R 从光栅文件中提取数据以与shp文件匹配

更新：

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更新：问题是我生成网格单元的方法导致了我的shapefile的错误投影，这导致了上述错误。我能够制作出另一种形式的网格单元，它与我的投影完美配合

在我成功为世界地图（）创建光栅栅格之后

我现在尝试使用海德数据（）将其与每个细胞的人口信息合并

我下载了bayseline hyde数据，并试图通过如下提取函数将我生成的网格应用于人口数据：

## use extract function for nightlights
shape <- rgdal::readOGR("data/grid/grid.shp")

source("scripts/extractfunction_hyde.R")
extract_hyde(directory = "data/hyde/hyde_harmonized", shape)

extract_hyde <- function(directory = ".", shp,
                          years = NULL) {
  require(raster)
  require(velox)
  
  #sname <- shpname 
  #shpname <- paste0("data/FDI/", shpname)
  #shp <- readRDS(shpname)
  
  if (!class(shp) %in% c("SpatialPolygons", "SpatialPolygonsDataFrame",
                         "SpatialPointsDataFrame")) {
    stop(paste("'shp' must be either a SpatialPolygons",
               "SpatialPolygonsDataFrame or SpatialPointsDataFrame"))
  }
  
  crs <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
  shp <- sp::spTransform(shp, sp::CRS(crs))
  
  orig.dir <- getwd()
  setwd(directory)
  
  files <- list.files(pattern = "*.asc$")
  
  # Years in which this population data are available:
  all.years <- as.numeric(substr(files, 6, 9))  # The year is characters 4 to 9
  
  # Need to average the years where there are two satellite readings:
  double.years <- all.years[duplicated(all.years)]
  
  # If years aren't provided, take all of them:
  if (is.null(years)) {
    years <- sort(unique(all.years))
  }
  
  # Start the output data.frame:
  if (class(shp) == "SpatialPolygons") {
    df <- data.frame(id = 1:length(shp@polygons))
  } else if (class(shp) == "SpatialPolygonsDataFrame") {
    df <- data.frame(shp@data)
  } else if (class(shp) == "SpatialPointsDataFrame") {
    df <- data.frame(shp@data)
  }
  
  for (i in seq_along(years)) {
    
    cat("Extracting  data for year ", years[i], "...", sep = "")
    
    # If there are two satellite readings in a year, average them first:
    if (years[i] %in% double.years) {
      both.files <- grep(years[i], files, value = TRUE)
      r  <- crop(raster(both.files[1]), shp, snap = "out")
      r2 <- crop(raster(both.files[2]), shp, snap = "out")
      values(r) <- (values(r) + values(r2)) / 2
      rm(r2)
      r <- velox(r)
      
      # With only one reading in a year, just read in the file normally:
    } else {
      r <- crop(raster(grep(years[i], files, value = TRUE)), shp, snap = "out")
      r <- velox(r)
    }
    
    extract <- r$extract(sp=shp, fun = function(x) mean(x, na.rm = TRUE))
    df[[paste0("pop_mean", years[i])]] <- c(extract)
    extract <- r$extract(sp=shp, fun = function(x) sum(x, na.rm = TRUE))
    df[[paste0("pop_sum", years[i])]] <- c(extract)

    
    
    cat("Done\n")
  }
  
  saveRDS(df, "data/hyde/hyde_grid.Rds")
}

该函数适用于我使用过的所有其他形状文件。例如，我在另一篇stackoverflow文章中使用了第二个选项，这非常有效

我研究过，有时当有不同的投影时，这个问题会引起注意。但是我的函数会调整海德文件和形状文件的投影

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我假设我收到此错误是因为shp文件没有正确生成？

请提供一个最小的（且自包含且可复制的）示例。很难想到一个可复制的示例，因为您需要此形状文件和任何类型的tif/asc光栅文件（在我的案例中为海德）。我尝试添加更多信息，以便更容易复制！是的，这可能会很困难，但tou至少可以简化您的示例，几乎所有的示例都与手头的问题无关。更新：问题是我生成网格单元的方法导致了形状文件的错误投影，从而导致了上述错误。我能够制作出另一种形式的网格单元，它与我的投影完美配合。

## use extract function for nightlights
shape <- rgdal::readOGR("data/grid/grid.shp")

source("scripts/extractfunction_hyde.R")
extract_hyde(directory = "data/hyde/hyde_harmonized", shape)

extract_hyde <- function(directory = ".", shp,
                          years = NULL) {
  require(raster)
  require(velox)
  
  #sname <- shpname 
  #shpname <- paste0("data/FDI/", shpname)
  #shp <- readRDS(shpname)
  
  if (!class(shp) %in% c("SpatialPolygons", "SpatialPolygonsDataFrame",
                         "SpatialPointsDataFrame")) {
    stop(paste("'shp' must be either a SpatialPolygons",
               "SpatialPolygonsDataFrame or SpatialPointsDataFrame"))
  }
  
  crs <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
  shp <- sp::spTransform(shp, sp::CRS(crs))
  
  orig.dir <- getwd()
  setwd(directory)
  
  files <- list.files(pattern = "*.asc$")
  
  # Years in which this population data are available:
  all.years <- as.numeric(substr(files, 6, 9))  # The year is characters 4 to 9
  
  # Need to average the years where there are two satellite readings:
  double.years <- all.years[duplicated(all.years)]
  
  # If years aren't provided, take all of them:
  if (is.null(years)) {
    years <- sort(unique(all.years))
  }
  
  # Start the output data.frame:
  if (class(shp) == "SpatialPolygons") {
    df <- data.frame(id = 1:length(shp@polygons))
  } else if (class(shp) == "SpatialPolygonsDataFrame") {
    df <- data.frame(shp@data)
  } else if (class(shp) == "SpatialPointsDataFrame") {
    df <- data.frame(shp@data)
  }
  
  for (i in seq_along(years)) {
    
    cat("Extracting  data for year ", years[i], "...", sep = "")
    
    # If there are two satellite readings in a year, average them first:
    if (years[i] %in% double.years) {
      both.files <- grep(years[i], files, value = TRUE)
      r  <- crop(raster(both.files[1]), shp, snap = "out")
      r2 <- crop(raster(both.files[2]), shp, snap = "out")
      values(r) <- (values(r) + values(r2)) / 2
      rm(r2)
      r <- velox(r)
      
      # With only one reading in a year, just read in the file normally:
    } else {
      r <- crop(raster(grep(years[i], files, value = TRUE)), shp, snap = "out")
      r <- velox(r)
    }
    
    extract <- r$extract(sp=shp, fun = function(x) mean(x, na.rm = TRUE))
    df[[paste0("pop_mean", years[i])]] <- c(extract)
    extract <- r$extract(sp=shp, fun = function(x) sum(x, na.rm = TRUE))
    df[[paste0("pop_sum", years[i])]] <- c(extract)

    
    
    cat("Done\n")
  }
  
  saveRDS(df, "data/hyde/hyde_grid.Rds")
}

         [,1]     [,2] [,3] [,4]
[1,] -9070131 -8626996  Inf  Inf
[2,] -9080131 -8626996  Inf  Inf
Error in .spTransform_Polygon(input[[i]], to_args = to_args, from_args = from_args,  :failure in Polygons 1584553 Polygon 1 points 3:4