# [R] Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute

Eric Berger er|cjberger @end|ng |rom gm@||@com
Sun Nov 6 16:19:51 CET 2022

```Hi Tiffany,
Here is some code that might help with your problem. I solve a "toy"
problem that is conceptually the same.
Part 1 sets up my toy problem. You would have to replace Part 1 with
your real case. The main point is to define
a function f(i, j, data) which returns 0 or 1 depending on whether the
observations in rows i and j in your dataset 'data'
are within your cutoff distance (i.e. 50m).

You can then use Part 2 almost without changes (except for changing
'myData' to the correct name of your data).

I hope this helps,
Eric

library(dplyr)

## PART 1: create fake data for minimal example
set.seed(123) ## for reproducibility
N <- 5       ## replace by number of locations (approx 9000 in your case)
MAX_DISTANCE <- 2  ## 50 in your case
myData <- data.frame(x=rnorm(N),y=rnorm(N),Conc=sample(1:N,N))

## The function which you must re-define for your actual case.
f <- function(i,j,a) {
dist <- sqrt(sum((a[i,1:2] - a[j,1:2])^2)) ## Euclidean distance
as.integer(dist < MAX_DISTANCE)
}

## PART 2: You can use this code on the real data with f() defined appropriately
A <- matrix(0,N,N)
## get the indices (j,k) where j < k (as columns in a data.frame)
idx <- expand.grid(v,v) |> rename(j=Var1,k=Var2) |> filter(j < k)
u <- sapply(1:nrow(idx),\(i){ j <- idx\$j[i]; k <- idx\$k[i]; A[j,k] <<-
f(j,k,myData) })
B <- A + t(A) + diag(N)
C <- diag(myData\$Conc)
D <- B %*% C
D[D==0] <- NA
myData\$Conc_min <- apply(D,MAR=1,\(v){min(v,na.rm=TRUE)})

On Sat, Nov 5, 2022 at 5:14 PM Bert Gunter <bgunter.4567 using gmail.com> wrote:
>
> Probably better posted on R-sig-geo.
>
> -- Bert
>
> On Sat, Nov 5, 2022 at 12:36 AM Duhl, Tiffany R. <Tiffany.Duhl using tufts.edu>
> wrote:
>
> > Hello,
> >
> > I have sets of spatial points with LAT, LON coords (unprojected, WGS84
> > datum) and several value attributes associated with each point, from
> > numerous csv files (with an average of 6,000-9,000 points in each file) as
> > shown in the following example:
> >
> >
> > > data
> >     ID      Date         Time        LAT            LON           Conc
> > Leg.Speed    CO2  H2O BC61 Hr Min Sec
> > 1   76 4/19/2021 21:25:38 42.40066 -70.98802 99300   0.0 mph 428.39 9.57
> > 578 21  25  38
> > 2   77 4/19/2021 21:25:39 42.40066 -70.98802 96730   0.0 mph 428.04 9.57
> > 617 21  25  39
> > 3   79 4/19/2021 21:25:41 42.40066 -70.98802 98800   0.2 mph 427.10 9.57
> > 1027 21  25  41
> > 4   80 4/19/2021 21:25:42 42.40066 -70.98802 96510     2 mph 427.99 9.58
> > 1381 21  25  42
> > 5   81 4/19/2021 21:25:43 42.40067 -70.98801 95540     3 mph 427.99 9.58
> > 1271 21  25  43
> > 6   82 4/19/2021 21:25:44 42.40068 -70.98799 94720     4 mph 427.20 9.57
> > 910 21  25  44
> > 7   83 4/19/2021 21:25:45 42.40069 -70.98797 94040     5 mph 427.18 9.57
> > 652 21  25  45
> > 8   84 4/19/2021 21:25:46 42.40072 -70.98795 95710     7 mph 427.07 9.57
> > 943 21  25  46
> > 9   85 4/19/2021 21:25:47 42.40074 -70.98792 96200     8 mph 427.44 9.56
> > 650 21  25  47
> > 10  86 4/19/2021 21:25:48 42.40078 -70.98789 93750    10 mph 428.76 9.57
> > 761 21  25  48
> > 11  87 4/19/2021 21:25:49 42.40081 -70.98785 93360    11 mph 429.25 9.56
> > 1158 21  25  49
> > 12  88 4/19/2021 21:25:50 42.40084 -70.98781 94340    12 mph 429.56 9.57
> > 107 21  25  50
> > 13  89 4/19/2021 21:25:51 42.40087 -70.98775 92780    12 mph 428.62 9.56
> > 720 21  25  51
> >
> >
> > What I want to do is, for each point, identify all points within 50m of
> > that point, find the minimum value of the "Conc" attribute of each nearby
> > set of points (including the original point) and then create a new variable
> > ("Conc_min") and assign this minimum value to a new variable added to
> > "data".
