closest_points not working
IrenaItova opened this issue · 8 comments
Thank you for developing this package, very much needed!
I have issue with closest_points for calculating the nearest neighbour between two SpatialPointDataFrames, it returns this message:
Error in closest_points(x, y) : could not find function "closest_points"
I tried upgrading R to the latest 4.0.5 version, but that didn't help. I also tried downloading the package directly from your repo with devtools::install_github("JeremyGelb/spNetwork") with no luck either. Do you think I am missing any dependent package?
R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=English_Netherlands.1252 LC_CTYPE=English_Netherlands.1252 LC_MONETARY=English_Netherlands.1252
[4] LC_NUMERIC=C LC_TIME=English_Netherlands.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] spNetwork_0.1.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[8] tibble_3.1.1 ggplot2_3.3.3 tidyverse_1.3.0 stplanr_0.7.2 spdep_1.1-7 spData_0.3.8 sf_0.9-8
[15] rgdal_1.5-16 geojson_0.3.4 geojsonio_0.9.2 sp_1.4-5
loaded via a namespace (and not attached):
[1] cubature_2.0.4.1 colorspace_2.0-1 deldir_0.2-10 ellipsis_0.3.2 class_7.3-18 rprojroot_1.3-2
[7] fs_1.5.0 httpcode_0.3.0 rstudioapi_0.13 proxy_0.4-25 remotes_2.3.0 fansi_0.4.2
[13] lubridate_1.7.9 xml2_1.3.2 codetools_0.2-18 splines_4.0.5 cachem_1.0.4 pkgload_1.2.1
[19] jsonlite_1.7.0 broom_0.7.0 dbplyr_1.4.4 rgeos_0.5-5 compiler_4.0.5 httr_1.4.2
[25] backports_1.1.7 assertthat_0.2.1 Matrix_1.3-2 fastmap_1.1.0 lazyeval_0.2.2 cli_2.5.0
[31] prettyunits_1.1.1 tools_4.0.5 igraph_1.2.6 coda_0.19-4 gtable_0.3.0 glue_1.4.2
[37] gmodels_2.18.1 tinytex_0.24 V8_3.2.0 Rcpp_1.0.6 cellranger_1.1.0 raster_3.4-10
[43] vctrs_0.3.8 crul_1.0.0 gdata_2.18.0 nlme_3.1-152 xfun_0.15 ps_1.6.0
[49] testthat_3.0.2 rvest_0.3.5 lifecycle_1.0.0 gtools_3.8.2 devtools_2.4.1 jqr_1.1.0
[55] LearnBayes_2.15.1 MASS_7.3-53.1 scales_1.1.1 hms_0.5.3 expm_0.999-6 curl_4.3
[61] memoise_2.0.0 geosphere_1.5-10 stringi_1.4.6 maptools_1.1-1 desc_1.3.0 e1071_1.7-6
[67] boot_1.3-27 pkgbuild_1.2.0 rlang_0.4.11 pkgconfig_2.0.3 lattice_0.20-41 tidyselect_1.1.0
[73] processx_3.5.2 magrittr_2.0.1 R6_2.5.0 generics_0.0.2 DBI_1.1.1 pillar_1.6.0
[79] haven_2.3.1 foreign_0.8-81 withr_2.4.2 units_0.7-1 modelr_0.1.8 crayon_1.4.1
[85] KernSmooth_2.23-18 utf8_1.2.1 usethis_2.0.1 grid_4.0.5 readxl_1.3.1 data.table_1.14.0
[91] blob_1.2.1 callr_3.7.0 reprex_0.3.0 classInt_0.4-3 munsell_0.5.0 sessioninfo_1.1.1
Hello ! I will have a look on this this weekend, could you write here the code used so that I can reproduce the bug ? Thank you very much for you interest in spNetwork !
Hey Jeremy,
Thanks for looking in to this! this is my code:
library(geojsonio) library(geojson) library(rgdal) library(stplanr) library(tidyverse) library(sf) library(spNetwork) library(spdep) library(sp)
cents_oa <- readOGR(file.path("01_DataInput/cents_london_oa/0_downloaded/Output_Areas__December_2011__Population_Weighted_Centroids.shp"))
nn_cents <- closest_points(cents_oa, cents_oa)
Response:
Error in closest_points(cents_oa, cents_oa) : could not find function "closest_points"
You can obtain the centroids from this site:
Great, thank you,
I am just a little bit puzzled , what do you want to do here? Do you want to find the closest neighbour of each point in the SpatialPointDataFrame cents_oa? To do so, do you want to use euclidean distance or network distance? In the second case, you will need a SpatialLineDataFrame representing the roads in your study area. In the first case, it is not necessary to use spNetwork, but a good old nearest neighbour search like :
require(fractal)
cents_oa$OID <- 1:nrow(cents_oa)
coords<- data.frame(coordinates(cents_oa))
coords$OID <- cents_oa$OID
neighbors <- findNeighbors(coords, n.neighbor = 1)I you want to use a road network, let me know, I could give you some code here to do it.
Indeed, nearest neighbour. Thank you Jeremy (!), I was not aware of fractal.
Would you mind giving me the code including the network-based search? Atm I was after Euclidean since that was obvious to me with closest_points. Thanks again in advance, appreciate it!
There is a lot of package for R, do not worry if you do not know one ;-).
With spNetwork, there is currently no dedicated function to find the nearest neighbour of an observation on a network, but it is possible to find it by first creating a spatial neighbouring matrix, and then extracting for each feature its nearest neighbour. Here is a reproductible example with the datasets included in the package.
library(spNetwork)
# loading the data
networkgpkg <- system.file("extdata", "networks.gpkg",package = "spNetwork", mustWork = TRUE)
mtl_network <- rgdal::readOGR(networkgpkg,layer="mtl_network", verbose=FALSE)
eventsgpkg <- system.file("extdata", "events.gpkg", package = "spNetwork", mustWork = TRUE)
bike_accidents <- rgdal::readOGR(eventsgpkg,layer="bike_accidents", verbose=FALSE)
# for each observation in bike_accidents, finding the neighbours within a 3000m radius (on network)
listw <- network_listw(bike_accidents,
mtl_network,
maxdistance=3000,
line_weight = "length",
dist_func = 'identity', # I use here the identity function to keep the network distances
matrice_type='W', grid_shape = c(2,2))
# and then extracting for each observation its nearest neighbour
neighbours <- sapply(1:length(listw$neighbours), function(i){
listw$neighbours[[i]][listw$weights[[i]] == min(listw$weights[[i]])][[1]]
})Thus, you will obtain a vector with the indices of the nearest observation for each row in bike_accidents.
If a big network is used, it could be great to use the multicore version (network_listw.mc) and a finer grid (with higher values in grid_shape).
If it is ok for you, I will close this issue
Wonderful! Thank you so much. Feel free to close the issue.
Perfect !
for completness, a new function has been added to the package today : network_knn, that will perform k-nearest neighbour search on a network without extra-code.