我有一个26层的光栅堆栈，代表一年中的不同周。在这个区域内有854个不规则形状的多边形，我对每个层的（均值和标准差）的汇总值感兴趣。
我相信我已经成功提取了这些值。但是，当我尝试将它们与多边形文件合并时，我不成功。我相信extract()函数已经剥离了其识别标准的值（Name_2）。我尝试将extract()函数的“大列表”输出转换为数据框，但这也不起作用。
有什么建议吗？
谢谢。
Brazil0 <- getData('GADM', country='BRA', level=0)
Brazil1 <- getData('GADM', country='BRA', level=1)
Brazil2 <- getData('GADM', country='BRA', level=2)
MinasGerais<-subset(Brazil2, NAME_1 =='Minas Gerais')
e <- extent(MinasGerais)
MG <- as(Brazil2[Brazil2$NAME_1 == 'Minas Gerais',], 'SpatialPolygons')
row.names(MG) <- as.character(1:length(MG))
setwd("H:/Brazilian Arbovirus Project/ratsers_EpiWeek")
Precip_2017 <- stack("Precip_2017.tif")
Precip_crop <- crop(Precip_2017, e, snap="out")
crop<- setValues(Precip_crop, NA)
Precip_raster <- rasterize(MinasGerais, crop)
Precip_MG<- mask(x=Precip_crop, mask=Precip_raster)
MG_Municip_mean_2 <- extract(Precip_MG,
MG,
method='simple',
match.ID=FALSE,
FUN=mean,
#sp=TRUE,
small=TRUE)
output=data.frame(MG_Municip_mean_2)
MG_1 <- merge( MinasGerais, MG_Municip_mean_2, by='NAME_2', all=TRUE)
writeOGR(MG_1, getwd(), "MG_Municipalities", driver="ESRI Shapefile",
check_exists=TRUE,
overwrite_layer=TRUE)
答案 0 :(得分：0)
对于这种计算，我更喜欢光栅化多边形而不是使用extract()，这种方法很容易用于多层栅格。
有一个可重复的例子：
library(sp)
library(raster)
library(dplyr)
Brazil0 <- getData('GADM', country='BRA', level=0)
Brazil1 <- getData('GADM', country='BRA', level=1)
Brazil2 <- getData('GADM', country='BRA', level=2)
set.seed(123)
r <- raster(ext = extent(Brazil0@bbox))
rList <- list()
for(i in 1:26){
rList[[i]] <- setValues(r,values = rnorm(n = ncell(r)))
}
rs <- stack(rList)
上面的代码创建了一个带有随机值的多层栅格，现在是时候栅格化并创建data.frame进行计算了：
B0r <- rasterize(Brazil0,r,'OBJECTID')
B1r <- rasterize(Brazil1,r,'OBJECTID')
B2r <- rasterize(Brazil2,r,'OBJECTID')
df <- as.data.frame(stack(B0r,B1r,B2r,rs))
names(df)[1:3] <- paste0('level',0:2)
names(df)[4:29] <- paste0('week',1:26)
我会在此示例中使用Brazil2多边形，在这种情况下，存在小于1像素的多边形，因此并非所有多边形都有信息（可能您正在使用更好的空间分辨率）。使用dplyr包，您可以轻松计算每个多边形的任何类型的公式，并合并回属性表：
# example for level2
Brazil2@data <- df %>% select(-c(level0,level1)) %>% group_by(level2) %>%
summarise_each(funs(mean(., na.rm = TRUE),sd(., na.rm = TRUE))) %>%
left_join(x=Brazil2@data,by=c('OBJECTID'='level2'))
head(Brazil2)
## OBJECTID ID_0 ISO NAME_0 ID_1 NAME_1 ID_2 NAME_2 HASC_2 CCN_2 CCA_2 TYPE_2 ENGTYPE_2
## 1 1 33 BRA Brazil 1 Acre 1 Acrelândia NA Município Municipality
## 2 2 33 BRA Brazil 1 Acre 2 Assis Brazil NA Município Municipality
## 3 3 33 BRA Brazil 1 Acre 3 Brasiléia NA Município Municipality
## 4 4 33 BRA Brazil 1 Acre 4 Bujari NA Município Municipality
## 5 5 33 BRA Brazil 1 Acre 5 Capixaba NA Município Municipality
## 6 6 33 BRA Brazil 1 Acre 6 Cruzeiro do Sul NA Município Municipality
## NL_NAME_2 VARNAME_2 week1_mean week2_mean week3_mean week4_mean week5_mean week6_mean week7_mean
## 1 0.02799743 0.65684118 0.18032148 0.15386044 0.5635764 0.13131793 0.66844905
## 2 0.62831026 -0.46710306 0.56323394 0.16482915 -0.1190538 -0.42822608 0.11841901
## 3 -0.04441109 0.30017178 0.24697659 -0.44659707 0.1560986 0.08040939 0.11568072
## 4 0.04773195 -0.18690385 0.03840785 -0.06729135 0.3124202 -0.04866495 -0.38620625
## 5 -0.18407894 0.21761910 0.30628065 0.11929847 -0.5596950 -0.45436160 0.34162667
## 6 -0.31508001 -0.07326906 0.19979837 -0.07487962 0.2475976 0.13038549 0.08667631
## week8_mean week9_mean week10_mean week11_mean week12_mean week13_mean week14_mean week15_mean
## 1 