@shasheminassab chiesto in Dati GPS aggregati per codice postale come convertire le coordinate GPS in codici postali. @ gzmorgan0 ha sottolineato che questo è un lavoro impossibile da svolgere in modo completamente corretto, per molte ragioni. Un motivo importante è che i codici postali sono punti fissi (che potrebbero cambiare domani) sulla rotta di un corriere; possono sovrapporsi e potrebbero non coprire l'area e non sono mai stati pensati per rappresentare un'area. La gente del censimento produce qualcosa di abbastanza vicino per alcuni scopi, chiamato file ZCTA (area di tabulazione del codice postale). Ecco un po 'di JSL per scaricare ZCTA 2019 e usarlo per geocodifica in avanti e indietro. Ci sono due piccoli esempi alla fine. Provalo prima di decidere che fa quello che ti serve.
If( JMP Version() < "15",
Throw( "JMP 15 required" )
);
zipcodeNS = New Namespace(
"zipcodeNS"
);
zipcodeNS:zipCodeSetup = Function( {},
{i, za, blobshp, tempfile, blobdbf, zipkeys, latlonvals},
If(
!File Exists( "$desktop/cb_2019_us_zcta510_500kdbf.jmp" ) //
| //
!File Exists( "$desktop/cb_2019_us_zcta510_500kshp.jmp" ) //
, //
// from https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html
za = Open( "https://www2.census.gov/geo/tiger/GENZ2019/shp/cb_2019_us_zcta510_500k.zip", "zip" );
blobshp = za << read( "cb_2019_us_zcta510_500k.shp", Format( blob ) );
tempfile = Save Text File( "$temp/cb_2019_us_zcta510_500k.shp", blobshp );
zipcodeNS:dtshp = Open( tempfile, Add to Recent Files( 0 ), invisible );
zipcodeNS:dtshp << save( "$desktop/cb_2019_us_zcta510_500kshp.jmp" );
blobdbf = za << read( "cb_2019_us_zcta510_500k.dbf", Format( blob ) );
tempfile = Save Text File( "$temp/cb_2019_us_zcta510_500k.dbf", blobdbf );
zipcodeNS:dtdbf = Open( tempfile, Add to Recent Files( 0 ), invisible );
zipcodeNS:dtdbf << save( "$desktop/cb_2019_us_zcta510_500kdbf.jmp" );
, //
zipcodeNS:dtshp = Open( "$desktop/cb_2019_us_zcta510_500kshp.jmp", Add to Recent Files( 0 ), invisible );
zipcodeNS:dtdbf = Open( "$desktop/cb_2019_us_zcta510_500kdbf.jmp", Add to Recent Files( 0 ), invisible );
);
zipcodeNS:dtdbf:shape << Set Property( "Link ID", 1 );
zipcodeNS:dtshp:shape << Set Property( "Link Reference", Reference Table( zipcodeNS:dtdbf ) );
zipcodeNS:dtshp:name( "ZCTA5CE10[Shape]" ) << hide( 0 );
zipcodeNS:dtsummary = zipcodeNS:dtshp << Summary(
invisible,
Group( :Shape ),
Mean( :X ),
Mean( :Y ),
Freq( "None" ),
Weight( "None" ),
statistics column name format( "stat of column" ),
Link to original data table( 0 )
);
zipcodeNS:dtsummary:shape << Set Property( "Link Reference", Reference Table( zipcodeNS:dtdbf ) );
// validate shape is monotonic ascending index
For( i = 1, i <= N Rows( zipcodeNS:dtsummary ), i += 1,
If( zipcodeNS:dtsummary:shape[i] != i,
Throw( "bad shape indexing" )
)
);
// add a column that can be used with nrows to find the rows of the shape in dtshp
zipcodeNS:dtsummary << New Column( "LastRow", Numeric, "Continuous", Format( "Best", 12 ), Formula( Col Cumulative Sum( :N Rows ) ) );
zipcodeNS:dtsummary << runformulas;
// build the lookup table for initial guessing
zipcodeNS:kdt = KDTable( zipcodeNS:dtsummary[0, {"mean of Y", "mean of X"}] );
// the forward lookup table produces a coordinate from the polygon's
// mean (median is worse) coordinates that is inside the polygon 90%
// of the time.
zipkeys = zipcodeNS:dtsummary:name( "ZCTA5CE10[Shape]" ) << getvalues;
latlonvals = As List( (zipcodeNS:dtsummary:mean of y << getvalues) || (zipcodeNS:dtsummary:mean of x << getvalues) );
zipcodeNS:aa = Associative Array( zipkeys, latlonvals );
zipcodeNS:aa << Set Default Value( . );
zipcodeNS:dtsummary << setdirty( 0 );
zipcodeNS:dtshp << setdirty( 0 );
zipcodeNS:dtdbf << setdirty( 0 );
);
zipcodeNS:zipCodeSetup();
zipcodeNS:LatLonToZip = Function( {lat, lon},
{shapeid, distance, iCandidate, testid, endrange, beginrange},
If( !Is Missing( lat ) & !Is Missing( lon ),
{shapeid, distance} = zipcodeNS:kdt << KNearestRows( 10, lat || lon );
For( iCandidate = 1, iCandidate < N Cols( shapeid ), iCandidate += 1,
testid = shapeid[iCandidate];
endrange = zipcodeNS:dtsummary:lastrow[testid];
beginrange = endrange - zipcodeNS:dtsummary:nrows[testid] + 1;
If( In Polygon( lat, lon, zipcodeNS:dtshp[beginrange :: endrange, "x"], zipcodeNS:dtshp[beginrange :: endrange, "y"] ),
Return( zipcodeNS:dtshp[beginrange, "ZCTA5CE10[Shape]"] )
);
);
// inside the bounding box of the 1st one? consider that good enough.
