@shasheminassab preguntado en Agrega datos de GPS por código postal cómo convertir coordenadas GPS a códigos postales.@gzmorgan0 señaló que este es un trabajo imposible de hacer completamente correctamente, por muchas razones. Una razón importante es que los códigos postales son puntos fijos (que pueden cambiar mañana) en la ruta de un cartero; pueden superponerse y pueden no cubrir el área y nunca tuvieron la intención de representar un área. La gente del Censo produce algo lo suficientemente parecido para algunos propósitos, llamados archivos ZCTA (área de tabulación de código postal). Aquí hay algo de JSL para descargar el ZCTA de 2019 y usarlo para geocodificación directa e inversa. Hay dos pequeños ejemplos al final. Pruébelo antes de decidir que hace lo que necesita.
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));El LatLonToZip convertirá alrededor de 1000 puntos por segundo; utiliza una KDTable para encontrar un conjunto de 10 polígonos cercanos que se verificarán para ver si el punto está realmente dentro. Hay algunos casos degenerados, polis con un solo borde, que no funcionan de ida y vuelta muy bien.
La función está configurada para almacenar en caché algunas tablas de datos en su escritorio; la descarga de 100 MB es lenta. Es posible que desee almacenarlos en caché en un lugar mejor.
un artículo antiguo con detalles importantes: http://web.archive.org/web/20050209030255/http://www.manifold.net/cases/zip_codes/zip_codes.html
respuestas mas recientes: https://gis.stackexchange.com/questions/2682/sources-for-us-zip-code-boundaries
Punto de partida del censo: https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html