0.29398763 -0.42470630 -0.39871869 -0.11133687 -0.11600877 0.04953248 0.2538490 -0.6387728
## 2 0.43123080 -0.19684622 -0.52333000 0.48386358 0.30651641 0.02512788 -0.0629341 -0.3729230
## 3 -0.36615371 0.26210474 -0.30212885 -0.15083871 -0.07095279 -0.11673077 -0.4448210 0.2400551
## 4 -0.12058451 -0.07914649 0.07276792 0.08345732 -0.17132937 -0.25823307 -0.1630691 -0.3555612
## 5 -1.28023743 -0.43201474 -0.92807227 0.32306609 0.76073801 0.50142559 0.7291509 0.6027042
## 6 0.08928839 -0.23224347 0.10913390 0.35135734 0.04670324 0.09490760 0.3855241 0.1820617
## week16_mean week17_mean week18_mean week19_mean week20_mean week21_mean week22_mean week23_mean
## 1 0.62806442 -0.23251001 0.41445121 -0.357856979 0.06260211 0.25732954 -0.08781137 0.33854357
## 2 -0.45495979 -0.05871667 -0.09732541 -0.131669502 -0.08885639 -0.48216217 -0.11940655 -0.52798951
## 3 0.31939261 -0.19266620 0.08671799 0.241000093 0.14864434 0.07044010 0.47460312 0.07706955
## 4 0.08420384 -0.35656266 -0.18245677 0.008064379 0.07319371 0.09271722 -0.13242697 0.29805436
## 5 0.62892059 -0.07006391 0.41960685 0.167582501 0.10296624 0.07797158 0.03335396 0.20283157
## 6 0.26078806 0.16028080 0.31722646 -0.166467152 0.25832317 -0.13020199 -0.05083594 -0.07325540
## week24_mean week25_mean week26_mean week1_sd week2_sd week3_sd week4_sd week5_sd week6_sd
## 1 1.0375927 0.05829013 -0.08900512 0.5039079 0.7364619 0.6436874 1.1242115 0.8373088 1.2155468
## 2 0.5672509 0.44157869 -0.45395791 0.6429615 0.6969150 1.2544599 1.1263861 0.8122241 0.9647385
## 3 -0.3314678 -0.16537211 0.27106228 1.2177288 0.7593655 0.9915033 1.0651381 0.8324841 1.1640994
## 4 0.0992971 0.46187201 0.19528712 1.0670819 0.5727231 0.9797493 0.9037118 1.1892758 0.6578113
## 5 -0.5825252 -0.24512778 0.48153479 1.3876316 1.0093498 0.6523201 0.6672334 0.2975399 0.6660514
## 6 -0.1356949 -0.11380037 -0.07519057 0.8528147 1.0521164 0.6692769 0.9140993 1.1120830 0.9439429
## week7_sd week8_sd week9_sd week10_sd week11_sd week12_sd week13_sd week14_sd week15_sd week16_sd
## 1 1.0242434 1.0283737 0.7440292 0.7977511 0.5330551 1.1060657 1.0586667 0.9557767 1.2021589 0.8015805
## 2 0.8650897 1.1318929 0.7925099 0.8416769 0.6735254 1.2130762 1.1684757 1.4122850 1.1372183 1.2514929
## 3 1.0337027 0.9709125 0.7129832 0.7574105 1.3073536 0.8376609 1.1161002 0.8083519 1.2163283 0.9789011
## 4 0.7550593 0.6019020 1.0640847 1.1187340 0.9331608 0.9246086 1.0412971 0.9577963 1.0302680 0.8863142
## 5 1.2286351 0.8620402 0.5704018 1.5840274 0.8929725 0.6766018 0.4504001 0.4747059 0.6959029 1.8061449
## 6 1.1981396 1.0422314 1.0996494 0.7749073 0.7755682 1.2341215 0.9699409 0.8534397 0.8832001 1.1267263
## week17_sd week18_sd week19_sd week20_sd week21_sd week22_sd week23_sd week24_sd week25_sd week26_sd
## 1 1.7168281 1.3058866 0.7613507 1.4192652 1.0980767 0.5848721 1.1768701 1.2879231 1.1683099 0.8173507
## 2 0.8853379 1.2223339 1.0174595 0.8165643 0.7453179 1.4108344 1.4206444 1.0326178 0.5363038 1.1400113
## 3 0.7980645 1.2271442 1.1425325 0.9385259 1.1597657 1.0255039 1.1598827 0.7623564 0.7854232 0.8780667
## 4 0.9595817 1.1212473 0.8296976 1.1852795 1.2297023 1.0755448 1.0810457 0.8593361 1.0980879 0.7695382
## 5 1.3182950 0.4643851 0.8796118 1.0272719 0.6404252 0.4710161 0.5726681 0.6737512 1.3994816 1.0740231
## 6 1.1344667 0.9216528 1.3398224 0.7941615 1.2181886 1.1101733 1.0151286 1.3209190 1.0377582 1.0426156