    # 1.在指标State_Name中筛选城市New York, Vermont, New Hampshire, Massachusetts, Connecticut 这五个地方
    # 2.在指标Parameter_Name中筛选 Ozone, Nitrogen dioxide, PM2. 5 local这三个变量值。
    
    str(subdata1)
    
    subdata2 <-  subset(subdata1,State_Name=c("New York","Vermont","New Hampshire","Massachusetts","Connecticut"))
    subdata2$State=tolower(subdata2$State_Name)
    newdata1 = subdata2[subdata2$Parameter_Name == 'Ozone',]
    newdata2 = subdata2[subdata2$Parameter_Name == 'PM2.5 - Local Conditions',]
    newdata3 = subdata2[subdata2$Parameter_Name == 'Nitrogen dioxide (NO2)',]
    
    
    endata1 <- newdata1
    endata2 <- newdata2
    endata3 <- newdata3
    
    
    #write.csv(endata1,"Ozone.csv")
    #write.csv(endata2,"Nitrogen dioxide.csv")
    #write.csv(endata3,"PM2.5.csv")
    str(endata1)

## 聚类分析 ##

#install.packages("fpc")
    library(fpc)
    
    ## 聚类数据提取
    mydata1<-endata1[,"Arithmetic_Mean"]
    mydata2<-endata2[,"Arithmetic_Mean"]
    mydata3<-endata3[,"Arithmetic_Mean"]
    
    ## 聚类个数判断
    library(factoextra)
    df1 <- scale(mydata1)
    fviz_nbclust(df1, kmeans, method = "wss") + geom_vline(xintercept = 4, linetype = 2) ##得出第一个数据聚成4类
    
    df2 <- scale(mydata2)
    fviz_nbclust(df2, kmeans, method = "wss") + geom_vline(xintercept = 4, linetype = 2)
    
    df3 <- scale(mydata3)
    fviz_nbclust(df3, kmeans, method = "wss") + geom_vline(xintercept = 4, linetype = 2)
    
    ## 进行聚类
    
    kmd1=kmeans(mydata1,centers = 4)
    kmd2=kmeans(mydata2,centers = 4)
    kmd3=kmeans(mydata3,centers = 4)
    
    ##聚类结果合并
    
    cldata1<-cbind(endata1,type=kmd1$cluster) #聚类结果和原始数据合并
    cldata2<-cbind(endata2,type=kmd2$cluster) #聚类结果和原始数据合并
    cldata3<-cbind(endata3,type=kmd3$cluster) #聚类结果和原始数据合并
    
    head(cldata1)
    str(cldata1)
    cldata1$type=factor(cldata1$type)
    cldata2$type=factor(cldata2$type)
    cldata3$type=factor(cldata3$type)

### 地图绘制 ###

euro <- map_data("state", region = c("new york","vermont",
     "new hampshire","massachusetts", "connecticut"))
    
    hb_map1<-merge(euro,cldata1,by.x="region",by.y="State")
    hb_map2<-merge(euro,cldata2,by.x="region",by.y="State")
    hb_map3<-merge(euro,cldata3,by.x="region",by.y="State")
    
    ### 地图1
    ggplot(hb_map1,aes(x=long,y=lat,fill=type))+
    geom_polygon(colour="grey")+
    coord_map("polyconic")+ggtitle("Ozone")
    
    ### 地图2
    ggplot(hb_map2,aes(x=long,y=lat,fill=type))+
    geom_polygon(colour="grey")+
    coord_map("polyconic")+ggtitle("PM2.5")
    
    ### 地图3
    ggplot(hb_map3,aes(x=long,y=lat,fill=type))+
    geom_polygon(colour="grey")+
    coord_map("polyconic")+ggtitle("N02")

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70]: /images/20230528/912736d0a5a345da92e447124b6b6d24.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 1]: /images/20230528/8b0e56946a48443eb552ff016dde5cb4.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 2]: /images/20230528/63500ad683d74febac838cb8191a39b5.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 3]: /images/20230528/bb7c37bb35d64417bbaabd8d36b2676e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 4]: /images/20230528/cbe8fdf44abc4e219b35c1fa034061df.png