# php - MySQL Great Circle Distance (Haversine formula)

ID : 20091

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Tags : phpmysqlgreat-circlephp

### Top 5 Answer for php - MySQL Great Circle Distance (Haversine formula)

99

Here's the SQL statement that will find the closest 20 locations that are within a radius of 25 miles to the 37, -122 coordinate. It calculates the distance based on the latitude/longitude of that row and the target latitude/longitude, and then asks for only rows where the distance value is less than 25, orders the whole query by distance, and limits it to 20 results. To search by kilometers instead of miles, replace 3959 with 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 < 25  ORDER BY distance  LIMIT 0 , 20; ``

89

`\$greatCircleDistance = acos( cos(\$latitude0) * cos(\$latitude1) * cos(\$longitude0 - \$longitude1) + sin(\$latitude0) * sin(\$latitude1));`

with latitude and longitude in radian.

so

``SELECT    acos(        cos(radians( \$latitude0 ))     * cos(radians( \$latitude1 ))     * cos(radians( \$longitude0 ) - radians( \$longitude1 ))     + sin(radians( \$latitude0 ))      * sin(radians( \$latitude1 ))   ) AS greatCircleDistance   FROM yourTable; ``

to get your results in Km or miles, multiply the result with the mean radius of Earth (`3959` miles,`6371` Km or `3440` nautical miles)

The thing you are calculating in your example is a bounding box. If you put your coordinate data in a spatial enabled MySQL column, you can use MySQL's build in functionality to query the data.

``SELECT    id FROM spatialEnabledTable WHERE    MBRWithin(ogc_point, GeomFromText('Polygon((0 0,0 3,3 3,3 0,0 0))')) ``

71

If you add helper fields to the coordinates table, you can improve response time of the query.

Like this:

``CREATE TABLE `Coordinates` ( `id` INT(10) UNSIGNED NOT NULL COMMENT 'id for the object', `type` TINYINT(4) UNSIGNED NOT NULL DEFAULT '0' COMMENT 'type', `sin_lat` FLOAT NOT NULL COMMENT 'sin(lat) in radians', `cos_cos` FLOAT NOT NULL COMMENT 'cos(lat)*cos(lon) in radians', `cos_sin` FLOAT NOT NULL COMMENT 'cos(lat)*sin(lon) in radians', `lat` FLOAT NOT NULL COMMENT 'latitude in degrees', `lon` FLOAT NOT NULL COMMENT 'longitude in degrees', INDEX `lat_lon_idx` (`lat`, `lon`) )     ``

If you're using TokuDB, you'll get even better performance if you add clustering indexes on either of the predicates, for example, like this:

``alter table Coordinates add clustering index c_lat(lat); alter table Coordinates add clustering index c_lon(lon); ``

You'll need the basic lat and lon in degrees as well as sin(lat) in radians, cos(lat)*cos(lon) in radians and cos(lat)*sin(lon) in radians for each point. Then you create a mysql function, smth like this:

``CREATE FUNCTION `geodistance`(`sin_lat1` FLOAT,                               `cos_cos1` FLOAT, `cos_sin1` FLOAT,                               `sin_lat2` FLOAT,                               `cos_cos2` FLOAT, `cos_sin2` FLOAT)     RETURNS float     LANGUAGE SQL     DETERMINISTIC     CONTAINS SQL     SQL SECURITY INVOKER    BEGIN    RETURN acos(sin_lat1*sin_lat2 + cos_cos1*cos_cos2 + cos_sin1*cos_sin2);    END ``

This gives you the distance.

Don't forget to add an index on lat/lon so the bounding boxing can help the search instead of slowing it down (the index is already added in the CREATE TABLE query above).

``INDEX `lat_lon_idx` (`lat`, `lon`) ``

Given an old table with only lat/lon coordinates, you can set up a script to update it like this: (php using meekrodb)

``\$users = DB::query('SELECT id,lat,lon FROM Old_Coordinates');  foreach (\$users as \$user) {   \$lat_rad = deg2rad(\$user['lat']);   \$lon_rad = deg2rad(\$user['lon']);    DB::replace('Coordinates', array(     'object_id' => \$user['id'],     'object_type' => 0,     'sin_lat' => sin(\$lat_rad),     'cos_cos' => cos(\$lat_rad)*cos(\$lon_rad),     'cos_sin' => cos(\$lat_rad)*sin(\$lon_rad),     'lat' => \$user['lat'],     'lon' => \$user['lon']   )); } ``

Then you optimize the actual query to only do the distance calculation when really needed, for example by bounding the circle (well, oval) from inside and outside. For that, you'll need to precalculate several metrics for the query itself:

