I am trying to write an R script to do pollution routing in world rivers, and need some help on selecting matrix cell coordinates and applying these to other matrices of the same dimension.
My data: I have several matrices corresponding to hydrological parameters of world rivers on a half degree grid (360 rows, 720 columns). These matrices represent flow accumulation (how many cells flow into this cell), flow direction (which of the 8 surrounding cells does the load of certain cell flow to) and pollutant load.
My idea: compute pollutant load in each grid cell from the start to the end of a river. I can base this on flow accumulation (low to high). However, each river basin can have multiple cells with the same flow accumulation value.
The problem: I need to select all matrix cells of each value of flow accumulation (low to high), find their coordinates (row,column), and transfer the corresponding pollutant load to the correct adjacent cell using the flow direction matrix. I have tried various ways, but selecting the coordinates of the correct cells and applying these to another matrix I cannot get to work.
I will give an example of what I have tried, using two for loops on one single river basin. In this example, a flow direction value of 1 means that the pollutant load needs to be transferred to the adjacent cell to the right (row is the same, column +1):
BasinFlowAccumulation <-FlowAccumulation[Basin]
BasinFlowAccumulationMaximum <- max(BasinFlowAccumulation)
BasinFlowDirection <-FlowDirection[Basin]
BasinPollutant <-Pollutant[Basin]
b<-0
for(i in 0:BasinFlowAccumulationMaximum){
cells.index<-which(BasinFlowAccumulation[]==b, arr.ind=TRUE)
for (j in 1:length(cells.index)){
print(BasinFlowDirection[cells[j]])
Row<-BasinPollutant[cells[j[1]]]
Column<-BasinPollutant[cells[j[2]]]
ifelse(BasinFlowDirection[cells.index[j]]==1, BasinPollutant[Row,(Column+1)]<-BasinPollutant[Row,(Column+1)]+Basinpollutant[Row,Column]
}
b<-b+1
}
Any advice would be greatly appreciated!
Related
I have a seemingly simple question that I can’t seem to figure out. I have a large dataset of millions of data points. Each data point represents a single fish with its biological information as well as when and where it was caught. I am running some statistics on these data and have been having issues which I have finally tracked down to some data points having latitude and longitude values that fall exactly on the corners of the grid cells which I am using to bin my data. When these fish with lats and long that fall exactly onto grid cell corners are grouped into their appropriate grid cell, they end up being duplicated 4 times (one for each cell that touches the grid cell corner their lats and long identify).
Needless to say this is bad and I need to force those animals to have lats and long that don’t put them exactly on a grid cell corner. I realize there are probably lots of ways to correct something like this but what I really need is a simply way to identify latitudes and longitudes that have integer values, and then to modify them by a very small amount (randomly adding or subtracting) so as to shift them into a specific cell without creating a bias by shifting them all the same way.
I hope this explanation makes sense. I have included a very simple example in order to provide a workable problem.
fish <- data.frame(fish=1:10, lat=c(25,25,25,25.01,25.2,25.1,25.5,25.7,25,25),
long=c(140,140,140,140.23,140.01,140.44,140.2,140.05,140,140))
In this fish data frame there are 10 fish, each with an associated latitude and longitude. Fish 1, 2, 3, 9, and 10 have integer lat and long values that will place them exactly on the corners of my grid cells. I need some way of shifting just these values by something like plus are minus 0.01.
I can identify which lats or longs are integers easy enough with something like:
fish %>%
near(as.integer(fish$lat))
But am struggling to find a way to then modify all the integer values by some small amount.
To answer my own question I was able to work this out this morning with some pretty basic code, see below. All it takes is making a function that actually looks for whole numbers, where is.integer does not.
