[R-sig-Geo] Calculate shortest distance between points belonging to different polygons

[Thierry Onkelinx]

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_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
>
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