[R-sig-Geo] How set up spatstat::densityHeat()?

[Michal Kvasnička]

Dear Rolf.

Thanks a lot for the suggestions! I'll check it out.

Best wishes to you and all spatstat team,
*Ing. Michal Kvasnička, Ph.D.*



*Masaryk University | School of Economics and Administration*
Department of Economics
A: Lipová 41a | 602 00 Brno
E: michal.kvasnicka using econ.muni.cz | W: www.econ.muni.cz/~qasar


pá 17. 7. 2020 v 7:34 odesílatel Rolf Turner <r.turner using auckland.ac.nz>
napsal:

>
> On 14/07/20 1:39 am, Michal Kvasnička wrote:
>
> > Hello.
> >
> > I have to find hotspots of road accidents. It seems that
> > spatstat::densityHeat() implements McSwiggan et al. (2017) algorithm I
> want
> > to use. I plan to follow path of DrHotNet package:
> >
> > 1. estimate the density function
> > 2. lixelize the network
> > 3. calculate the density in the middle point of each lixel from (2) using
> > function estimated in (1)
> > 4. find clusters of lixels from (3) with sufficiently high density
> >
> > However, I don't know how to set the function's parameters.
> >
> > I cannot use the implicit setting (finespacing=TRUE) because it suggests
> > tens of billions iterations. (I use OSM road maps of Czech districts
> > converted to Krovak projection. My testing district has about 2,700 km of
> > roads with an average length of road segments of about 30 meters due to
> > road curvatures. This is not the largest district I have to
> handle---Prague
> > is much larger.)
> >
> > I can set finespacing = FALSE and then finetune the parameters myself.
> > However, I can't find any documentation. Having skimmed the source code,
> > I'm trying something like this:
> >
> > density_function <- densityHeat(accident_lpp, sigma, finespacing = FALSE,
> > eps = 10, fun = TRUE)
> >
> > This way I get the function I need. However, I'm not certain what I'm
> > doing. It seems that the densityHeat() uses some grid, based on lixelized
> > network. My questions are:
> >
> > * Is it sufficient to set the eps parameter or do I need to set other
> > parameters too? Which ones?
> > * Is the parameter in meters (my map is projected with meters as its
> units)?
> > * If eps = 10 means grid of 10x10 meters, what precisely does it mean?
> How
> > is the network lixelized (and why)? How does it affect the estimated
> > function (not the linim object)?
> > * What is the (economically/substantially) reasonable value of eps (and
> > other necessary parameters)?
> > * Is there any documentation to the function? (I've read chapter 17 of
> the
> > spatstat book but found nothing there. The function help page doesn't
> cover
> > this either.)
> > * Does it any difference if I lixelize the network myself before running
> > densityHeat()?
> >
> > Many thanks for your advice. (And an apology for such a long question.)
> >
> > Best wishes,
> > *Ing. Michal Kvasnička, Ph.D.*
>
> Dear Michal,
>
> I am replying on behalf of the spatstat authors (and not claiming any
> personal competence in the area of point processes on networks).
>
> My co-authors suggest that you:
>
>     * upgrade to the latest development version of spatstat.  You
>       could use
>
>       > remotes::install_github("spatstat/spatstat",lib=.Rlib)
>
>       to do this,
>
>     * look at help(densityHeat), in particular the
>       section "Discretisation and Error Messages" which explains
>       how the discretisation is performed,
>
>     * use densityfun.lpp instead of density.lpp if you want a
>       function rather than a pixel image.
>
> Note that if finespacing=FALSE is selected, the discretisation is
> determined by the default pixel resolution of the window containing the
> network. The arguments eps and dimyx would be passed to as.mask to
> determine pixel resolution in the usual way (their interpretation is
> explained in the help for as.mask()).
>
> Note also that density.lpp() calls densityHeat() which calls the
> internal algorithm. Similarly densityfun.lpp() calls the same internal
> algorithm. Consequently they all handle discretisation arguments the
> same way.
>
> The spatstat authors know nothing about the package DrHotNet and do not
> necessarily endorse whatever they are doing. Definitely *do not*
> 'lixellate' (discretise) the network before applying density(), because
> this will screw up the discretisation procedure.
>
> We are very sceptical about the validity of applying clustering to the
> elements of the discretisation.
>
> HTH
>
> cheers,
>
> Rolf Turner
>
> --
> Honorary Research Fellow
> Department of Statistics
> University of Auckland
> Phone: +64-9-373-7599 ext. 88276
>

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