Initial example below, please scroll down:
On Wed, 12 Mar 2014, Roger Bivand wrote:
On Wed, 12 Mar 2014, Alice C. Hughes wrote:
Please do not take threads off-list - I am not offering private advice, and
what is said on the list is intended to be public so that others can
contribute.
Hi
Thanks so much for the informative answer-but I'm not certain if it will
yield what I am looking for-basically I have distribution range polygons
for multiple species and I want to collapse them down to give the number or
species sugested to be in an area-whether that be 1 or 1000 in map form-so
basically you have a "heat-map" or species occurences: which tool would
yield this? I have all the polygons in one shapefile at present in
geographic projection and WGS 84, decimal degrees Thanks so much for your
help
The whole point of using R is that users are encouraged to create and modify
tools from parts (aka functions or methods). Most research problems are
coerced into fixed subsets of problems that can be tackled with "other
people's" tools. If researchers are to take responsibility for actually
tackling research questions with the best alignment of data collection, data
representation, workflow in data handling, analysis, it is their
respopnsibility to ensure that the tools are as well aligned with the problem
as possible. Otherwise, your ability to infer about the actual processes will
have been amputated by the limitations imposed by using tools not matching
your problem.
Change of support is particularly important here, so your ourput map of
species incidence (don't call it a heatmap, it isn't measuring heat) depends
crucially on steps in the workflow (for example, what is the error involved
in delineating the range polygons?).
I was mislead by your phrasing to think that you also needed the areas of
each species x species overlap (counts are a bit trivial), and you haven't
said what you mean by area (is it a measure, a polygon, or a raster cell?).
What does: "number o[f] species sugested to be in an area" mean? Do you mean
a raster cell? Or the polygonal intersection output for each unique count? Or
what?
gOverlaps(..., byid=TRUE) should give counts (it returns a matrix, and the
rows/columns should be the counts if the polygons are species). However, if
you have many polygons, do use the STR tree approach only to search for
overlaps among polygons that actually have intersecting bounding boxes. You
can count the number of overlaps in this way. If someone can provide random
overlapping polygons in a SpatialPolygons object, it would be easier to show.
All R lists do ask for reproducible cases with built-in or simulated data.
A first cut at an example:
library(sp)
library(rgeos)
box <- readWKT("POLYGON((0 0, 0 1000, 1000 1000, 1000 0, 0 0))")
plot(box)
set.seed(1)
pts <- spsample(box, n=2000, type="random")
pols <- gBuffer(pts, byid=TRUE, width=50)
plot(pols, add=TRUE)
STR <- gBinarySTRtreeQuery(pols, pols)
length(STR)
summary(sapply(STR, length))
res <- vector(mode="list", length=length(STR))
for(i in seq(along=STR)) res[[i]] <- gOverlaps(pols[i], pols[STR[[i]]],
byid=TRUE)
summary(sapply(res, sum)-1) # to remove self-counting, i overlaps i
So then we have # overlap counts per input polygon - but what are the
output reporting units? Should we actually be counting the number of
polygons that a fine grid of points over box belong to? If we focus on
this, then maybe:
GT <- GridTopology(c(0.5, 0.5), c(1, 1), c(1000, 1000))
SG <- SpatialGrid(GT)
o <- over(SG, pols, returnList=TRUE)
ct <- sapply(o, length)
summary(ct)
SGDF <- SpatialGridDataFrame(SG, data=data.frame(ct=ct))
spplot(SGDF, "ct", col.regions=bpy.colors(20))
is closer? I haven't checked why the overlaps and the grid over counts
differ - homework for someone? How does the output resolution affect the
detected number of hits?
Roger
Roger
Alice
Alice C. HughesAssociate ProfessorCentre for Integrative
Conservation,Xishuangbanna Tropical Botanical Garden,Chinese Academy of
SciencesMenglun, Mengla, Yunnan 666303, P.R. ChinaPh:
[email protected]
Date: Tue, 11 Mar 2014 20:56:02 +0100
From: [email protected]
To: [email protected]
CC: [email protected]
Subject: Re: [R-sig-Geo] overlapping polygons in shapefile
On Tue, 11 Mar 2014, Alice C. Hughes wrote:
Hi All
This should be simple-but ArcGis keeps getting stuck at 22%...I have a
large shapefile, which contains multiple overlapping polygons for
various species-and I would like to count for any area how many polygons
overlap.A script would be really fantastic!Thanks in advance
Wouldn't it just? However, as usual, such scripts will only cause grief,
as they will not (necessarily) match your workflow. Most likely, you
have two choices, one to rasterise thr polygons separately (not
together, because they overlap), then count overlapping cells.
If the shapefile is "large", like tens of thousands of polygons, you could
try the rgeos package, first using gUnarySTRtreeQuery() or
gBinarySTRtreeQuery() to identify possible overlapping candidates (these
functions report intersecting bounding boxes of polygons).
