下面是我用PHP实现的完整解决方案。

该解决方案使用http://www.scribd.com/doc/2569355/Geo-Distance-Search-with-MySQL中给出的Haversine公式。

值得注意的是，哈弗辛公式在极点处有弱点。这个答案展示了如何实现vincenty大圆距离公式来解决这个问题，但是我选择只使用Haversine，因为它足够适合我的目的。

我将纬度存储为DECIMAL(10,8)，经度存储为DECIMAL(11,8)。希望这能有所帮助!

showClosest.php

<?PHP
/**
 * Use the Haversine Formula to display the 100 closest matches to $origLat, $origLon
 * Only search the MySQL table $tableName for matches within a 10 mile ($dist) radius.
 */
include("./assets/db/db.php"); // Include database connection function
$db = new database(); // Initiate a new MySQL connection
$tableName = "db.table";
$origLat = 42.1365;
$origLon = -71.7559;
$dist = 10; // This is the maximum distance (in miles) away from $origLat, $origLon in which to search
$query = "SELECT name, latitude, longitude, 3956 * 2 * 
          ASIN(SQRT( POWER(SIN(($origLat - latitude)*pi()/180/2),2)
          +COS($origLat*pi()/180 )*COS(latitude*pi()/180)
          *POWER(SIN(($origLon-longitude)*pi()/180/2),2))) 
          as distance FROM $tableName WHERE 
          longitude between ($origLon-$dist/cos(radians($origLat))*69) 
          and ($origLon+$dist/cos(radians($origLat))*69) 
          and latitude between ($origLat-($dist/69)) 
          and ($origLat+($dist/69)) 
          having distance < $dist ORDER BY distance limit 100"; 
$result = mysql_query($query) or die(mysql_error());
while($row = mysql_fetch_assoc($result)) {
    echo $row['name']." > ".$row['distance']."<BR>";
}
mysql_close($db);
?>

/资产/ db / db。php

<?PHP
/**
 * Class to initiate a new MySQL connection based on $dbInfo settings found in dbSettings.php
 *
 * @example $db = new database(); // Initiate a new database connection
 * @example mysql_close($db); // close the connection
 */
class database{
    protected $databaseLink;
    function __construct(){
        include "dbSettings.php";
        $this->database = $dbInfo['host'];
        $this->mysql_user = $dbInfo['user'];
        $this->mysql_pass = $dbInfo['pass'];
        $this->openConnection();
        return $this->get_link();
    }
    function openConnection(){
    $this->databaseLink = mysql_connect($this->database, $this->mysql_user, $this->mysql_pass);
    }

    function get_link(){
    return $this->databaseLink;
    }
}
?>

资产/ db - dbSettings。php

<?php
$dbInfo = array(
    'host'      => "localhost",
    'user'      => "root",
    'pass'      => "password"
);
?>

根据上面“使用MySQL进行地理距离搜索”文章的建议，可以通过使用MySQL存储过程来提高性能。

我有一个约17,000个位置的数据库，查询执行时间为0.054秒。

simpledb.execSQL("CREATE TABLE IF NOT EXISTS " + tablename + "(id INTEGER PRIMARY KEY   AUTOINCREMENT,lat double,lng double,address varchar)");
            simpledb.execSQL("insert into '" + tablename + "'(lat,lng,address)values('22.2891001','70.780154','craftbox');");
            simpledb.execSQL("insert into '" + tablename + "'(lat,lng,address)values('22.2901396','70.7782428','kotecha');");//22.2904718 //70.7783906
            simpledb.execSQL("insert into '" + tablename + "'(lat,lng,address)values('22.2863155','70.772108','kkv Hall');");
            simpledb.execSQL("insert into '" + tablename + "'(lat,lng,address)values('22.275993','70.778076','nana mava');");
            simpledb.execSQL("insert into '" + tablename + "'(lat,lng,address)values('22.2667148','70.7609386','Govani boys hostal');");


    double curentlat=22.2667258;  //22.2677258
    double curentlong=70.76096826;//70.76096826

    double curentlat1=curentlat+0.0010000;
    double curentlat2=curentlat-0.0010000;

    double curentlong1=curentlong+0.0010000;
    double curentlong2=curentlong-0.0010000;

    try{

        Cursor c=simpledb.rawQuery("select * from '"+tablename+"' where (lat BETWEEN '"+curentlat2+"' and '"+curentlat1+"') or (lng BETWEEN         '"+curentlong2+"' and '"+curentlong1+"')",null);

        Log.d("SQL ", c.toString());
        if(c.getCount()>0)
        {
            while (c.moveToNext())
            {
                double d=c.getDouble(1);
                double d1=c.getDouble(2);

            }
        }
    }
    catch (Exception e)
    {
        e.printStackTrace();
    }

