Re: points to grid

Mike

I am not sure what is meant be "vegetation".
My limited experience is that this type of data
is more categorical. Something like:

" 1 Evergreen Needleleaf Forest\n",
" 2 Evergreen Broadleaf Forest \n",
" 3 Deciduous Needleleaf Forest\n",
" 4 Deciduous Broadleaf Forest \n",
" 5 Mixed Forest \n",
" 6 Closed Shrublands \n",
" 7 Open Shrublands \n",
" 8 Woody Savannas \n",
" 9 Savannas \n",
"10 Grasslands \n",
"11 Permanent Wetlands \n",
"12 Croplands \n",
"13 Urban and Built-up \n",
"14 Cropland Mosaics \n",
"15 Snow and Ice (permanent) \n",
"16 Bare Soil and Rocks \n",
"17 Water Bodies \n",
"18 Tundra " ;

These data are nor well suited to any general interpolation method.
These generally assume some level of smoothness/continuous data.
If you were to average
[ 10 Grasslands and 12 Croplands] =????=> 11 Permanent Wetlands

====
You could use (1) triple2grid; (2) use poisson_grid_fill, (3) mask out
ocean areas

poissong grid_fill will give you numbers NUT are the
the correct numbers?

D

Michael Notaro wrote:
> Thanks for the suggestion.
> triple2grid does no interpolation, so it produces data on a regular
> 0.5x0.5 grid with periodic missing values.
> Kind of like (D=data value, M=missing):
>
> DMMDMMDMM
> MMMMMMMMM
> DMMDMMDMM
>
> Mike
>
> On Dec 3, 2008, at 1:28 PM, Correia, James wrote:
>
>> You can try triple2grid I think.
>> jimmyc
>>
>>
>> On 12/3/08 11:18 AM, "Michael Notaro" <mnotaro_at_wisc.edu> wrote:
>>
>>> I have 11,000 random data points of vegetation with a latitude and
>>> longitude for each point.
>>> I want to create a global 2D lat/lon grid (0.5x0.5 degree) with this
>>> data, using interpolation. Data over the ocean
>>> should either be all 0's or missing, either way. Which approach /
>>> function is best for this?
>>>
>>> veg(11000)
>>> lat(11000)
>>> lon(11000)
>>>
>>> TO
>>>
>>> data(360,720)
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Dennis J. Shea                  tel: 303-497-1361    |
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