Next try gOverlaps() subsetting the input objects by the output of the
intersecting bounding boxes. Once you've found the overlaps, take their
intersections with gIntersection(), and get the area with gArea(). Note
that this only applies to planar, projected polygons. If they are in
geographical coordinates, project before using gArea(). Look carefully at
the byid= arguments in gOverlaps, gIntersection and gArea. Depending on
the complexity of the setting, you may be using for loops and subsetting
by indices - there are examples in Ch. 5 of ASDAR (2nd edition) of this
kind of subsetting (or see the online code at www.asdar-book.org), about
chunks 41-3 in http://www.asdar-book.org/book2ed/cm2_mod.R.
Hope this helps,
Roger
Alice
Alice C. HughesAssociate ProfessorCentre for Integrative
Conservation,Xishuangbanna Tropical Botanical Garden,Chinese Academy of
SciencesMenglun, Mengla, Yunnan 666303, P.R. ChinaPh:
[email protected]
Date: Mon, 6 May 2013 14:33:29 +1000
Subject: Re: [Trop-R] Kernel Density Estimation Bootstrap
From: [email protected]
To: [email protected]
Hi Christian,
This formatting shows that the data has been read in as a single column
with 'V1' as the column name and 'x,y' as the value of row 1, and so on:
test<-read.table("test.csv")> test
V11 x,y
2 648501,89104003 641686,8917264
4 645984,89143285 647058,8913794
6 649659,89129257 649613,8912828
.....
You can check how many columns there are in a data frame using 'ncol':
ncol(test)
To troubleshoot, I would first try using read.csv:
test=read.csv(test,as.is=TRUE)ncol(test)
If this does not give you two columns, then I would check that the file
really is in standard .csv format.
Lauren
On Mon, May 6, 2013 at 1:17 PM, Christian Gredzens
<[email protected]> wrote:
Leila,
Thanks for your reply. I'm using likelihood cross validation (CVh)
instead of least squares cross validation (LSCV). To my knowledge
adehabitatHR doesn't run CVh so I think I will need to write a script or
find another program which can calculate CVh.
I used GME from spatialecology.com to calculate all of my KDEs, but I
don't think GME runs bootstrapping. So I'm trying to run it through the
bootstrapping package in R. Here is what I need to do:
1) Create my spatial points layer
=mydata
2) Run the bootstrapping script:
mydataboot<-boot(mydata, (KDE script), R=1000)
mydata= my spatial points file
(KDE script)= what formula to run the bootstrap on
R=1000 = the number of iterations I would like to run
The issue I'm having is getting the bootstrapping script to run the KDE
formula=(KDE script)
Cheers,
Christian
On Friday, May 3, 2013 12:58:55 AM UTC-7, Leila Brook wrote:
Hi Christian,
There is a package called adehabitatHR that does kernel density
estimation with LSCV bandwidth using a function called kernelUD(). I
don't know if this will is similar to what you want? You will need to
have the data in UTM and in SpatialPoints or SpatialPointsDataFrame
format, which you can do using the sp package.
There is a great vignette for adehabitatHR that will help with data
formatting and running the KDE. I exported the utilisation distribution
for ArcGIS using writeRaster() in the raster package, but you can also
export as contours.
Cheers
Leila
________________________________________
From: [email protected] [[email protected]] on behalf of
Stewart Macdonald [[email protected]]
Sent: Friday, 3 May 2013 5:12 PM
To: [email protected]
Subject: Re: [Trop-R] Kernel Density Estimation Bootstrap
Hi Christian,
What sort of data are you trying to import? A series of lat/longs that
are in a text (or Excel) file? If so, the rGDAL library can be used to
create spatial points:
###############
library(rgdal)
# if you don't already have this installed, you can do:
install.packages('rgdal')
# read in the data from a tab-delimited text file that has a header line
rawPoints <- read.table('/data.csv', header=TRUE, sep='\t')
# format as a SpatialPoint class and define the projection/datum (WGS84)
q <- cbind(rawPoints$recordLon, rawPoints$recordLat)
colnames(q) <- c("x", "y")
qqq <- SpatialPoints(q, proj4string=CRS("+init=epsg:4326"))
###############
Note: the above is a copied-and-pasted fragment from a working script of
mine. I assume this fragment will work for you.
I can't help you with your second question, and the answer to your third
question will depend on the format of the bootstrap data (e.g., more
points, polygons, raster).
Hope this helps,
Stewart
On 03/05/2013, at 4:57 PM, Christian Gredzens
<[email protected]> wrote:
Hi All,
I would like to run a bootstrap within R on a spatial dataset using a
kernel density estimation function. Can anyone help me with this? I am
new to R and thus have very little experience. It seems relatively easy
to run the actual bootstrap but I am having problems in the following
areas:
1) I don't know how to import spatial data into R and assign it an
appropriate projection
2) I need to setup a kernel function using a specific bandwidth
(likelihood cross-validation (CVh)) and create the script so it will run
within the bootstrap script
3) I need to export the bootstrap data as an ArcGIS 10 file.
Best Regards,
Christian Gredzens
Christian Gredzens
MSc Student
School of Marine and Tropical Biology
James Cook University
Townsville, QLD, Australia 4811
[email protected]
Phone: 04 3875 3652
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voice: +47 55 95 93 55; fax +47 55 95 95 43
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