I have an irregular grid of points in the form (X,Y,Z), where (X,Y) are coordinates on a plane (can be geographical longitude/latitude), Z is some property to interpolate between points. I use akima interpolation package in R. The data set can contain missed values, and akima package does not like it. This can be remedied by a complete.cases() directive reorganizing the data set. But there is a following issue. Certain points contain no data in the sense that the interpolated quality is absent there (NA in R). As a closest example, Z is a depth of a stratigraphic interval, for example, Quaternary deposits. In these places I need to have a "hole" in the interpolated grid, showing that this layer is absent here; meanwhile the algorithm simply interpolates between available points with data.
#small extract from data
mydf<-read.csv(text="lon, lat, Q
411,362,1300
377,395.5,1425
427,370,1800
435.5,352,
428,357,
390,423,1700")
library("akima")
bbb<-data.frame(lon=mydf$lon,lat=mydf$lat,H=mydf$Q)
ccc<-bbb[complete.cases(bbb),]
Q.int<-interp(ccc$lon,ccc$lat,ccc$H,linear=TRUE,
extrap=FALSE,nx=240,ny=240,duplicate="mean")
Then it can be visualized, for example, with image.plot() from fields package
library("fields")
image.plot(Q.int)
In this data set, points 4,5 are lacking. This can be either 1) lack of data on these points, or 2) indication, that the deposits are absent here. I can note it in data set explicitly, for example, with NA symbol. But I need the interpolation, which distinguishes these two cases.
My solution was to interpolate "as is", and then to use a trick: declare that all interpolated values of a property Z on a grid <30 meters are effectively NA's, and then plot it:
Q.int$z.cut<-ifelse(Q.int$z<30,NA,Q.int$z)
This could reflect the geological situation, since layers with decreasing thickness indeed "fade out", and can be stripped on a map, but would it be possible to arrange this problem in a more elegant solution?
Related
I have a set of coordinates X and Y for my points and used the deldir to create determine and plot the Voronoi Polygons. (I've used this tutorial here)
This is my plot: (sorry that its so small, but you get the idea).
I need to determine the area of each polygon. How can I do that?
I looked up in the deldirpackage page and couldnt find anything related to the Voronoi polygons, only about other
Based on the reference manual (https://cran.r-project.org/web/packages/deldir/index.html), the output of the deldir function is a list. One of the list element, summary, is a data frame, which contains a column called dir.area. This is the the area of the Dirichlet tile surrounding the point, which could be what you are looking for.
Below I am using the example from the reference manual. Use $ to access the summary data frame.
library(deldir)
x <- c(2.3,3.0,7.0,1.0,3.0,8.0)
y <- c(2.3,3.0,2.0,5.0,8.0,9.0)
dxy1 <- deldir(x,y)
dxy1$summary
I've been working with a spatial model which contains 21,000 grid cells of unequal size (i by j, where i is [1:175] and j is[1:120]). I have the latitude and longitude values in two seperate arrays (lat_array,lon_array) of i and j dimensions.
Plotting the coordinates:
> plot(lon_array, lat_array, main='Grid Coordinates')
Result:
My question: Is it possible to plot these spatial coordinates as a grid rather than as points? Does anyone know of a package or function that might be able to do this? I haven't been able to find anything online to this nature.
Thanks.
First of all it is always a bit dangerous to plot inherently spherical coordinates (lat,long) directly in the plane. Usually you should project them in some way, but I will leave it for you to explore the sp package and the function spTransform or something like that.
I guess in principle you could simply use the deldir package to calculate the Dirichlet tessellation of you points which would give you a nice grid. However, you need a bounding region for this to avoid large cells radiating out from the border of your region. I personally use spatstat to call deldir so I can't give you the direct commands in deldir, but in spatstat I would do something like:
library(spatstat)
plot(lon_array, lat_array, main='Grid Coordinates')
W <- clickpoly(add = TRUE) # Now click the region that contains your grid
i_na <- is.na(lon_array) | is.na(lat_array) # Index of NAs
X <- ppp(lon_array[!i_na], lat_array[!i_na], window = W)
grid <- dirichlet(X)
plot(grid)
I have not tested this yet and I will update this answer once I get the chance to test it with some artificial data. A major problem is the size of your dataset which may take a long time to calculate the Dirichlet tessellation of. I have only tried to call dirichlet on dataset of size up to 3000 points...
I have a time series dataset with spatial data (x,y coordinates). Each point is static in location, but its value varies over time, ie. each point has its own unique function. I want to assign these functions as a mark, so I can plot the point pattern with each individual time series as a plotting symbol.
This is an exploratory step to eventually perform some spatial functional data analysis.
