计算边界框以选择SQL查询的WHERE子句中的行的子集，以便仅对该行子集执行昂贵的距离计算，而不是对表中的全部200k记录执行。本文在Movable Type（带有PHP代码示例）中描述了该方法。然后，您可以针对该子集在查询中包含haversine计算，以计算实际距离，并在该点处将其包含在HAVING子句中。
这是帮助您提高性能的边界框，因为这意味着您仅对一小部分数据进行了昂贵的距离计算。实际上，这是与Patrick所建议的方法相同的方法，但是Movable Type链接对方法进行了详尽的解释，以及可用于构建边界框和SQL查询的PHP代码。
如果您认为haversine不够准确，那么还有Vincenty公式。
// Vincenty formula to calculate great circle distance between 2 locations expressed as Lat/Long in KM
function VincentyDistance($lat1,$lat2,$lon1,$lon2){
$a = 6378137 - 21 * sin($lat1);
$b = 6356752.3142;
$f = 1/298.257223563;
$p1_lat = $lat1/57.29577951;
$p2_lat = $lat2/57.29577951;
$p1_lon = $lon1/57.29577951;
$p2_lon = $lon2/57.29577951;
$L = $p2_lon - $p1_lon;
$U1 = atan((1-$f) * tan($p1_lat));
$U2 = atan((1-$f) * tan($p2_lat));
$sinU1 = sin($U1);
$cosU1 = cos($U1);
$sinU2 = sin($U2);
$cosU2 = cos($U2);
$lambda = $L;
$lambdaP = 2*M_PI;
$iterLimit = 20;
while(abs($lambda-$lambdaP) > 1e-12 && $iterLimit>0) {
$sinLambda = sin($lambda);
$cosLambda = cos($lambda);
$sinSigma = sqrt(($cosU2*$sinLambda) * ($cosU2*$sinLambda) + ($cosU1*$sinU2-$sinU1*$cosU2*$cosLambda) * ($cosU1*$sinU2-$sinU1*$cosU2*$cosLambda));
//if ($sinSigma==0){return 0;} // co-incident points
$cosSigma = $sinU1*$sinU2 + $cosU1*$cosU2*$cosLambda;
$sigma = atan2($sinSigma, $cosSigma);
$alpha = asin($cosU1 * $cosU2 * $sinLambda / $sinSigma);
$cosSqAlpha = cos($alpha) * cos($alpha);
$cos2SigmaM = $cosSigma - 2*$sinU1*$sinU2/$cosSqAlpha;
$C = $f/16*$cosSqAlpha*(4+$f*(4-3*$cosSqAlpha));
$lambdaP = $lambda;
$lambda = $L + (1-$C) * $f * sin($alpha) * ($sigma + $C*$sinSigma*($cos2SigmaM+$C*$cosSigma*(-1+2*$cos2SigmaM*$cos2SigmaM)));
}
$uSq = $cosSqAlpha*($a*$a-$b*$b)/($b*$b);
$A = 1 + $uSq/16384*(4096+$uSq*(-768+$uSq*(320-175*$uSq)));
$B = $uSq/1024 * (256+$uSq*(-128+$uSq*(74-47*$uSq)));
$deltaSigma = $B*$sinSigma*($cos2SigmaM+$B/4*($cosSigma*(-1+2*$cos2SigmaM*$cos2SigmaM)- $B/6*$cos2SigmaM*(-3+4*$sinSigma*$sinSigma)*(-3+4*$cos2SigmaM*$cos2SigmaM)));
$s = $b*$A*($sigma-$deltaSigma);
return $s/1000;
}
echo VincentyDistance($lat1,$lat2,$lon1,$lon2);
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