[R-sig-Geo] [FORGED] Create a Spatial Weight Matrix based on road distance

Wed Jun 26 20:44:13 CEST 2019

Dear Prof. Roger, many thanks for your help, I am working on.

I have a doubt: Which is the differece between Great Circle and Euclidean
Distance?

Thanks

El lun., 24 de jun. de 2019 a la(s) 00:58, Roger Bivand (Roger.Bivand using nhh.no)
escribió:

> On Mon, 24 Jun 2019, Rolando Valdez wrote:
>
> > Dear Andres,
> >
> > I could follow an example provided with the package, it was a little bit
> > simple, however, I got this message:
> >
> >> dists <- osrmTable(loc = muns13, measure = "duration")
> > The OSRM server returned an error:
> > Error: The public OSRM API does not allow results with a number of
> > durations
> > higher than 10000. Ask for fewer durations or use your own server and set
> > its
> > --max-table-size option.
> >
> > My sample size is 2,457
>
> Were you asking for the whole nx(n-1)/2 set of durations in one run? You
> see that the API limits the number of interactions (possibly by time and
> by unique IP number). So you need to reduce the number of queries. If you
> are going to impose a distance threshold anyway, you can do that using the
> Great Circle (not Euclidean) distances between your geographical
> coordinates and dnearneigh(), and step through the nb list object with
> src= being the data frame of the observation coordinates, and dest= the
> data frame of the neighbours' coordinates.
>
> library(sf)
> nc <- st_read(system.file("shapes/sids.shp", package="spData")[1],
> quiet=TRUE)
> st_crs(nc) <- "+proj=longlat +datum=NAD27"
> nc1 <- st_transform(nc, 32019)
> nc2 <- st_centroid(nc1, of_largest_polygon=TRUE)
> nc3 <- st_transform(nc2, 4326)
> crds <- st_coordinates(st_geometry(nc3))
> df <- data.frame(id=nc3$FIPSNO, long=crds[,1], lat=crds[,2])
> library(spdep)
> nb <- dnearneigh(crds, 0, 50, longlat=TRUE)
> library(osrm)
> res <- vector(mode="list", length=nrow(df))
> for (i in seq(along=res)) res[[i]] <- osrmTable(src=df[i,],
>   dst=df[nb[[i]],], measure = "duration")
> res1 <- lapply(res, function(x) 10/x$duration)
> lw <- nb2listw(nb, glist=res1, style="B")
>
> gives general spatial weights based on 10/# minutes travel time between
> county centroids (centroids calculated from projected coordinates), for
> county centroids closer than 50 km measured by Great Circle.
>
> In your case, you may need to split the for() loop into portions of
> cumsum(card(nb)) of less than the limit. If durations are symmetric,
> you could also halve the query count by taking only neighbour ids > i,
> but you'd have to fold them back afterwards. You also wanted to categorise
> the durations into 0,1, which you could do with lapply() instead of using
> inverse durations.
>
> Hope this helps,
>
> Roger
>
> >
> > El vie., 21 de jun. de 2019 a la(s) 02:53, Andres Diaz Loaiza (
> > madiazl using gmail.com) escribió:
> >
> >> Dear Rolando,
> >>
> >> The advantage of using Open Street Maps engine is that you can give the
> >> travel option. This means you can select whether are you traveling by
> bike,
> >> car or walking. The previous approach didn't consider this topic. For
> this,
> >> you should have added a vector layer depending on your position of the
> >> street you can take (or not). Open Street Maps project allow you two
> >> options: a service in which you give your current position, the position
> >> you want to reach and your transport method (giving you back the fastest
> >> route). Or the option to download the engine/algorithm compile by
> yourself
> >> (if I am not wrong is made in C or python) and then you can make your
> own
> >> calculation at your own computer. For the first option, the package
> OSRM is
