Duhl, Tiffany R.
2022-Nov-04 15:13 UTC
[R] Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
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<- read.csv("R_find_pts_testdata.csv")
> 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\\")
data<- read.csv("R_find_pts_testdata.csv")
#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]]
Bert Gunter
2022-Nov-05 15:08 UTC
[R] Selecting a minimum value of an attribute associated with point values neighboring a given point and assigning it as a new attribute
Probably better posted on R-sig-geo. -- Bert On Sat, Nov 5, 2022 at 12:36 AM Duhl, Tiffany R. <Tiffany.Duhl at 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<- read.csv("R_find_pts_testdata.csv") > > > 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\\") > data<- read.csv("R_find_pts_testdata.csv") > > #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]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]