在极端情况下，这种方法会失败，但为了性能考虑，我跳过了三角函数，只是简单地计算对角线的平方。

你要找的是哈弗辛公式。看这里。

还有其他的，但这是最常被引用的。

如果您正在寻找更健壮的东西，则可能需要考虑数据库的GIS功能。它们能够做一些很酷的事情，比如告诉你一个点(城市)是否出现在给定的多边形(区域、国家、大陆)中。

 +----+-----------------------+---------+--------------+---------------+
| id | email                 | name    | location_lat | location_long |
+----+-----------------------+---------+--------------+---------------+
| 7  | test@gmail.com        | rembo   | 23.0249256   |  72.5269697   |
| 25 | test1@gmail.com.      | Rajnis  | 23.0233221    | 72.5342112   |
+----+-----------------------+---------+--------------+---------------+

$lat = 23.02350629;

$long = 72.53230239;

DB:: 选择 (“ 选择 * 从 （ 选择 ， （ (acos(sin)。决。”* pi(美元)/ 180)* sin (lat * pi(+) - 180)因为(”。拉丁语“圆周率(美元)/ 180)* cos (lat * pi () / 180) * cos(("。龙。”- long) * pi(美元)/ 180))* 180 / pi() * 60 * 1515 1。1 . 609344 ) 距离美国 从 \用户 ) \用户 在哪里 距离<= 2");

谷歌的解决办法:

创建表

When you create the MySQL table, you want to pay particular attention to the lat and lng attributes. With the current zoom capabilities of Google Maps, you should only need 6 digits of precision after the decimal. To keep the storage space required for your table at a minimum, you can specify that the lat and lng attributes are floats of size (10,6). That will let the fields store 6 digits after the decimal, plus up to 4 digits before the decimal, e.g. -123.456789 degrees. Your table should also have an id attribute to serve as the primary key.

CREATE TABLE `markers` (
  `id` INT NOT NULL AUTO_INCREMENT PRIMARY KEY ,
  `name` VARCHAR( 60 ) NOT NULL ,
  `address` VARCHAR( 80 ) NOT NULL ,
  `lat` FLOAT( 10, 6 ) NOT NULL ,
  `lng` FLOAT( 10, 6 ) NOT NULL
) ENGINE = MYISAM ;

填充表

创建表之后，是时候用数据填充它了。下面提供的样本数据是分布在美国各地的大约180家披萨店。在phpMyAdmin中，您可以使用IMPORT选项卡导入各种文件格式，包括CSV(逗号分隔值)。Microsoft Excel和谷歌电子表格都导出为CSV格式，因此您可以通过导出/导入CSV文件轻松地将数据从电子表格传输到MySQL表。

INSERT INTO `markers` (`name`, `address`, `lat`, `lng`) VALUES ('Frankie Johnnie & Luigo Too','939 W El Camino Real, Mountain View, CA','37.386339','-122.085823');
INSERT INTO `markers` (`name`, `address`, `lat`, `lng`) VALUES ('Amici\'s East Coast Pizzeria','790 Castro St, Mountain View, CA','37.38714','-122.083235');
INSERT INTO `markers` (`name`, `address`, `lat`, `lng`) VALUES ('Kapp\'s Pizza Bar & Grill','191 Castro St, Mountain View, CA','37.393885','-122.078916');
INSERT INTO `markers` (`name`, `address`, `lat`, `lng`) VALUES ('Round Table Pizza: Mountain View','570 N Shoreline Blvd, Mountain View, CA','37.402653','-122.079354');
INSERT INTO `markers` (`name`, `address`, `lat`, `lng`) VALUES ('Tony & Alba\'s Pizza & Pasta','619 Escuela Ave, Mountain View, CA','37.394011','-122.095528');
INSERT INTO `markers` (`name`, `address`, `lat`, `lng`) VALUES ('Oregano\'s Wood-Fired Pizza','4546 El Camino Real, Los Altos, CA','37.401724','-122.114646');

使用MySQL查找位置

要在标记表中查找给定纬度/经度的某个半径距离内的位置，可以使用基于Haversine公式的SELECT语句。哈弗辛公式一般用于计算球面上两对坐标之间的大圆距离。维基百科给出了一个深入的数学解释，Movable Type的网站上有一个关于公式的很好的讨论，因为它与编程有关。

下面的SQL语句将查找距离坐标37 -122 25英里半径内最近的20个位置。它根据该行的纬度/经度和目标纬度/经度计算距离，然后只请求距离值小于25的行，根据距离对整个查询进行排序，并将结果限制为20个。要按公里而不是英里搜索，将3959替换为6371。

SELECT 
id, 
(
   3959 *
   acos(cos(radians(37)) * 
   cos(radians(lat)) * 
   cos(radians(lng) - 
   radians(-122)) + 
   sin(radians(37)) * 
   sin(radians(lat )))
) AS distance 
FROM markers 
HAVING distance < 28 
ORDER BY distance LIMIT 0, 20;

这个方法是在小于28英里的距离内找到经纬度。

另一种方法是在28到29英里的距离上找到它们:

SELECT 
id, 
(
   3959 *
   acos(cos(radians(37)) * 
   cos(radians(lat)) * 
   cos(radians(lng) - 
   radians(-122)) + 
   sin(radians(37)) * 
   sin(radians(lat )))
) AS distance 
FROM markers 
HAVING distance < 29 and distance > 28 
ORDER BY distance LIMIT 0, 20;

https://developers.google.com/maps/articles/phpsqlsearch_v3#creating-the-map