I'm trying to extract the coordinates of 50m squares and the flooding factor associated with that square, from data provided by the Environment agency, here (https://data.gov.uk/dataset/risk-of-flooding-from-rivers-and-sea1). I've downloaded the shapefile format, when you click through from that page to this one (http://environment.data.gov.uk/ds/catalogue/#/8d57464f-d465-11e4-8790-f0def148f590).
The data claims to show the flooding factor for each 50m square. I'm completely new to Shapefiles and also new to R. From what I've read I expect the 50m squares to be represented by 'Polygons' and have viewed the Polygons using
polys <- slot(data,"polygons")
and then printing the coordinates of a few of them using
for (i in 1:length(polys)) {
print(paste("Polygon #",i))
print(slot(slot(polys[[i]],"Polygons")[[1]],"coords" ))
}
I'm confused by the output as I assumed a square would be specified by four points, however, the number of (pairs of) coordinates specifying the polygons varies greatly.
Is this assumption correct? Or does the data not consist of 50m squares as it claims?
If they are indeed 50m squares, is there an easy way to extract the coordinates of the centre of the polygons and their IDs?
Related
I use density.lpp for kernel density estimation. I want to pick specific segment in that and plot the estimation through chosen segment. As an example, I have a road which is a combination of two segments. each segments have different length so I don't know how many pieces each of them are divided by.
here is the locations of vertices and road segment ids.
https://www.dropbox.com/s/fmuul0b6lus279c/R.csv?dl=0
here is the code I used to create spatial lines data frame and random points on the network and get density estimation.
Is there a way to know how many pieces each segment divided by? OR if I want to plot locations vs estimation for chosen segment how can I do? Using dimyx=100 created 199 estimation points but I don't know how many of them belongs to Swid=1 or Swid=2.
One approached I used was, using gDistance it works fine in this problem because these segments connected to one directions however, when there is 4 ways connection, some of the lambda values connects to another segments which is not belongs to that segment. I provided picture and circled 2 points, when I used gDistance, those points connected to other segments. Any ideas?
R=read.csv("R.csv",header=T,sep=",")
R2.1=dplyr::select(R, X01,Y01,Swid)
coordinates(R2.1) = c("X01", "Y01")
proj4string(R2.1)=CRS("+proj=utm +zone=17 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0")
plot(R2.1,main="nodes on the road")
##
LineXX <- lapply(split(R2.1, R2.1$Swid), function(x) Lines(list(Line(coordinates(x))), x$Swid[1L]))
##
linesXY <- SpatialLines(LineXX)
data <- data.frame(Swid = unique(R2.1$Swid))
rownames(data) <- data$Swid
lxy <- SpatialLinesDataFrame(linesXY, data)
proj4string(lxy)=proj4string(trtrtt.original)
W.1=as.linnet.SpatialLines(lxy)
Rand1=runiflpp(250, W.1)
Rand1XY=coords(Rand1)[,1:2]
W2=owin(xrange=c(142751.98, 214311.26), yrange=c(3353111, 3399329))
Trpp=ppp(x=Rand1XY$x, y=Rand1XY$y, window=W2) ### planar point object
L.orig=lpp(Trpp,W.1) # discrete
plot(L.orig,main="Original with accidents")
S1=bw.scott(L.orig)[1] # in case to change bandwitdh
Try274=density(L.orig,S1,distance="path",continuous=TRUE,dimyx=100)
L=as.linnet(L.orig)
length(Try274[!is.na(Try274$v)])
[1] 199
This is a question about the spatstat package.
The result of density.lpp is an object of class linim. For any such object, you can use as.data.frame to extract the data. This yields a data frame with one row for each sample point on the network. For each sample point, the data are xc, yc (coordinates of nearest pixel centre), x,y (exact coordinates of sample point on network), seg (identifier of segment), tp (relative position along segment) and values (the density value). If you split the data frame by the seg column, you will get the data for invididual segments of the network.
However, it seems that you may want information about the internal workings of density.lpp. In order to achieve adequate accuracy during the computation phase, density.lpp subdivides each network segment into many short segments (using a complex set of rules). This information is lost when the final results are discretised into a linim object and returned. The attribute "dx" reports the length of the short segments that were used in the computation phase, but that's all.
If you email me directly I can show you how to extract the internal information.
I would like to generate vector arrows that conform to the topography/slope of a raster dataset of a river catchment area.