> >
> > So far, I have the following code:
> >
> > library(spdep)
> > library(sf)
> >
> > setwd("C:\\mydirectory\\")
> >
> > #make sure the data is a data frame
> > pts <- data.frame(data)
> >
> > #create spatial data frame and define projection
> > pts_coords <- cbind(pts\$LON, pts\$LAT)
> > data_pts <- SpatialPointsDataFrame(coords= pts_coords,
> > data=pts, proj4string = CRS("+proj=longlat +datum=WGS84"))
> >
> > #Re-project to WGS 84 / UTM zone 18N, so the analysis is in units of m
> > ptsUTM  <- sf::st_as_sf(data_pts, coords = c("LAT", "LON"), remove = F)%>%
> > st_transform(32618)
> >
> > #create 50 m buffer around each point then intersect with points and
> > finally find neighbors within the buffers
> > pts_buf <- sf::st_buffer(ptsUTM, 50)
> > coords  <- sf::st_coordinates(ptsUTM)
> > int <- sf::st_intersects(pts_buf, ptsUTM)
> > x   <- spdep::dnearneigh(coords, 0, 50)
> >
> > Now at this point, I'm not sure what to either the "int" (a sgbp list) or
> > "x" (nb object) objects (or even if I need them both)
> >
> > > int
> > Sparse geometry binary predicate list of length 974, where the predicate
> > was `intersects'
> > first 10 elements:
> >  1: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  2: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  3: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  4: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  5: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  6: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  7: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  8: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >  9: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
> >
> > > x
> > Neighbour list object:
> > Number of regions: 974
> > Number of nonzero links: 34802
> > Percentage nonzero weights: 3.668481
> > Average number of links: 35.73101
> >
> > One thought is that maybe I don't need the dnearneigh function and can
> > instead convert "int" into a dataframe and somehow merge or associate
> > (perhaps with an inner join) the ID fields of the buffered and intersecting
> > points and then compute the minimum value of "Conc" grouping by ID:
> >
> > > as.data.frame(int)
> >     row.id col.id
> > 1        1      1
> > 2        1      2
> > 3        1      3
> > 4        1      4
> > 5        1      5
> > 6        1      6
> > 7        1      7
> > 8        1      8
> > 9        1      9
> > 10       1     10
> > 11       1     11
> > 12       1     12
> > 13       1     13
> > 14       1     14
> > 15       1     15
> > 16       1     16
> > 17       1     17
> > 18       1     18
> > 19       2      1
> > 20       2      2
> > 21       2      3
> > 22       2      4
> > 23       2      5
> > 24       2      6
> > 25       2      7
> > 26       2      8
> > 27       2      9
> > 28       2     10
> >
> >
> > So in the above example I'd like to take the minimum of "Conc" among the
> > col.id points grouped with row.id 1 (i.e., col.ids 1-18) and assign the
> > minimum value of this group as a new variable in data (Data\$Conc_min), and
> > do the same for row.id 2 and all the rest of the rows.
> >
> > I'm just not sure how to do this and I appreciate any help folks might
> > have on this matter!
> >
> > Many thanks,
> > -Tiffany
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
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> > and provide commented, minimal, self-contained, reproducible code.
> >
>
>         [[alternative HTML version deleted]]
>
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