``// assuming the search center coordinates are \$lat and \$lon in degrees // and radius in km is given in \$distance \$lat_rad = deg2rad(\$lat); \$lon_rad = deg2rad(\$lon); \$R = 6371; // earth's radius, km \$distance_rad = \$distance/\$R; \$distance_rad_plus = \$distance_rad * 1.06; // ovality error for outer bounding box \$dist_deg_lat = rad2deg(\$distance_rad_plus); //outer bounding box \$dist_deg_lon = rad2deg(\$distance_rad_plus/cos(deg2rad(\$lat))); \$dist_deg_lat_small = rad2deg(\$distance_rad/sqrt(2)); //inner bounding box \$dist_deg_lon_small = rad2deg(\$distance_rad/cos(deg2rad(\$lat))/sqrt(2)); ``

Given those preparations, the query goes something like this (php):

``\$neighbors = DB::query("SELECT id, type, lat, lon,        geodistance(sin_lat,cos_cos,cos_sin,%d,%d,%d) as distance        FROM Coordinates WHERE        lat BETWEEN %d AND %d AND lon BETWEEN %d AND %d        HAVING (lat BETWEEN %d AND %d AND lon BETWEEN %d AND %d) OR distance <= %d",   // center radian values: sin_lat, cos_cos, cos_sin        sin(\$lat_rad),cos(\$lat_rad)*cos(\$lon_rad),cos(\$lat_rad)*sin(\$lon_rad),   // min_lat, max_lat, min_lon, max_lon for the outside box        \$lat-\$dist_deg_lat,\$lat+\$dist_deg_lat,        \$lon-\$dist_deg_lon,\$lon+\$dist_deg_lon,   // min_lat, max_lat, min_lon, max_lon for the inside box        \$lat-\$dist_deg_lat_small,\$lat+\$dist_deg_lat_small,        \$lon-\$dist_deg_lon_small,\$lon+\$dist_deg_lon_small,   // distance in radians        \$distance_rad); ``

EXPLAIN on the above query might say that it's not using index unless there's enough results to trigger such. The index will be used when there's enough data in the coordinates table. You can add FORCE INDEX (lat_lon_idx) to the SELECT to make it use the index with no regards to the table size, so you can verify with EXPLAIN that it is working correctly.

With the above code samples you should have a working and scalable implementation of object search by distance with minimal error.

68

I have had to work this out in some detail, so I'll share my result. This uses a `zip` table with `latitude` and `longitude` tables. It doesn't depend on Google Maps; rather you can adapt it to any table containing lat/long.

``SELECT zip, primary_city,         latitude, longitude, distance_in_mi   FROM ( SELECT zip, primary_city, latitude, longitude,r,        (3963.17 * ACOS(COS(RADIANS(latpoint))                   * COS(RADIANS(latitude))                   * COS(RADIANS(longpoint) - RADIANS(longitude))                   + SIN(RADIANS(latpoint))                   * SIN(RADIANS(latitude)))) AS distance_in_mi  FROM zip  JOIN (         SELECT  42.81  AS latpoint,  -70.81 AS longpoint, 50.0 AS r    ) AS p   WHERE latitude     BETWEEN latpoint  - (r / 69)        AND latpoint  + (r / 69)    AND longitude    BETWEEN longpoint - (r / (69 * COS(RADIANS(latpoint))))       AND longpoint + (r / (69 * COS(RADIANS(latpoint))))   ) d  WHERE distance_in_mi <= r  ORDER BY distance_in_mi  LIMIT 30 ``

Look at this line in the middle of that query:

``    SELECT  42.81  AS latpoint,  -70.81 AS longpoint, 50.0 AS r ``

This searches for the 30 nearest entries in the `zip` table within 50.0 miles of the lat/long point 42.81/-70.81 . When you build this into an app, that's where you put your own point and search radius.

If you want to work in kilometers rather than miles, change `69` to `111.045` and change `3963.17` to `6378.10` in the query.

Here's a detailed writeup. I hope it helps somebody. http://www.plumislandmedia.net/mysql/haversine-mysql-nearest-loc/

57

`` SELECT *, (       6371 * acos(cos(radians(search_lat)) * cos(radians(lat) ) *    cos(radians(lng) - radians(search_lng)) + sin(radians(search_lat)) *         sin(radians(lat)))   ) AS distance   FROM table   WHERE lat != search_lat AND lng != search_lng AND distance < 25    ORDER BY distance   FETCH 10 ONLY  ``

for distance of 25 km