views:

132

answers:

3

I need to filter geocodes for near-ness to a location. For example, I want to filter a list of restaurant geocodes to identify those restaurants within 10 miles of my current location.

Can someone point me to a function that will convert a distance into latitude & longitude deltas? For example:

class GeoCode(object):
   """Simple class to store geocode as lat, lng attributes."""
   def __init__(self, lat=0, lng=0, tag=None):
      self.lat = lat
      self.lng = lng
      self.tag = None

def distance_to_deltas(geocode, max_distance):
   """Given a geocode and a distance, provides dlat, dlng
      such that

         |geocode.lat - dlat| <= max_distance
         |geocode.lng - dlng| <= max_distance
   """
   # implementation
   # uses inverse Haversine, or other function?
   return dlat, dlng

Note: I am using the supremum norm for distance.

+1  A: 

This is how you calculate distances between lat/long pairs using the haversine formula:

import math 

R = 6371 # km
dLat = (lat2-lat1) # Make sure it's in radians, not degrees
dLon = (lon2-lon1) # Idem 
a = math.sin(dLat/2) * math.sin(dLat/2) +
    math.cos(lat1) * math.cos(lat2) * 
    math.sin(dLon/2) * math.sin(dLon/2) 
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a)) 
d = R * c;

It is now trivial to test "d" (also in km) against your threshold. If you want something else than km, adjust the radius.

I'm sorry I can't give you a drop-in solution, but I do not understand your code skeleton (see comment).

Also note that these days you probably want to use the spherical law of cosines rather than Haversine. The advantages in numerical stability are no longer worth it, and it's a hell of a lot simple to understand, code and use.

Ranieri
@Ranieri Yes, I have the haversine formula. I am looking for the implementation of the inversion.
Andrew B.
@Ranieri Fixed the code snippet. Basically I want to compute the max/min lat/lng values that are within a max_distance range of a geocode. So inverse_haversine should return a dlat, dlng tuple that are the +/- deltas that would still be within range. Does that help?
Andrew B.
A: 

If you store data in MongoDB, it does nicely indexed geolocation searches for you, and is superior to the pure-Python solutions above because it will handle optimization for you.

http://www.mongodb.org/display/DOCS/Geospatial+Indexing

A. Jesse Jiryu Davis
Chris Columbus wouldn't have "discovered" America with this gear: "The current implementation assumes an idealized model of a flat earth, meaning that an arcdegree of latitude (y) and longitude (x) represent the same distance everywhere."
John Machin
Yes, that *will* be a limitation near the poles. A location N or S of the center point will be considered closer than it should be, and a location E or W will be considered farther.
A. Jesse Jiryu Davis
+1  A: 

There seems not to have been a good Python implementation. Fortunately the SO "Related articles" sidebar is our friend. This SO article points to an excellent article that gives the maths and a Java implementation. The actual function that you require is rather short and is embedded in my Python code below. Tested to extent shown. Read warnings in comments.

from math import sin, cos, asin, sqrt, degrees, radians

Earth_radius_km = 6371.0
RADIUS = Earth_radius_km

def haversine(angle_radians):
    return sin(angle_radians / 2.0) ** 2

def inverse_haversine(h):
    return 2 * asin(sqrt(h)) # radians

def distance_between_points(lat1, lon1, lat2, lon2):
    # all args are in degrees
    # WARNING: loss of absolute precision when points are near-antipodal
    lat1 = radians(lat1)
    lat2 = radians(lat2)
    dlat = lat2 - lat1
    dlon = radians(lon2 - lon1)
    h = haversine(dlat) + cos(lat1) * cos(lat2) * haversine(dlon)
    return RADIUS * inverse_haversine(h)

def bounding_box(lat, lon, distance):
    # Input and output lats/longs are in degrees.
    # Distance arg must be in same units as RADIUS.
    # Returns (dlat, dlon) such that
    # no points outside lat +/- dlat or outside lon +/- dlon
    # are <= "distance" from the (lat, lon) point.
    # Derived from: http://janmatuschek.de/LatitudeLongitudeBoundingCoordinates
    # WARNING: problems if North/South Pole is in circle of interest
    # WARNING: problems if longitude meridian +/-180 degrees intersects circle of interest
    # See quoted article for how to detect and overcome the above problems.
    # Note: the result is independent of the longitude of the central point, so the
    # "lon" arg is not used.
    dlat = distance / RADIUS
    dlon = asin(sin(dlat) / cos(radians(lat)))
    return degrees(dlat), degrees(dlon)

if __name__ == "__main__":

    # Examples from Jan Matuschek's article

    def test(lat, lon, dist):
        print "test bounding box", lat, lon, dist
        dlat, dlon = bounding_box(lat, lon, dist)
        print "dlat, dlon degrees", dlat, dlon
        print "lat min/max rads", map(radians, (lat - dlat, lat + dlat))
        print "lon min/max rads", map(radians, (lon - dlon, lon + dlon))

    print "liberty to eiffel"
    print distance_between_points(40.6892, -74.0444, 48.8583, 2.2945) # about 5837 km
    print
    print "calc min/max lat/lon"
    degs = map(degrees, (1.3963, -0.6981))
    test(*degs, dist=1000)
    print
    degs = map(degrees, (1.3963, -0.6981, 1.4618, -1.6021))
    print degs, "distance", distance_between_points(*degs) # 872 km
John Machin