问
如何将SpatialPointsDataFrame更改为R中的SpatialPolygonsDataFrame,以便在ggplot2之后使用它？
鄧淇-琪___106 发布于 2023-02-13 15:06我使用R在世界地图上使用ggplot2的大圆圈投影一些数据流.我想也可以在我的地图上预测城市地区:http: //www.naturalearthdata.com/downloads/
但是,它们位于SpatialPointsDataFrame中.也许我的问题很简单,但我不知道如何将文件更改为SpatialPolygons.
我的代码如下:
urbanareasin <- readShapePoly("//....//ne_10m_populated_places//ne_10m_populated_places.shp")
simp<-gSimplify(urbanareasin, tol=0.1)
urbanareas<-ggplot2:::fortify(simp)
我也尝试过:
urbanareas<-fortify.SpatialPolygonsDataFrame(simp)
和:
urbanareas<-ggplot2:::fortify.SpatialPolygonsDataFrame(simp)
但它们都不起作用.我不得不遗漏一些东西......我是R的初学者,我会很感激任何建议.
先感谢您!
PS.查找以下数据信息:
str(urbanareasin) # to get info about the object Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots ..@ data :'data.frame': 7322 obs. of 92 variables: .. ..$ SCALERANK : int [1:7322] 10 10 10 10 10 10 10 10 10 10 ... .. ..$ NATSCALE : int [1:7322] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ LABELRANK : int [1:7322] 8 8 8 8 8 8 8 7 7 7 ... .. ..$ FEATURECLA: Factor w/ 8 levels "Admin-0 capital",..: 4 4 4 4 4 4 4 4 4 4 ... .. ..$ NAME : Factor w/ 7069 levels "'Ataq","'s-Hertogenbosch",..: 1453 6358 2017 1135 1973 612 5894 3924 2991 6136 ... .. ..$ NAMEPAR : Factor w/ 81 levels "Adi Ugri","Alleppey",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ NAMEALT : Factor w/ 454 levels "\xdcr\xfcmqi|Wulumqi",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ DIFFASCII : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ NAMEASCII : Factor w/ 7063 levels "'Ataq","'s-Hertogenbosch",..: 1441 6355 2008 1125 1964 605 5887 3912 2981 6127 ... .. ..$ ADM0CAP : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ CAPALT : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ CAPIN : Factor w/ 23 levels "Administrative",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ WORLDCITY : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MEGACITY : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ SOV0NAME : Factor w/ 200 levels "Afghanistan",..: 191 191 191 191 191 177 177 180 180 180 ... .. ..$ SOV_A3 : Factor w/ 201 levels "AFG","AGO","ALB",..: 189 189 189 189 189 175 175 182 182 182 ... .. ..$ ADM0NAME : Factor w/ 223 levels "Afghanistan",..: 214 214 214 214 214 200 200 203 203 203 ... .. ..$ ADM0_A3 : Factor w/ 223 levels "ABW","AFG","AGO",..: 210 210 210 210 210 196 196 203 203 203 ... .. ..$ ADM1NAME : Factor w/ 2477 levels "?li Bayramli",..: 535 718 1856 431 719 1065 460 1318 1100 2202 ... .. ..$ ISO_A2 : Factor w/ 225 levels "-99","AD","AE",..: 212 212 212 212 212 197 197 202 202 202 ... .. ..$ NOTE : Factor w/ 6 levels "1","From 1996 as a summer only station",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ LATITUDE : num [1:7322] -34.5 -33.5 -33.1 -34.5 -34.1 ... .. ..$ LONGITUDE : num [1:7322] -57.8 -56.9 -58.3 -56.3 -56.2 ... .. ..$ CHANGED : num [1:7322] 4 4 4 4 4 4 4 4 4 4 ... .. ..$ NAMEDIFF : int [1:7322] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ DIFFNOTE : Factor w/ 52 levels "Added","Added from GeoNames for UN mega cities.",..: 9 9 9 9 9 9 9 6 9 9 ... .. ..$ POP_MAX : int [1:7322] 21714 21093 23279 19698 32234 61845 21054 61705 19875 62577 ... .. ..$ POP_MIN : int [1:7322] 21714 21093 23279 19698 32234 61845 21054 61705 19875 62577 ... .. ..$ POP_OTHER : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ RANK_MAX : int [1:7322] 7 7 7 6 7 8 7 8 6 8 ... .. ..$ RANK_MIN : int [1:7322] 7 7 7 6 7 8 7 8 6 8 ... .. ..$ GEONAMEID : num [1:7322] 3443013 3439749 3442568 3443413 3442585 ... .. ..$ MEGANAME : Factor w/ 588 levels "\xdcr\xfcmqi (Wulumqi)",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ LS_NAME : Factor w/ 6559 levels "25 de Mayo","28 de Noviembre",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ LS_MATCH : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ CHECKME : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_POP10 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_POP20 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_POP50 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_POP300: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_POP310: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_NATSCA: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_AREAKM: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_AREAKM: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_AREAMI: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_AREAMI: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_PERKM : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_PERKM : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_PERMI : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_PERMI : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_BBXMIN: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_BBXMIN: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_BBXMAX: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_BBXMAX: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_BBYMIN: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_BBYMIN: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MIN_BBYMAX: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MAX_BBYMAX: num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MEAN_BBXC : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ MEAN_BBYC : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ COMPARE : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ GN_ASCII : Factor w/ 5960 levels "'Ayoun el 'Atrous",..: 1236 5382 1692 979 1656 529 5000 NA 2536 5197 ... .. ..$ FEATURE_CL: Factor w/ 1 level "P": 1 1 1 1 1 1 1 NA 1 1 ... .. ..$ FEATURE_CO: Factor w/ 10 levels "PPL","PPLA","PPLA2",..: 1 1 1 1 1 1 1 NA 1 1 ... .. ..$ ADMIN1_COD: num [1:7322] 4 6 12 2 7 5 22 0 31 34 ... .. ..$ GN_POP : num [1:7322] 21714 21093 23279 19698 32234 ... .. ..$ ELEVATION : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ GTOPO30 : num [1:7322] 28 134 43 29 74 463 374 0 49 247 ... .. ..$ TIMEZONE : Factor w/ 319 levels "Africa/Abidjan",..: 121 121 121 121 121 32 32 NA 49 49 ... .. ..$ GEONAMESNO: Factor w/ 16 levels "Added from GeoNames.",..: 2 2 2 2 2 5 2 13 2 5 ... .. ..$ UN_FID : int [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ UN_ADM0 : Factor w/ 119 levels "Afghanistan",..: NA NA NA NA NA NA NA NA NA NA ... .. ..$ UN_LAT : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ UN_LONG : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1950 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1955 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1960 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1965 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1970 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1975 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1980 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1985 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1990 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP1995 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2000 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2005 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2010 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2015 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2020 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2025 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ POP2050 : num [1:7322] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ CITYALT : Factor w/ 140 levels "Ad Damman","Al Hudaydah",..: NA NA NA NA NA NA NA NA NA NA ... ..@ coords.nrs : num(0) ..@ coords : num [1:7322, 1:2] -57.8 -56.9 -58.3 -56.3 -56.2 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "coords.x1" "coords.x2" ..@ bbox : num [1:2, 1:2] -179.6 -90 179.4 82.5 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "coords.x1" "coords.x2" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slots .. .. ..@ projargs: chr "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
rafa.pereira.. 9
ggplot不适用于形状文件或光栅图像等空间对象.您需要将它们转换为data.frames.
如果您正在使用SpatialPointsDataFrame,这很简单:
mapdata <- data.frame(yourshapefile) # now create the map ggplot() + geom_point( data= mapdata, aes(x=long, y=lat), color="red")
如果你正在使用SpatialPolygonsDataFrame,你需要fortify对象,如下所示:
yourshapefile_df <- fortify(yourshapefile, region ="id") # now create the map ggplot() + geom_point(data= yourshapefile_df, aes(x=long, y=lat, group=group), color="red")
这个答案来自ZevRoss网页,这是一个非常有用的空间分析来源R.
1 个回答
-
ggplot不适用于形状文件或光栅图像等空间对象.您需要将它们转换为data.frames.如果您正在使用
SpatialPointsDataFrame,这很简单:mapdata <- data.frame(yourshapefile) # now create the map ggplot() + geom_point( data= mapdata, aes(x=long, y=lat), color="red")
如果你正在使用
SpatialPolygonsDataFrame,你需要fortify对象,如下所示:yourshapefile_df <- fortify(yourshapefile, region ="id") # now create the map ggplot() + geom_point(data= yourshapefile_df, aes(x=long, y=lat, group=group), color="red")
这个答案来自ZevRoss网页,这是一个非常有用的空间分析来源
R.2023-02-13 15:07 回答
小狐狸2502895237
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