如何计算多边形的地理距离? [英] How to calculate geo-distance from a polygon?

问题描述

我有一个shapefile，其中有50多种不同的多边形(代表50多种不同的区域)和10,000多种数据点，这些数据点应该存在于其中一个区域中.事实是，已经对10,000+点的区域进行了编码，我想算出它们在地理空间距离上与该编码区域相距多远.

I have a shapefile with 50+ different polygonal shapes (representing 50+ different regions) and 10,000+ data points that are supposed to be present in one of the regions. The thing is, the 10,000+ points are already coded with a region they are supposed to be in, and I want to figure out how far they are from this coded region in geo-spatial distance.

我当前的方法(下面的代码)包括将shapefile从sp库转换为owin对象，并使用distfun可以在拉长的欧几里得空间中获得距离.但是我想获得地理空间距离(最终转换为公里).我下一步应该去哪里?

My current approach (code below), which involves converting shapefiles to owin objects from the sp library and using distfun gets me distances in lat,long euclidean space. But I would like to get geo-spatial distances (eventually to convert to km). Where should I go next?

#basically cribbed from http://cran.r-project.org/web/packages/spatstat/vignettes/shapefiles.pdf (page 9)
shp <- readShapeSpatial("myShapeFile.shp", proj4string=CRS("+proj=longlat +datum=WGS84"))
regions <- lapply(slot(shp, "polygons"), function(x) SpatialPolygons(list(x)))
windows <- lapply(regions, as.owin)

# need to convert this to geo distance
distance_from_region <- function(regionData, regionName) {
    w <- windows[[regionName]]
    regionData$dists <- distfun(w)(regionData$lat, regionData$long)
    regionData
}   

推荐答案

我将数据投影到一个欧几里得(或接近欧几里得)坐标系-除非您跨越地球的大部分，否则这是可行的.使用来自maptools的spTransform或sp或rgdal(我忘记了)，然后转换为数据附近的UTM区域.

I'd project the data to a euclidean (or near euclidean) coordinate system - unless you are spanning a large chunk of the globe then this is feasible. Use spTransform from maptools or sp or rgdal (I forget which) and convert to a UTM zone near your data.

您还可以使用rgeos软件包和gDistance函数来做得更好:

You also might do better with package rgeos and the gDistance function:

 gDistance by default returns the cartesian minimum distance
 between the two geometries in the units of the current projection.

如果您的数据遍及全球，那么...棘手... 42 ...

If your data is over a large chunk of globe then... tricky... 42...

巴里

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