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From |
Roy Wada <roywada@hotmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: exact command for distance ? |

Date |
Sat, 12 Sep 2009 17:22:37 -0700 |

We can keep the discussion here if others are interested. And you are welcome. Yes, that is what I mean by grid-search. There is no point in comparing something that is not in the neighborhood. The quickiest way I have found is the cut the map into overlapping grids. This is an old code that was in process of revision (about a year old), but it will do 2 million observations in about 5 minutes assuming random dispersion of points. In Laura's case, she just need to reverse the comparison group. Roy clear set mem 1g set obs 2000000 gen id=_n set seed 123 gen longit=uniform()*10000 gen latit=uniform()*10000 sum cap program drop distance program define distance syntax [using], kernel(real) RADius(real) gen X=round(longit,`kernel') gen Y=round(latit,`kernel') tempfile file0 file1 gen markX=. save `file0' replace markX=0 if markX==. append using `file0' replace markX=1 if markX==. append using `file0' replace markX=2 if markX==. gen markY=. save `file1' replace markY=3 if markY==. append using `file1' replace markY=4 if markY==. append using `file1' replace markY=5 if markY==. *sort id markX markY replace X=X-`kernel' if markX==1 replace X=X+`kernel' if markX==2 replace Y=Y-`kernel' if markY==4 replace Y=Y+`kernel' if markY==5 gen mark=0 if markX==0 & markY==3 egen XY=group(X Y) bys XY: gen count=_N keep if count>1 * drop if the middle part (mark==0) is not there bys XY: egen min=min(mark) drop if min~=0 drop min * renumber drop XY egen XY=group(X Y) *** re-sorting is necessary sort XY drop count bys XY: gen count=_N *gen row=_n *bys XY: gen begin=row if _n==1 *bys XY: replace begin=begin[_n-1] if begin[_n-1]~=. *bys XY: gen end0=row if _n==_N *bys XY: egen end=max(end0) *drop end0 forval num=1/10 { gen match`num'=. gen dist`num'=. } egen max=max(XY) local max=max drop max local place=1 while `place'=0 & `=mark[`first']'==0 { if `dist'<=`radius' & `dist'>0 & `=mark[`first']'==0 { di " `max' `=id[`first']' `=id[`second']' `dist'" local num=`num'+1 qui replace match`num'=`=id[`second']' in `first' qui replace dist`num'=`dist' in `first' } } } local place=`place'+`=count[`place']' } end distance, kernel(.7) rad(.1) > > To quote GH Hardy: "I am reluctant to intrude in a discussion concerning > matters of which I have no expert knowledge,..." > > But, it seems to me that for each farm, you could create a quick screen > that would remove the polygon points that were unlikely to be close to > the farm. > > If f_lat and f_long are the coordinates of the farm and the coordinates > for each polygon point are p_lat and p_long, then > > gen fp_lat = f_lat - p_lat > gen fp_long = f_long - p_long > gen dist = sqrt(fp_lat^2 + fp_long^2) > qui sum dist, detail > keep if dist > If all your farms and lakes are in the UK, you would need to add a > constant to the longitudes before calculating the differences (modulo > 180 or 360; I don't know how longitude is specified in the data). > _________________________________________________________________ Hotmail: Powerful Free email with security by Microsoft. http://clk.atdmt.com/GBL/go/171222986/direct/01/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: RE: exact command for distance ?***From:*Laura Platchkov <LMP881@bham.ac.uk>

**RE: st: RE: exact command for distance ?***From:*"Kieran McCaul" <Kieran.McCaul@uwa.edu.au>

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