I have created a Fishnet grid of points in ArcGIS and I would like to create a single arrow for each point of a set length that will follow the shape of the slope i.e. follow the path of least resistance, the line will follow progressively small numbers in a 3 x 3 grid.
I think I can generate the vector arrows using vector plot. Is it possible to achieve the lines conforming to the raster?
UPDATE: I have ~200,000 lines that I generated from a grid of points. I am going to turn these into a raster using R and set it to the same resolution as my slope raster.
Any ideas on how to layer the raster lines on the slope so I can get the lines to follow the lowest values of the slope?
This is for display/mapping purposes only? Use a DEM or TIN and display your arrow lines in ArcScene.
EDIT: given your update about your data and software not working-
Try this:
1) Make a raster surface covering the extent of your data with a cell size of 100m (or smaller or larger if that doesn't suit)
2) Convert that raster to a polygon layer e.g. 'area_grid100m'
3) Do a spatial join and assign all points a polygon cell id from one of the unique id fields in 'area_grid100m'
4) Use Summarize to get the mean lat/long of the start points and mean lat/long of the end points for each polygon. Summarize on the polygon id field and get select mean for both the lat and long fields
5) Add summary table to ArcMap, right click and select Display XY Data (set X Field as longitude and y Field as latitude). Right right the result and select Data > Export Data to make it permanent. You will now have two points per 'area_grid100m' cell.
5) Recreate your lines using this new file, which will give you one line per cell
If the res is not small enough, make the 'area_grid' cells smaller.
I'm trying to use distanceFromPoints function in raster package as:
distanceFromPoints(object,xy,...)
Where, object is raster and xy is matrix of x and y coordinates
Now, if my raster has, for example, 1000 cells and xy represents one point, I get 1000 values representing distances between xy and each raster cell. my problem is when xy has multiple coordinates, e.g., 10 points. the function description indicates that xy can be multiple points but when I run this function with multiple XY points, I still get only 1000 values while I'm expecting 1000 values for each coordinate in XY. How does this work?
Thanks!
using distanceFromPoints on multiple points gives a single value for each raster cell, which is the distance to the nearest point to that cell.
To create raster layers giving the distance to each point separately, you can use apply
a reproducible example:
r = raster(matrix(nrow = 10, ncol = 10))
p = data.frame(x=runif(5), y=runif(5))
dp = apply(p, 1, function(p) distanceFromPoints(r,p))
This gives a list of raster layers, each having the distance to one point
# for example, 1st raster in the list has the distance to the 1st point
plot(dp[[1]])
points(p[1,])
For convenience, you can convert this list into a raster stack
st = stack(dp)
plot(st)
A final word of caution:
It should be noted that the raster objects thus created do not really contain any more information than the list of points from which they are generated. As such, they are a computationally- and memory-expensive way to store that information. I can't easily think of any situation in which this would be a sensible way to solve a specific question. Therefore, it may be worth thinking again about the reasons you want these raster layers, and asking whether there may be a more efficient way to solve you overall problem.
I am using the Spatstat package in R for spatial point analysis. My dataset comprises location coordinates i.e. latitude and longitude of some event upto 6 places of decimal. It has some 9898 observations.
Here`s the output of the summary for the point pattern:
Planar point pattern: 9898 points
Average intensity 149786.3 points per square unit
Coordinates are given to 6 decimal places
units
Window area = 0.0660808 square units
My question is that how can the Average Intensity value be so huge? Or is my approach of creating a point pattern is wrong? Please help!
You are using a geographic coordinate system coordinates which spatstat doesn't support.
The coordinates are simply interpreted as units and since the window area is only 0.0660803 square units the point density is extrapolated to an average intensity of 149786.3 points per square unit.
Have a look at this thread how you convert (project) your coordinates to points on a flat map:
Unit length in spatstat
I am looking to calculate the distance between points (about 47K) and the closest X countries (of all world countries). I have imported the lat/long of points as SpatialPoints, and loaded a world map as a SpatialPolygons. I think I could build off of the advice given here:
SpatialLinesDataFrame: how to calculate the min. distance between a point and a line
It looks like I have to calculate the distance between all countries and all points and then extract the X closest, which is a bit intense with so many points.
In short, is there a way to impose a polygon limit? If not, what would you suggest- my only thought is to import a smaller number of points and then loop through this code (I am a new R user).
Thanks!