As an example, I want something like Figure 2 published in this article:
*Delicado,P., R. Giraldo, C. Comas, and J. Mateu. 2010. Spatial Functional Data: Some Recent Contibutions. Environmetrics 21:224-239
I'm having trouble posting an image of the figure
1) Working in R with ggplot2, I can plot a line of change in quant of each id over time:
(Fake example dataset, where x and y are Carteian coordinates, id is an individual observation, and quant are values of id at each year):
x<-c(1,1,1,2,2,2,3,3,3)
y<-c(1,1,1,2,2,2,3,3,3)
year<-c(1,2,3,1,2,3,1,2,3)
id<-c("a","a","a","b","b","b","c","c","c")
quant<-c(5,2,4,2,4,2,4,4,6)
allData<-data.frame(x,y,year,id,quant)
ggplot(allData,aes(x=year,y=quant, group=id))+geom_line()
2) Or I can plot the geographic point pattern of id:
ggplot(allData,aes(x=x,y=y,color=id))+geom_point()
I want to plot the graph from (2), but use the line plots from (1) as the point symbols (marks). Any suggestions?
I have two spatially projected raster objects that are NOT overlapping. One raster (rk) has reliable values and the values of the other one (dnn) is unreliable.
#reliable
plot(rk)
#unreliable
plot(dnn, add=TRUE, col='gray80')
In this figure (showing a subset of my data), the colors are representing the values of the reliable raster whereas I plotted all unreliable raster pixels as gray. I want to change the unreliable values based on the reliable values. An easier solution to the problem might be assigning the mean of the nearest neighbor values of the surrounding reliable pixels (mean) to the unreliable ones and thereafter merging those together.
I thought the first step might be finding a buffer around each of the gray cells, then finding the mean values within each buffer. However, to deal with the NAs (not including those in computation), I determined the non-NA indexes first
library(raster)
#selecting those index that are non-NAs
idx<-is.na(getValues(dnn))
idx<-which(idx==FALSE)
> length(idx)
[1] 602
#proving that the determined idx is correct
> sum(!is.na(getValues(dnn)))
[1] 602
But I'm getting an error while doing the buffering
#doing buffer for those idx only
for (i in idx[i]) {b(i)<-buffer(dnn[i], width=500)}
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘buffer’ for signature ‘"numeric"’
I'm not sure if this is the right way, even after getting the buffers, calculating the mean (I expect to use the zonal statistics) and assigning those to corresponding gray cells doesn't look like an easy task to me. Any suggestions??
Use a point-pattern approach. Treat each unreliable cell as an x,y coordinate, and all the reliable cells as x,y,Z values. Put the x,y,Z into a spatial interpolation algorithm, such as simple inverse distance weighting or less-simple Kriging, and then predict at the unreliable x,y locations. Put the predicted values back in the grid at the right place.
The automap package can help you here (also gstat and the R Spatial Task View).
Your comments, suggestions, or solutions are/will be greatly appreciated, thank you.
I'm using the fpc package in R to do a dbscan analysis of some very dense data (3 sets of 40,000 points between the range -3, 6).
I've found some clusters, and I need to graph just the significant ones. The problem is that I have a single cluster (the first) with about 39,000 points in it. I need to graph all other clusters but this one.
The dbscan() creates a special data type to store all of this cluster data in. It's not indexed like a data frame would be (but maybe there is a way to represent it as such?).
I can graph the dbscan type using a basic plot() call. But, like I said, this will graph the irrelevant 39,000 points.
tl;dr:
how do I graph only specific clusters of a dbscan data type?
If you look at the help page (?dbscan) it is organized like all others into sections labeled Description, Usage, Arguments, Details and Value. The Value section describes what the function dbscan returns. In this case it is simply a list (a standard R data type) with a few components.
The cluster component is simply an integer vector whose length it equal to the number of rows in your data that indicates which cluster each observation is a member of. So you can use this vector to subset your data to extract only those clusters you'd like and then plot just those data points.
For example, if we use the first example from the help page:
set.seed(665544)
n <- 600
x <- cbind(runif(10, 0, 10)+rnorm(n, sd=0.2), runif(10, 0, 10)+rnorm(n,
sd=0.2))
ds <- dbscan(x, 0.2)
we can then use the result, ds to plot only the points in clusters 1-3:
#Plot only clusters 1, 2 and 3
plot(x[ds$cluster %in% 1:3,])
Without knowing the specifics of dbscan, I can recommend that you look at the function smoothScatter. It it very useful for examining the main patterns in a scatterplot when you otherwise would have too many points to make sense of the data.
The probably most sensible way of plotting DBSCAN results is using alpha shapes, with the radius set to the epsilon value. Alpha shapes are closely related to convex hulls, but they are not necessarily convex. The alpha radius controls the amount of non-convexity allowed.
This is quite closely related to the DBSCAN cluster model of density connected objects, and as such will give you a useful interpretation of the set.
As I'm not using R, I don't know about the alpha shape capabilities of R. There supposedly is a package called alphahull, from a quick check on Google.