Hi,


Maybe you're right: perimeter/ratio could be sufficient...

Fractal dimension and lacunarity are good indicators for urban areas. Do you
know any R package, tool to quantify these?

I have found an ImageJ plugin called Fraclac.

There is another one called r.lacunarity included in spatialtools
http://www.clusterville.org/spatialtools/index.html
r.lacunarity is interesting compared to ImageJ::Fraclac because it uses a
moving window and generates a raster.

I launched a post about lacunarity and fractal dimension on R but didn't
have any answer.


So, if anyone manages to use r.lacunarity or knows other tools than Fraclac,
I'd be happy!


For those interested , here is some literature on fractal dimension applied
to analysis or aerial images or cities:
http://www.public.asu.edu/~smyint/publications/CEUS-Lacunarity-Myint-Lam.pdf
http://cybergeo.revues.org/8902
http://www.isprs.org/proceedings/XXXVII/congress/3b_pdf/80.pdf
http://www.fatih.edu.tr/~mcadams/geo352/fractal.pdf


2011/9/30 Sarah Goslee <sarah.goslee@gmail.com>

> Hi,
>
> On Fri, Sep 30, 2011 at 5:21 AM, Mathieu Rajerison
> <mathieu.rajerison@gmail.com> wrote:
> > Hello list,
> >
> >
> > I have determined major urban areas.
> >
> > This is just a post to get ideas from R-users on how to qualify urban
> > shapes.
> >
> > The data can either be binary raster (urban/ not urban), either vector.
> >
> > 1) Some urban areas follow linear infrastructures, thus are linear
> > 2) Some other diverge equally from a central heart, and are circular.
> > 3) Some are a mix and are like stars.
>
> That sounds like the kind of task that patch metrics such as perimeter/area
> ratio and fractal dimension were created for. Take a look at the copious
> Fragstats literature. I don't know if any have been implemented in R, but
> wouldn't be surprised.
>
> Sarah
>
> > The idea would be to get an index that give for each area, the
> probability
> > of belonging to each of these three classes. Like a 3-column data frame
> >
> > I wondered if packages already existed, or statistical methods for this
> > purpose. Notably, I think that topographic derivatives derived from
> > smoothed/unsmoothed binary data like aspect, could be used to qualify
> these
> > shapes (?)
> >
> > Thanks for any idea or exchange on the subject!
> >
>
>
> --
> Sarah Goslee
> http://www.functionaldiversity.org
>

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