Perhaps, rangeMapper can be of use. It is using spsample(, type =
"regular") and over() so it is similar with Roger's example below.
# Species richness of American wrens -----
require(rangeMapper); require(RSQLite); require(rgdal)
dbcon = rangeMap.start(file = "test.sqlite", overwrite = TRUE, dir =
tempdir() )
f = system.file(package = "rangeMapper", "extdata", "wrens",
"vector_combined")
r = readOGR(f, "wrens")
global.bbox.save(con = dbcon, bbox = f)
gridSize.save(dbcon, gridSize = 2) ; canvas.save(dbcon)
processRanges(spdf = r, con = dbcon, ID = "sci_name" )
rangeMap.save(dbcon) # FUN is missing so species richness is computed by
default. see ?rangeMap.save for other args
m = rangeMap.fetch(dbcon)
plot(m)
####
Mihai
On 3/12/2014 11:00 AM, Roger Bivand wrote:
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
--
+-----------------------------------------------+
Mihai Valcu, PhD
Max Planck Institute for Ornithology
Behavioural Ecology and Evolutionary Genetics
Eberhard-Gwinner Street 7
D-82319 Seewiesen
o Phone: +49-(0)8157-932-343
o Fax: +49-(0)8157-932-400
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o http://www.orn.mpg.de/valcu
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