Karl,
Thank you for that - some interesting ideas there.  While not a solution for
you - I'm working on a similar problem and I've discovered mapserver(.org)
for the display side of things.  It will handle rasters and shape files and
display them quickly on a google map background.  It will also manage
different projections automagically - so I'm thinking of just doing my plots
in the National Grid and I won't have to worry about re-projecting for
display...

I intend creating shape files or rasters for various "risks" and then the
end user can just display these as and when they want to.

Kind regards,
Sean O'Riordain

On 14 October 2010 13:56, Karl Ove Hufthammer <karl@huftis.org> wrote:

> Karl Ove Hufthammer wrote:
>
> > However, since the ‘heatmap’ is really a density estimate, it integrates
> > to 1. I would instead want the heatmap colours to correspond to real
> > frequencies (of course, they do, but the actual mapping is not visible on
> > the colour scale).
>
> I have been thinking a bit more on this, and what I really need is having
> the value at a point correspond to the *sum* of the weights close to the
> points (e.g., in a 1 km radius).
>
> An example:
>
> x=c(.2, .25, .8)
> y=c(.75, .7, .2)
>
> If all the points have unit weight, the output value at the two points at
> the top-left corner should be 2, and the value at the bottom right corner
> should be 1. If we have weights
>
> w=c(10, 10, 20)
>
> the output value should be 20 near both point clusters, 10 in areas only
> close to *one* of the points in the top-left clusters, and 0 everywhere
> else.
>
> A couple of real-life examples:
>
> 1. The weights being housing prices, and the output being the money earned
>   by selling all the houses in an area (a circle centred at the output
>   point).
> 2. The weights being the quantity (number or weight) of fish caught at
>   different locations, and the output being the total amount caught in
>   an area.
>
> I could of course set up a grid for this, and simply calculate the sum of
> weights in each grid cell, but I would prefer using distance from the
> centre
> of each cell to select which observations to count. And using some sort of
> kernel smoothing sounds like a good idea, at least in theory. But the (lack
> of) a normalizing constant makes the solution non-obvious (for me). And
> computational issues when there are many observations may of course also be
> a problem. I feel there must already be an R function/package written for
> just this type of calculation. :)
>
> So, any ideas?
>
> --
> Karl Ove Hufthammer
>
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