[R-sig-Geo] interpolation with missing values

[Clint Bowman]

Interesting paper in the June 2013 Atmospheric Environment, "Time-space 
Kriging to address the spatiotemporal misalignment in the large datasets" 
<http://waesearch.kobv.de/uid.do?query=rss_feeds_2403984&ref=feed>

Clint Bowman			INTERNET:	clint at ecy.wa.gov
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On Wed, 17 Apr 2013, Edzer Pebesma wrote:

>
>
> On 04/17/2013 07:09 PM, Barry Rowlingson wrote:
>> On Wed, Apr 17, 2013 at 4:35 PM, mauvela <mauricioandresvela at gmail.com> wrote:
>>> I need to interpolate some data  about PM10 for some location (schools). I
>>> have daily data and about 50 stations. I have to interpolate for every day
>>> but my problems comes with the missing values of many stations in many days.
>>> For example for one day I could have data for 10 stations while for other
>>> day data from 50. When ignoring these missing data and interpolating using
>>> ordinary kriging for each day, the results for each school varies a lot
>>> depending of which stations have available data. For example a school near
>>> one station changes a lot when that station have missing in one day. What
>>> should be the best way to deal with this missing values, is there a method
>>> for imputation that takes into account the temporal and the spatial
>>> variability of the data?
>>
>>  Off the top of my head, do multiple imputations of the missing values
>> based on the mean and sd of the values at that site when not missing.
>> You'll then end up with a number (100, say) of kriged maps. You can
>> probably then take the mean over those as your map and for the
>> variance you'll have to combine the kriging variance with the
>> imputation variance...
>>
>>  This is probably valid assuming the dropouts are random... Also, it
>> doesn't take into account any temporal correlation which might get you
>> a better estimate of your imputed values...
>>
>>  What you do may also depend on what you are doing with the data. If
>> its just to produce pretty maps, then you might not need something so
>> sophisticated. If you are computing the number of days that PM10 in
>> some location exceeds some threshold, then you may have to give it
>> some more thought...
>>
>
> Maybe try spatio-temporal interpolation?
>
> -- 
> Edzer Pebesma
> Institute for Geoinformatics (ifgi), University of Münster
> Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
> 8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
> http://www.52north.org/geostatistics      e.pebesma at wwu.de
>
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