我想计算出数据点(经度/经度)到R中形状文件边缘的距离，然后对数据点应用一个标准。

Question

我有一个使用相机陷阱观察到的物种的数据点，并希望使用R测量每个相机陷阱位置(CameraStation)到国家公园边缘的距离。我有公园的shapefile (shp)，并希望将一个标准应用于距离边缘<5 5km的CameraStation(s)。我的数据帧(df)由每个CameraStation的多个事件/观察值(EventID)组成。目的是分析当公园边缘附近的事件最频繁的时候，考虑到其他环境因素，如季节，月相和DayNight (也是DF中的列)。

我在R中找到了一个名为distance的包，但这是用于距离采样的，而不是我想要做的。在这种情况下，哪个包是相关的？

我预计会有以下结果：

EventID  CameraStation  Distance(km)  Within 5km
0001     Station 1      4.3           Yes
0002     Station 1      4.3           Yes
0003     Station 2      16.2          No
0004     Station 3      0.5           Yes
...

Answer 1

这是一个通用的解决方案，改编自Spacedman对this question at gis.stackexchange的回答。注意:此解决方案需要在投影坐标系中工作。如果需要，您可以使用spTransform转换为投影的CRS。

rgeos包的gDistance函数计算几何图形之间的距离，但对于多边形内部的点，距离为零。诀窍是创建一个新的“遮罩”多边形，其中原始多边形是从遮罩中切出的一个洞。然后我们可以测量洞中的点和蒙版之间的距离，这是我们真正关心的到原始多边形边缘的距离。

我们将使用在this page上找到的黄石国家公园边界的形状文件。

library(sp) # for SpatialPoints and proj4string
library(rgdal) # to read shapefile with readOGR
library(rgeos) # for gDistance, gDifference, and gBuffer

# ab67 was the name of the shape file I downloaded.
yellowstone.shp <- readOGR("ab67")

# gBuffer enlarges the boundary of the polygon by the amount specified by `width`.
# The units of `width` (meters in this case) can be found in the proj4string 
# for the polygon.
yellowstone_buffer <- gBuffer(yellowstone.shp, width = 5000)

# gDifference calculates the difference between the polygons, i.e. what's
# in one and not in the other. That's our mask.
mask <- gDifference(yellowstone_buffer, yellowstone.shp)

# Some points inside the park
pts <- list(x = c(536587.281264245, 507432.037861251, 542517.161278414,
                  477782.637790409, 517315.171218198),
            y = c(85158.0056377799, 77251.498952222, 15976.0721391485,
                  40683.9055315169, -3790.19457474617))

# Sanity checking the mask and our points.
plot(mask)
points(pts)

​

​

# Put the points in a SpatialPointsDataFrame with camera id in a data field.
spts.df <- SpatialPointsDataFrame(pts, data = data.frame(Camera = ordered(1:length(pts$x))))

# Give our SpatialPointsDataFrame the same spatial reference as the polygon.
proj4string(spts.df) <- proj4string(yellowstone.shp)

# Calculate distances (km) from points to edge and put in a new column.
spts.df$km_to_edge <- apply(gDistance(spts.df, difference, byid=TRUE),2,min)/1000

# Determine which records are within 5 km of an edge and note in new column.
spts.df$edge <- ifelse(spts.df$km_to_edge < 5, TRUE, FALSE)

# Results
spts.df

#             coordinates Camera km_to_edge  edge
# 1  (536587.3, 85158.01)      1   1.855010  TRUE
# 2     (507432, 77251.5)      2   9.762755 FALSE
# 3  (542517.2, 15976.07)      3  11.668700 FALSE
# 4  (477782.6, 40683.91)      4   4.579638  TRUE
# 5 (517315.2, -3790.195)      5   8.211961 FALSE

Answer 2

这里有一个快速的解决方案。

将shapefile的轮廓简化为N个点。然后计算每个相机陷阱到国家公园轮廓中每个点的最小距离。

library(geosphrere)
n      <- 500 ##The number of points summarizing the shapefile
NPs    <- ##Your shapefile goes here
NP.pts <- spsample(NPs, n = n, type = "regular")


CP.pts <- ## Coordinates for a single trap
distances<-distm(coordinates(CP.pts), coordinates(NP.pts), fun = distHaversine)/1000 

##Distance in Km between the trap to each point in the perimeter of the shapefile:
distances

使用distances查找shapefile和给定陷印之间的最小距离。对于loops或apply函数，这种方法很容易推广。

Answer 3

我在投影点数据框和形状文件时遇到问题，因此我使用此链接中的示例来回答我的问题

https://gis.stackexchange.com/questions/225102/calculate-distance-between-points-and-nearest-polygon-in-r

基本上，我使用了以下代码；

df # my data frame with points
shp # my shapefile (non-projected)

dist.mat <- geosphere::dist2Line(p = df2, line = shp)
coordinates(df2)<-~Longitude+Latitude # Longitude and Latitude are columns in my df

dmat<-data.frame(dist.mat) # turned it into a data frame

dmat$km5 <- ifelse(dmat$distance < 5000, TRUE, FALSE) # in meters (5000)
coordinates(dmat)<-~lon+lat

df2$distance <- dmat$distance # added new Distance column to my df