问题
I\'ve got a working PHP script that gets Longitude and Latitude values and then inputs them into a MySQL query. I\'d like to make it solely MySQL. Here\'s my current PHP Code:
if ($distance != \"Any\" && $customer_zip != \"\") { //get the great circle distance
//get the origin zip code info
$zip_sql = \"SELECT * FROM zip_code WHERE zip_code = \'$customer_zip\'\";
$result = mysql_query($zip_sql);
$row = mysql_fetch_array($result);
$origin_lat = $row[\'lat\'];
$origin_lon = $row[\'lon\'];
//get the range
$lat_range = $distance/69.172;
$lon_range = abs($distance/(cos($details[0]) * 69.172));
$min_lat = number_format($origin_lat - $lat_range, \"4\", \".\", \"\");
$max_lat = number_format($origin_lat + $lat_range, \"4\", \".\", \"\");
$min_lon = number_format($origin_lon - $lon_range, \"4\", \".\", \"\");
$max_lon = number_format($origin_lon + $lon_range, \"4\", \".\", \"\");
$sql .= \"lat BETWEEN \'$min_lat\' AND \'$max_lat\' AND lon BETWEEN \'$min_lon\' AND \'$max_lon\' AND \";
}
Does anyone know how to make this entirely MySQL? I\'ve browsed the Internet a bit but most of the literature on it is pretty confusing.
回答1:
From Google Code FAQ - Creating a Store Locator with PHP, MySQL & Google Maps:
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;
回答2:
$greatCircleDistance = acos( cos($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;
is your SQL query
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))'))
回答3:
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.
回答4:
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/
回答5:
I have written a procedure that can calculate the same, but you have to enter the latitude and longitude in the respective table.
drop procedure if exists select_lattitude_longitude;
delimiter //
create procedure select_lattitude_longitude(In CityName1 varchar(20) , In CityName2 varchar(20))
begin
declare origin_lat float(10,2);
declare origin_long float(10,2);
declare dest_lat float(10,2);
declare dest_long float(10,2);
if CityName1 Not In (select Name from City_lat_lon) OR CityName2 Not In (select Name from City_lat_lon) then
select 'The Name Not Exist or Not Valid Please Check the Names given by you' as Message;
else
select lattitude into origin_lat from City_lat_lon where Name=CityName1;
select longitude into origin_long from City_lat_lon where Name=CityName1;
select lattitude into dest_lat from City_lat_lon where Name=CityName2;
select longitude into dest_long from City_lat_lon where Name=CityName2;
select origin_lat as CityName1_lattitude,
origin_long as CityName1_longitude,
dest_lat as CityName2_lattitude,
dest_long as CityName2_longitude;
SELECT 3956 * 2 * ASIN(SQRT( POWER(SIN((origin_lat - dest_lat) * pi()/180 / 2), 2) + COS(origin_lat * pi()/180) * COS(dest_lat * pi()/180) * POWER(SIN((origin_long-dest_long) * pi()/180 / 2), 2) )) * 1.609344 as Distance_In_Kms ;
end if;
end ;
//
delimiter ;
回答6:
I can't comment on the above answer, but be careful with @Pavel Chuchuva's answer. That formula will not return a result if both coordinates are the same. In that case, distance is null, and so that row won't be returned with that formula as is.
I'm not a MySQL expert, but this seems to be working for me:
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 OR distance IS NULL ORDER BY distance LIMIT 0 , 20;
回答7:
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
回答8:
I thought my javascript implementation would be a good reference to:
/*
* Check to see if the second coord is within the precision ( meters )
* of the first coord and return accordingly
*/
function checkWithinBound(coord_one, coord_two, precision) {
var distance = 3959000 * Math.acos(
Math.cos( degree_to_radian( coord_two.lat ) ) *
Math.cos( degree_to_radian( coord_one.lat ) ) *
Math.cos(
degree_to_radian( coord_one.lng ) - degree_to_radian( coord_two.lng )
) +
Math.sin( degree_to_radian( coord_two.lat ) ) *
Math.sin( degree_to_radian( coord_one.lat ) )
);
return distance <= precision;
}
/**
* Get radian from given degree
*/
function degree_to_radian(degree) {
return degree * (Math.PI / 180);
}
回答9:
calculate distance in Mysql
SELECT (6371 * acos(cos(radians(lat2)) * cos(radians(lat1) ) * cos(radians(long1) -radians(long2)) + sin(radians(lat2)) * sin(radians(lat1)))) AS distance
thus distance value will be calculated and anyone can apply as required.
来源：https://stackoverflow.com/questions/574691/mysql-great-circle-distance-haversine-formula