> >> an interface in which send a request to the OSM web page and wait for an
> >> answer. With this method, you can send a couple of request at the same
> time
> >> but no to many (you should read the manual for this). Of course, also
> will
> >> depend on whether the OSM server is down or not (or busy).
> >>
> >> I have to say that I used some years ago this app and nowadays I know
> that
> >> for some cities OSM has more streets reported than the same google maps.
> >> Also is an open project and they let you download their data for free,
> >> contrary to what google maps do.
> >>
> >> All the best,
> >>
> >>
> >> Andres
> >>
> >> El vie., 21 jun. 2019 a las 4:30, Adrian Baddeley (<
> >> adrian.baddeley using curtin.edu.au>) escribió:
> >>
> >>> Rather than converting an object of class 'SpatialLines' or
> >>> 'SpatialLinesDataFrame' to the spatstat class 'psp' and then
> converting it
> >>> to the spatstat class 'linnet', it is safer and more efficient to
> convert
> >>> the SpatialLines* object directly to class linnet using
> >>> as.linnet.SpatialLinesDataFrame() from the package 'maptools'.
> >>>
> >>>
> >>> Prof Adrian Baddeley DSc FAA
> >>>
> >>> John Curtin Distinguished Professor
> >>>
> >>> Department of Mathematics and Statistics
> >>>
> >>> Curtin University, Perth, Western Australia
> >>>
> >>>
> >>> ________________________________
> >>> From: Rolf Turner <r.turner using auckland.ac.nz>
> >>> Sent: Friday, 21 June 2019 10:08 AM
> >>> To: Rolando Valdez
> >>> Cc: r-sig-geo using r-project.org; Adrian Baddeley; Ege Rubak
> >>> Subject: Re: [FORGED] [R-sig-Geo] Create a Spatial Weight Matrix based
> on
> >>> road distance
> >>>
> >>>
> >>> On 21/06/19 12:26 PM, Rolando Valdez wrote:
> >>>
> >>>> Dear community,
> >>>>
> >>>> Is there any way to create a spatial weight matrix based on road
> >>> distance?
> >>>> I am trying to use the road distance between two points instead of
> >>>> euclidean distance.
> >>>>
> >>>> I've seen that there is a package named osrm. Can anyone give some
> >>> advice?
> >>>
> >>> I don't know anything about "osrm".  Calculating "road distances" can
> be
> >>> done in the spatstat package reasonably easily, if you take the trouble
> >>> to represent your collection of roads as a "linnet" object.
> >>>
> >>> Given that you have done so, suppose that your linnet object is "L" and
> >>> that you have vectors "x" and "y" specifying the points on L (i.e. on
> >>> your roads) between which you want to know the distances.
> >>>
> >>> Do:
> >>>
> >>>      X    <- lpp(data.frame(x=x,y=y),L)
> >>>      dMat <- pairdist(X)
> >>>
> >>> The object "dMat" is a (symmetric) square matrix; dMat[i,j] is the
> >>> distance between point i and point j.  (Of course the diagonal entries
> >>> are all 0.)
> >>>
> >>> If your collection of roads is specified by means of a shapefile,
> >>> vignette("shapefiles") will tell you how to turn this collection into a
> >>> "psp" ("planar segment pattern") object; the function (method)
> >>> as.linnet.psp() can then be used to turn the "psp" object into a
> >>> "linnet" object.
> >>>
> >>> HTH
> >>>
> >>> cheers,
> >>>
> >>> Rolf Turner
> >>>
> >>> --
> >>> Honorary Research Fellow
> >>> Department of Statistics
> >>> University of Auckland
> >>> Phone: +64-9-373-7599 ext. 88276
> >>>
> >>>         [[alternative HTML version deleted]]
> >>>
> >>> _______________________________________________
> >>> R-sig-Geo mailing list
> >>> R-sig-Geo using r-project.org
> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> >>>
> >>
> >>
> >> --
> >> Andrés D.
> >>
> >
> >
> >
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
> https://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en

-- 
Rol~

	[[alternative HTML version deleted]]