Finding the nearest distance between two SpatialPointsDataframes using gDistance rgeos? - r

Question

I have two separate but related questions.
First, I would like to determine the distance to the nearest construction site (construction_layer.csv) for every data point within the subset_original_data.csv file. I am trying to use the gDistance() function to calculate the nearest neighbor, but I am open to other ideas as well.
I want to append my subset_original_data.csv dataframe with this new vector of nearest neighbor distances from the construction_layer.csv. That is, for every row of my subset_original_data.csv dataframe, I want the minimum distance to the nearest construction site.
The second goal is to determine the nearest distance from each subset_original_data.csv row to a freeway shapefile (fwy.shp). I would also like to append this new vector back onto the subset_original.csv dataframe.
I have successfully converted the construction_layer.csv and subset_original_data.csv into SpatialPointsDataFrame. I have also converted the fwy.shp file into a SpatialLinesDataFrame by reading in the shape file with the readOGR() function. I am not sure where to go next. Your input is greatly appreciated!
~ $ spacedSparking
Here's my data:
construction_layer.csv, fwy.shp, subset_original_data.csv
Here's my code:
#requiring necessary packages:
library(rgeos)
library(sp)
library(rgdal)
#reading in the files:
mydata <- read.csv("subset_original_data.csv", header = T)
con <- read.csv("construction_layer.csv", header = T)
fwy <- readOGR(dsn = "fwy.shp")
#for those who prefer not to download any files:
data.lat <- c(45.53244, 45.53244, 45.53244, 45.53244, 45.53245, 45.53246)
data.lon <- c(-122.7034, -122.7034, -122.7034, -122.7033, -122.7033, -122.7032)
data.black.carbon <- c(187, 980, 466, 826, 637, 758)
mydata <- data.frame(data.lat, data.lon, data.black.carbon)
con.lat <- c(45.53287, 45.53293, 45.53299, 45.53259, 45.53263, 45.53263)
con.lon <- c(-122.6972, -122.6963, -122.6952, -122.6929, -122.6918, -122.6918)
con <- data.frame(con.lat, con.lon)
#I am not sure how to include the `fwy.shp` in a similar way,
#so don't worry about trying to solve that problem if you would prefer not to download the file.
#convert each file to SpatialPoints or SpatialLines Dataframes:
mydata.coords <- data.frame(lon = mydata[,2], lat = mydata[,1], data = mydata)
mydata.sp <- sp::SpatialPointsDataFrame(mydata.coords, data = data.frame(BlackCarbon = mydata[,3])) #appending a vector containing air pollution data
con.coords <- data.frame(lon = con[,2], lat = con[,1])
con.sp <- sp:SpatialPointsDataFrame(con.coords, data = con)
str(fwy) #already a SpatialLinesDataFrame
#Calculate the minimum distance (in meters) between each observation between mydata.sp and con.sp and between mydata.sp and fwy objects.
#Create a new dataframe appending these two nearest distance vectors back to the original mydata file.
#Desired output:
head(mydata.appended)
LATITUDE LONGITUDE BC6. NEAREST_CON (m) NEAREST_FWY (m)
1 45.53244 -122.7034 187 ??? ???
2 45.53244 -122.7034 980 ??? ???
3 45.53244 -122.7034 466 ??? ???
4 45.53244 -122.7033 826 ??? ???
5 45.53245 -122.7033 637 ??? ???
6 45.53246 -122.7032 758 ??? ???
EDIT:
SOLUTION:
When in doubt, ask a friend who is an R wizard! He even made a map.
library(rgeos)
library(rgdal)
library(leaflet)
library(magrittr)
#Define Projections
wgs84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0")
utm10n<-CRS("+proj=utm +zone=10 +ellps=GRS80 +datum=NAD83 +units=m +no_defs +towgs84=0,0,0")
#creating example black carbon data by hand:
lat <- c(45.5324, 45.5325, 45.53159, 45.5321, 45.53103, 45.53123)
lon <- c(-122.6972, -122.6963, -122.6951, -122.6919, -122.6878, -122.6908)
BlackCarbon <- c(187, 980, 466, 826, 637, 758)
bc.coords <- data.frame(lat, lon, BlackCarbon)
bc<-SpatialPointsDataFrame(data.frame(x=lon,y =lat),data=data.frame(BlackCarbon),proj4string = wgs84)
# Project into something - Decimal degrees are no fun to work with when measuring distance!
bcProj<-spTransform(bc,utm10n)
#creating example construction data layer:
con.lat <- c(45.53287, 45.53293, 45.53299, 45.53259, 45.53263, 45.53263)
con.lon <- c(-122.6972, -122.6963, -122.6952, -122.6929, -122.6918, -122.6910)
con.coords <- data.frame(con.lat, con.lon)
con<-SpatialPointsDataFrame(data.frame(x=con.lon,y =con.lat),data=data.frame(ID=1:6),proj4string = wgs84)
conProj<-spTransform(con,utm10n)
#All at once (black carbon points on top, construction on the y-axis)
dist<-gDistance(bcProj,conProj,byid=T)
min_constructionDistance<-apply(dist, 2, min)
# make a new column in the WGS84 data, set it to the distance
# The distance vector will stay in order, so just stick it on!
bc#data$Nearest_Con<-min_constructionDistance
bc#data$Near_ID<-as.vector(apply(dist, 2, function(x) which(x==min(x))))
#Map the original WGS84 data
pop1<-paste0("<b>Distance</b>: ",round(bc$Nearest_Con,2),"<br><b>Near ID</b>: ",bc$Near_ID)
pop2<-paste0("<b>ID</b>: ",con$ID)
m<-leaflet()%>%
addTiles()%>%
addCircleMarkers(data=bc,radius=8,fillColor = 'red',fillOpacity=0.8,weight=1,color='black',popup=pop1)%>%
addCircleMarkers(data=con,radius=8,fillColor = 'blue',fillOpacity=0.8,weight=1,color='black',popup=pop2)
m

Answer 1

You can use the a haversine distance function and use functional programming to achieve the desired result.
library(geosphere)
find_min_dist <- function(site, sites) {
min(distHaversine(site, sites))
}
#X is the data id, split into a list so you can iterate through each site point
data <- split(mydata[ , 3:2], mydata$X)
sapply(data, find_min_dist, sites = con.coords)