Calculate shortest distance between points belonging to different polygons

 Dear Thierry,

Thanks for your prompt reply!
I must admit I'm not familiar with ddply(), I'd need further advice from
you.

randp.ppp<-as.ppp(randp.df)
randp.hs.ppp

marked planar point pattern: 100 points
Mark variables: Point_ID, Polygon_ ID
window: rectangle = [650602.2, 766311.2] x [7503238, 7607430] units

distance<-as.data.frame(nndist(randp.ppp, by = randp.ppp$Polygon_ID))
distance

    nndist(randp.ppp, by = randp.hs.ppp$Polygon_ID)
1                                   2579.42199
2                                   1391.88915
3                                     59.85628
...
...
98                                   955.26483
99                                  3166.00894
100                                  705.25663

distance$Origin<-as.factor(randp.df$Polygon_ID)
distance$Origin

  [1] 13 6  12 5  5  12 12 10 8  3  5  13 3  3  10 3  5  3  13 3  2  3  12
6  5  13 3  2  3  3  9  1  3  4  12 6  12 12 10 2  13 3  6  3  3  6  3  9
1
 ...
 [99] 12 12
Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13

ddply(distance, "Origin", function(x){

+   ignore.vars<-c(levels(x$Origin)[x$Origin[1]], "Origin")
+   x<-x[,!colnames(x) %in% ignore.vars]
+   data.frame(
+     Distance = apply(x, 1, min),
+     Target = colnames(x)[apply(x, 1, which.min)]
+   )
+ })
Error: dim(X) must have a positive length

Sorry but I can't spot the problem.

Sincerely,

Ivan


On 17-Feb-15 13:21, Thierry Onkelinx wrote:

 Here is an example using ddply() to do the aggregation

library(spatstat)
library(plyr)
n <- 100
set.seed(123)
point <- matrix(runif(2 * n), ncol = 2)
colnames(point) <- c("X", "Y")
point <- data.frame(point, Polygon = factor(LETTERS[kmeans(point,
13)$cluster]))
pattern <- as.ppp(point, W = owin(0:1, 0:1))

distance <- as.data.frame(nndist(pattern, by = pattern$marks))
distance$Origin <- point$Polygon
ddply(distance, "Origin", function(x){
  ignore.vars <- c(levels(x$Origin)[x$Origin[1]], "Origin")
  x <- x[, !colnames(x) %in% ignore.vars]
  data.frame(
    Distance = apply(x, 1, min),
    Target = colnames(x)[apply(x, 1, which.min)]
  )
})

 Best regards,

Thierry

 ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2015-02-17 11:35 GMT+01:00 Ivan Palmegiani <pan.sapiens.it at gmail.com>:

Dear members of the list,

I'm handling a SpatialPointsDataFrame with 100 ramdom points distributed
within 13 different polygons.

randp

                    coordinates   Point_ID   Polygon_ ID
0  (690926.8, 7522595)         1_hs                    13
1  (696727.1, 7576122)         2_hs                      6
...
...
98 (728199.9, 7549810)     99_hs                    12
99 (723428.1, 7545891) 100 <7545891%29%20%20%20100>_hs
  12

I need to calculate the shortest distance between points belonging to
different polygons. Basically I'd like to do what nndist {spatstat} does.
The difference is that the distance should be calculated between groups of
points instead of within a group of points.

I tried to use "aggregate" as suggested below but it didn't work out for
me.
http://www.inside-r.org/packages/cran/spatstat/docs/nndist

Please find my try below:

randp.df<-data.frame(randp)
randp.hs.df

       Point_ID     coords.x1      coords.x2   Polygon_ ID
0            1_hs     690926.8       7522595 13
1            2_hs     696727.1       7576122 6
2            3_hs     723480.7       7546594 12

library(spatstat)

# Calculate nearest neighbors within a polygon

nn.within.pol<-nndist(randp.df[,c(2,3)],by=marks(randp.df$Polygon_ID))
nn.within.pol

 [1]  2579.42199  1391.88915    59.85628   734.95108   734.95108
840.65125   957.47838   741.58160   955.26483  3307.59444 1361.64626
2682.70690
 ...
 ...
 [97]  1349.88694   955.26483  3166.00894   705.25663
# Ok but these are not the distances I need

# Calculate nearest neighbors between polygons
nn.between.pol<-aggregate(nn.within.pol,
by=list(from=marks(randp.df$Polygon_ID)), min)
# Error in aggregate.data.frame(as.data.frame(x), ...) : arguments must
have same length

nn.between.hs<-aggregate(randp.hs.df[,c(2,3)],

by=list(randp.df$Polygon_ID), nndist)

nn.between.hs

The outcome is an asymmetric data frame (dim 13, 6) with a lot of empty
cells and values that look unlikely to be distances.

The result I'd like to get is a matrix (dim 100, 1) with the distances
between each random point and its nearest neighbor belonging to a different
polygon (i.e. its nearest neighbor having a different Polygon_ID).

Can someone kindly correct my script or suggest a function able to do the
job?

Cheers,

Ivan