# Used to fix the is.integer function to actually work and not just look at syntax
is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
# Use ifelse to change only whole number values of lat and long
fish$jitter_lat <- ifelse(is.wholenumber(fish$lat), fish$lat+rnorm(fish$lat, mean=0, sd=0.01), fish$lat)
fish$jitter_long <- ifelse(is.wholenumber(fish$long), fish$long+rnorm(fish$long, mean=0, sd=0.01), fish$long)
Assume X is a 5x5 matrix (which represents pixel luminosity from a picture):
I would like to select the element that fit within a given simple geometrical shape (e.g. square, circle, ovale) superposed to this matrix. In this example I would like to select the elements from the matrix that fall (even slightly) within the ovale.
Ultimately those elements position would be return into a vector logical vector elementInOvale on which I could perform a simple operation such as X[elementInOvale] <- 0
I have the feeling that this is a common problem which has already been solved, I just don't know how to formulate or where to find information about it, and to do with R.
I have a data frame that contains wifi download bandwidth and GPS data (latitude and longitude) on a transportation system. I want to determine from the data what the average bandwidth is when the vehicle is moving north, and what it is when it is moving south.
(bandwidth and latitude values from df)
bandwidth <- df$bandwidth
latitude <-df$latitude
(These both have 2800 entries)
(create empty vectors to fill with bandwidth values depending on whether the vehicle is moving north or south)
movingnorth <- vector('numeric')
movingsouth <- vector('numeric')
(If the train is moving north, fill the moving north vector with data from bandwidth vector)
for(y in latitude){
if(latitude[y]>= latitude[y+1]){
movingnorth <- c(movingnorth, received[y])}
}
Here, I am basically saying if the latitude value is going up, then the vehicle is moving north, and therefore enter the bandwidth value from that location into the movingnorth vector. I would expect only a portion of the values from bandwidth vector to be added to the movingnorth vector, but all 2800 values are added. What am I doing wrong here?
Take advantage of R's vectorized operations. First we use diff to find the change between successive elements of latitude
latitude_change <- diff(df$latitude)
Now we have a vector whose length is 1 less than the length of latitude. Direction happens between the measurements, so that makes sense. Let's say we won't determine direction for the first measurement. So that means if latitude_change[i] > 0, then the train's northbound at time i - 1.
df$movingnorth <- c(FALSE, latitude_change > 0)
I'm keeping this part of df because it's related information, so a table's the perfect place for it.
As lmo said, you want to use seq_along(latitude) or 1:length(latitude), which return the index instead of the actual element in latitude.
Also, you may want to double check that latitude[y+1] is correct. The current syntax assumes that the order of the latitude values in the data goes from the latest to the oldest. It is not possible to know if this is correct from the information you provide, but it may be the reverse.
As pointed out by Frank, you are growing your vector in a loop and that is bad practice (since it does not scale well and becomes very slow for large objects). Nathan Werth's answer suggests a vectorized implementation.
I've got a big problem.
I've got a large raster (rows=180, columns=480, number of cells=86400)
At first I binarized it (so that there are only 1's and 0's) and then I labelled the clusters.(Cells that are 1 and connected to each other got the same label.)
Now I need to calculate all the distances between the cells, that are NOT 0.
There are quiet a lot and that's my big problem.
I did this to get the coordinates of the cells I'm interested in (get the positions (i.e. cell numbers) of the cells, that are not 0):
V=getValues(label)
Vu=c(1:max(V))
pos=which(V %in% Vu)
XY=xyFromCell(label,pos)
This works very well. So XY is a matrix, which contains all the coordinates (of cells that are not 0). But now I'm struggling. I need to calculate the distances between ALL of these coordinates. Then I have to put each one of them in one of 43 bins of distances. It's kind of like this (just an example):
0<x<0.2 bin 1
0.2<x<0.4 bin2
When I use this:
pD=pointDistance(XY,lonlat=FALSE)
R says it's not possible to allocate vector of this size. It's getting too large.
Then I thought I could do this (create an empty data frame df or something like that and let the function pointDistance run over every single value of XY):
for (i in 1:nrow(XY))
{pD=PointDistance(XY,XY[i,],lonlat=FALSE)
pDbin=as.matrix(table(cut(pD,breaks=seq(0,8.6,by=0.2),Labels=1:43)))
df=cbind(df,pDbin)
df=apply(df,1,FUN=function(x) sum(x))}
It is working when I try this with e.g. the first 50 values of XY.
