10

I have to get distance from different markers on the map to the current location of the device and the pick up the shortest one. I have the lat and long for the markers and the current location lat and long can be fetched dynamically.

Suppose I have 5 markers on the map, Bangalore (Lat : 12.971599, Long : 77.594563), Delhi (Lat : 28.635308, Long : 77.224960), Mumbai (Lat : 19.075984, Long : 72.877656), Chennai (Lat : 13.052414, Long : 80.250825), Kolkata (Lat : 22.572646, Long : 88.363895).

Now suppose the user is standing somewhere near Hyderabad (Lat : 17.385044, Long : 78.486671). When the user clicks the button, the app should calculate distance from each marker and pick up and return the shortest one, that will be Bangalore here.

There is a way possible to do it with help of local databases. Can anyone help on that please.?

Can anyone suggest me a nice way to do this, or come up with a good code if you please can. Thanx in advance.

Aishvarya Jaiswal
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9 Answers9

4

from your comment I see that you expect a maximum of 70-80 locations. This is not much.

You can simply do a brute force search over all markers and take the minimum.

Iterate over all markers, and search min distance:

    List<Marker> markers = createMarkers(); // returns an ArrayList<Markers> from your data source
    int minIndex = -1;
    double minDist = 1E38; // initialize with a huge value that will be overwritten
    int size = markers.size();
    for (int i = 0; i < size; i++) {
        Marker marker = markers.get(i);
        double curDistance = calcDistance(curLatitude, curLongitude, marker.latitude, marker.longitude);
      if (curDistance < minDist) {
         minDist = curDistance;  // update neares
         minIndex = i;           // store index of nearest marker in minIndex
      }
    }

    if (minIndex >= 0) {
       // now nearest maker found:
       Marker nearestMarker = markers.get(minIndex);
       // TODO do something with nearesr marker
    } else {
      // list of markers was empty
    }

For calcDistance, use the distance calculation method provided by android. (e.g Location.distanceTo() )
For 70-80 markers there is no need to make it faster and much more complex. If you have some thousands points then it is worth to invest in a faster solution (using a spatial index, and an own distance calculation which avoids the sqrt calc).

Just print out the current time in milli seconds at the begin and at the end of the nearest maker search, and you will see, that it is fast enough.

AlexWien
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3

How about looping over all markers and checking the distance using Location.distanceBetween? There is no magic involved ;)

List<Marker> markers;
LatLng currentPosition;

float minDistance = Float.MAX_VALUE;
Marker closest = null;
float[] currentDistance = new float[1];
for (Marker marker : markers) {
    LatLng markerPosition = marker.getPosition();
    Location.distanceBetween(currentPosition.latitude, currentPosition.longitude, markerPosition.latitude, markerPosition.longitude, currentDistance);
    if (minDistance > currentDistance[0]) {
        minDistance = currentDistance[0];
        closest = marker;
    }
}
MaciejGórski
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3

If you want to find the shortest one not list the closest and you want the process to scale to a large amount of locations, you can do some filtering before you calculate distances and you can simplify the formula to speed it up as you don't care about actual distances (i.e. remove the multiplication by the radius of the earth).

Filtering algorithm, looping through each location :

  1. Calculate the difference in lat and long.
  2. If both differences are larger then a previously processed pair, discard it.
  3. Calculate distance, keep smallest.

You can further help the algorithm by feeding it with what might be close locations first. For example if you know one of the points is in the same country or state.


Here is some Python code to do that, use it as pseudocode for your solution :

locations = { 
    'Bangalore' : (12.971599, 77.594563), 
    'Delhi' : (28.635308,  77.224960), 
    'Mumbai' : (19.075984,  72.877656), 
    'Chennai' : (13.052414,  80.250825), 
    'Kolkata' : (22.572646,  88.363895)
    }

from math import sin, cos, atan2, sqrt

EARTH_RADIUS = 6373  # km

def distance(a, b):  # pass tuples
    (lat1, lon1) = a
    (lat2, lon2) = b
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = (sin(dlat/2))**2 + cos(lat1) * cos(lat2) * (sin(dlon/2))**2 
    c = 2 * atan2( sqrt(a), sqrt(1-a) ) 
    return EARTH_RADIUS * c


current = (17.385044, 78.486671)  # current lat & lng

closest = None
closest_name = None
for name, cordinates in locations.iteritems():
    d = distance(current, cordinates)
    if closest is None or d < closest:
        closest = d
        closest_name = name
    print "~%dkm (%s)" % (distance(current, cordinates), name)

print "\nClosest location is %s, %d km away." % (closest_name, closest)

Output :

~5700km (Kolkata)
~13219km (Chennai)
~12159km (Bangalore)
~7928km (Delhi)
~10921km (Mumbai)

Closest location is Kolkata, 5700 km away.
Emil Davtyan
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  • can you please suggest me by a small code please. I tried but no output.. Please help.. – Aishvarya Jaiswal Oct 07 '13 at 12:43
  • @Akshat Checkout the code, I posted I will also post the algorithm for filtering some locations to make it scale. – Emil Davtyan Oct 09 '13 at 08:40
  • @Emil.. Thanx a lot.. But I need Java code to use in Android. Although I will refer this and I will try to produce the code in Java tomorrow first thing. Heartily thanx a tonn for helping in such a nice way.. :) :) – Aishvarya Jaiswal Oct 09 '13 at 17:11
2

Although there has already been posted some answer, I thought I would present my implementation in java. This has been used with 4000+ markers wrapped in an AsyncTask and has been working with no problems.

