[R-sig-Geo] Ideas on qualifying urban shapes: linear / circular / star
sarah.goslee at gmail.com
Fri Sep 30 18:28:11 CEST 2011
On Fri, Sep 30, 2011 at 9:24 AM, Mathieu Rajerison
<mathieu.rajerison at gmail.com> wrote:
> 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've always just used Fragstats, as I already suggested, but you might look into
> I have found an ImageJ plugin called Fraclac.
> There is another one called r.lacunarity included in spatialtools
> 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:
> 2011/9/30 Sarah Goslee <sarah.goslee at gmail.com>
>> On Fri, Sep 30, 2011 at 5:21 AM, Mathieu Rajerison
>> <mathieu.rajerison at 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
>> 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.
>> > 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!
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