But when I use that for the whole XY matrix it's taking too much time.(Sometimes this XY matrix contains 10000 xy-coordinates)
Does anyone have an idea how to do it faster?
I don't know if this will works fast or not. I recommend you try this:
Let say you have dataframe with value 0 or 1 in each cell. To find coordinates all you have to do is write the below code:
cord_matrix <- which(dataframe == 1, arr.ind = TRUE)
Now, you get the coordinate matrix with row index and column index.
To find the euclidean distance use dist() function. Go through it. It will look like this:
dist_vector <- dist(cord_matrix)
It will return lower triangular matrix. can be transformed into vector/symmetric matrix. Now all you have to do is calculating bins according to your requirement.
Let me know if this works within the specific memory space.
I have a number of fishing boat tracks, and I'm trying to detect a certain pattern in their movement using R. In doing so I have reached a point where I have discarded all points of the track where the desired pattern is not occurring within a given time window, and I'm left with the remaining georeferenced points. These points have a score value associated, which measures the 'intensity' of the desired pattern.
track_1[1:10,]:
LAT LON SCORE
1 32.34855 -35.49264 80.67
2 31.54764 -35.58691 18.14
3 31.38293 -35.25243 46.70
4 31.21447 -35.25830 22.65
5 30.76365 -35.38881 11.93
6 30.75872 -35.54733 22.97
7 30.60261 -35.95472 35.98
8 30.62818 -36.27024 31.09
9 31.35912 -35.73573 14.97
10 31.15218 -36.38027 37.60
The code bellow provides the same data
data.frame(cbind(
LAT=c(32.34855,31.54764,31.38293,31.21447,30.76365,30.75872,30.60261,30.62818,31.35912,31.15218),
LON=c(-35.49264,-35.58691,-35.25243,-35.25830,-35.38881,-35.54733,-35.95472,-36.27024,-35.73573,-36.38027),
SCORE=c(80.67,18.14,46.70,22.65,11.93,22.97,35.98,31.09,14.97,37.60)))
Because some of these points occur geographically close to each other I need to 'pool' their scores together. Hence, I now need a way to throw this data into some kind of a spatial grid and cumulatively sum the scores of all points that fall in the same cell of the grid. This would allow me to find in what areas a given fishing boat exhibits the pattern I'm after the most (and this is not just about time spent in one place). Ultimately, the preferred output would contain lat and lon for every grid cell (center), and the sum of all scores on each cell. In addition, I would also like to be able to adjust the sizing of the grid cells.
I've looked around and all I can find either does not preserve the georeferenced information, is very inefficient, or performs binning of data. There may already be some answers out there, but it might be the case that I'm not able to recognize them since I'm a bit out of my league on this stuff. Can someone please point me to some direction (package, function, etc.)? Any guidance will be greatly appreciated.
Take your lat/lon coordinates, and multiply them by the inverse of your desired grid cell edge lengths, measured in degrees. The result will be a pair of floating point numbers whose integer part identifies the grid cell in question. Take the floor of these and you have two numbers describing the cell, which you could paste to form a single string. You may add that as a new factor column of your data frame. Then you can perform operations based on that factor, like summarizing values.
Example:
latScale <- 2 # one cell for every 0.5 degrees
lonScale <- 2 # likewise
track_1$cell <- factor(with(track_1,
paste(floor(LAT*latScale), floor(LON*lonScale), sep='.')))
library(plyr)
ddply(track_1, .(cell), summarize,
LAT=mean(LAT), LON=mean(LON), SCORE=sum(SCORE))
If you want to, you can use weighted.mean instead of mean. If you don't like these factors, you can put more effort in making them nice (e.g. by using compass directions instead of signs), or drop them altogether and use a pair of integer columns instead.