// handles points in the water in the middle of a horse shoe shape.
// there are also degenerate 2 point polys, so if withing .001 degree too..
testid = shapeid[1];
endrange = zipcodeNS:dtsummary:lastrow[testid];
beginrange = endrange - zipcodeNS:dtsummary:nrows[testid] + 1;
If(
distance[1] < .001 | (Min( zipcodeNS:dtshp[beginrange :: endrange, "x"] ) <= lon <= Max( zipcodeNS:dtshp[beginrange :: endrange, "x"] )
& Min( zipcodeNS:dtshp[beginrange :: endrange, "y"] ) <= lat <= Max( zipcodeNS:dtshp[beginrange :: endrange, "y"] )),
Return( zipcodeNS:dtshp[beginrange, "ZCTA5CE10[Shape]"] )
);
);
Return( . );// not found
);
zipcodeNS:ZipToLatLon = Function( {zip},
xy = zipcodeNS:aa[Char( zip )];
If( !Is List( xy ),
{., .},
xy
);
);
/*** some random tests.
show(zipcodeNS:LatLonToZip( 35.79, -78.65), zipcodeNS:ZipToLatLon("27605"));
show(zipcodeNS:LatLonToZip(31.832,-106.391)=="79908");
// test the round trip
nbad=0;
nall=0;
for(i=0,i<=99999,i+=1,
{lat,lon}= zipcodeNS:ZipToLatLon(i);
// just enough jitter here to create an occasional round-trip error...
zip=zipcodeNS:LatLonToZip(lat+randomnormal(0,.0001),lon+randomnormal(0,.0001));
if(!ismissing(lat),
nall+=1;
if(zip!=char(i),show(i,zip);nbad+=1;)
)
);
show(nall,nbad);// median bad=3936/30575. mean bad=2438/30575
//https://gist.github.com/erichurst/7882666
test=[
29506 34.221377 -79.650336,
29510 33.454312 -79.613121,
29511 33.975732 -79.122716,
29512 34.619033 -79.718041,
29516 34.437454 -79.634640,
29518 33.788608 -79.847975,
29519 34.018637 -79.359821,
29520 34.679486 -79.927292,
29525 34.552415 -79.540885,
29526 33.847406 -78.947093,
29527 33.785083 -79.140176,
29530 33.996055 -79.740075,
29532 34.280331 -79.869336,
29536 34.419502 -79.373048,
29540 34.377298 -79.846185,
29541 34.064949 -79.740750,
29543 34.287605 -79.265927,
29544 33.926098 -79.255583,
29545 34.163660 -78.966477,
29546 33.883845 -79.349409,
29547 34.496287 -79.334829,
29550 34.399975 -80.082806,
29554 33.704050 -79.381598,
29555 33.845583 -79.483856,
29556 33.668227 -79.763139,
29560 33.827895 -79.742142,
29563 34.353366 -79.208673,
29564 33.449386 -79.848408,
29565 34.358052 -79.499289,
29566 33.873402 -78.666957,
29567 34.560972 -79.432683,
29568 33.913616 -78.750292,
29569 34.032632 -78.911583,
29570 34.667866 -79.562707,
29571 34.135123 -79.425041,
29572 33.772568 -78.785662,
29574 34.165413 -79.260831,
29575 33.629132 -78.970412];
for(i=1,i<=nrows(test),i+=1,
z=zipcodeNS:LatLonToZip(test[i,2],test[i,3]);
if(num(z)==test[i,1],0,show(i,z));
ll=zipcodeNS:ZipToLatLon(char(test[i,1]));
if(abs(ll[1]-test[i,2])>.02 || abs(ll[2]-test[i,3])>.02,show(ll[2]-test[i,2],ll[1]-test[i,3]));
);
***/
show(zipcodeNS:LatLonToZip( 38.897,-77.043));
show(zipcodeNS:ZipToLatLon(20006));LatLonToZip converte circa 1000 punti al secondo; utilizza una tabella KD per trovare un set di 10 poligoni vicini che verranno controllati per vedere se il punto è effettivamente all'interno. Ci sono alcuni casi degeneri, polis con un solo bordo, che non vanno molto bene in andata e ritorno.
La funzione è impostata per memorizzare nella cache alcune tabelle di dati sul desktop; il download da 100 MB è lento. Potresti voler memorizzarli nella cache in un posto migliore.
un vecchio articolo con dettagli importanti: http://web.archive.org/web/20050209030255/http://www.manifold.net/cases/zip_codes/zip_codes.html
risposte più recenti: https://gis.stackexchange.com/questions/2682/sources-for-us-zip-code-boundaries
Punto di partenza del censimento: https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html