First, the logic to calculate distance (assuming you only have the markers and not Location objects, as those gives the possibility to do loc1.distanceTo(loc2)):

private float distBetween(LatLng pos1, LatLng pos2) {
    return distBetween(pos1.latitude, pos1.longitude, pos2.latitude,
            pos2.longitude);
}

/** distance in meters **/
private float distBetween(double lat1, double lng1, double lat2, double lng2) {
    double earthRadius = 3958.75;
    double dLat = Math.toRadians(lat2 - lat1);
    double dLng = Math.toRadians(lng2 - lng1);
    double a = Math.sin(dLat / 2) * Math.sin(dLat / 2)
            + Math.cos(Math.toRadians(lat1))
            * Math.cos(Math.toRadians(lat2)) * Math.sin(dLng / 2)
            * Math.sin(dLng / 2);
    double c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));
    double dist = earthRadius * c;

    int meterConversion = 1609;

    return (float) (dist * meterConversion);
}

Next, the code for selecting the nearest marker:

private Marker getNearestMarker(List<Marker> markers,
        LatLng origin) {

    Marker nearestMarker = null;
    double lowestDistance = Double.MAX_VALUE;

    if (markers != null) {

        for (Marker marker : markers) {

            double dist = distBetween(origin, marker.getPosition());

            if (dist < lowestDistance) {
                nearestMarker = marker;
                lowestDistance = dist;
            }
        }
    }

    return nearestMarker;
}

Perhaps not relevant for your use case but I use the algorithm to select the nearest markers based on a predefined distance. This way I weed out a lot of unnecessary markers:

private List<Marker> getSurroundingMarkers(List<Marker> markers,
        LatLng origin, int maxDistanceMeters) {
    List<Marker> surroundingMarkers = null;

    if (markers != null) {
        surroundingMarkers = new ArrayList<Marker>();
        for (Marker marker : markers) {

            double dist = distBetween(origin, marker.getPosition());

            if (dist < maxDistanceMeters) {
                surroundingMarkers.add(marker);
            }
        }
    }

    return surroundingMarkers;
}

Hope this helps you

cYrixmorten
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  • getSurroundingMarkers: I do not understand the sense: you iterate over all markers and calculate for all the distance and keep the near ones in a list. Then later to find the closes you need to calulate again over the surounding ones. This is a double calculation, this gains nothing – AlexWien Oct 10 '13 at 19:23
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    Your distBetween implementation is strange. first you use miles insetad of meters for earth radius, and to correct that you convert to meters later. better use earth radius in meters and avoid the last meterConversion – AlexWien Oct 10 '13 at 19:27
  • @AlexWien getSurroundingMarkers is used on its own. As I write, this is just something I use to get a subset of all the markers that are within a certain distance. Regarding distBetween you might be right, though it works and the conversion has no performance impact. – cYrixmorten Oct 11 '13 at 13:25
2

This code could help you getting the distances: https://github.com/BeyondAR/beyondar/blob/master/android/BeyondAR_Framework/src/com/beyondar/android/util/math/Distance.java

Joan P.S
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2

Here is my implementation of a so called KDTree, consisting of 3 classes: KDTree, KDTNode and KDTResult. What you finally need is to create the KDTree using KDTree.createTree(), which returns the rootNode of the tree and gets all your fixed points passed in. Then use KDTree.findNearestWp() to find the nearest Waypoint to the given location.

KDTree:

public class KDTree {

private Comparator<LatLng> latComparator = new LatLonComparator(true);
private Comparator<LatLng> lonComparator = new LatLonComparator(false);;

/**
 * Create a KDTree from a list of Destinations. Returns the root-node of the
 * tree.
 */
public KDTNode createTree(List<LatLng> recList) {
    return createTreeRecursive(0, recList);
}

/**
 * Traverse the tree and find the nearest WP.
 * 
 * @param root
 * @param wp
 * @return
 */
static public LatLng findNearestWp(KDTNode root, LatLng wp) {
    KDTResult result = new KDTResult();
    findNearestWpRecursive(root, wp, result);
    return result.nearestDest;
}

private static void findNearestWpRecursive(KDTNode node, LatLng wp,
        KDTResult result) {
    double lat = wp.latitude;
    double lon = wp.longitude;
    /* If a leaf node, calculate distance and return. */
    if (node.isLeaf) {
        LatLng dest = node.wp;
        double latDiff = dest.latitude - lat;
        double lonDiff = dest.longitude - lon;
        double squareDist = latDiff * latDiff + lonDiff * lonDiff;
        // Replace a previously found nearestDest only if the new one is
        // nearer.
        if (result.nearestDest == null
                || result.squareDistance > squareDist) {
            result.nearestDest = dest;
            result.squareDistance = squareDist;
        }
        return;
    }
    boolean devidedByLat = node.depth % 2 == 0;
    boolean goLeft;
    /* Check whether left or right is more promising. */
    if (devidedByLat) {
        goLeft = lat < node.splitValue;
    } else {
        goLeft = lon < node.splitValue;
    }
    KDTNode child = goLeft ? node.left : node.right;
    findNearestWpRecursive(child, wp, result);
    /*
     * Check whether result needs to be checked also against the less
     * promising side.
     */
    if (result.squareDistance > node.minSquareDistance) {
        KDTNode otherChild = goLeft ? node.right : node.left;
        findNearestWpRecursive(otherChild, wp, result);
    }

}

private KDTNode createTreeRecursive(int depth, List<LatLng> recList) {
    KDTNode node = new KDTNode();
    node.depth = depth;
    if (recList.size() == 1) {
        // Leafnode found
        node.isLeaf = true;
        node.wp = recList.get(0);
        return node;
    }
    boolean divideByLat = node.depth % 2 == 0;
    sortRecListByDimension(recList, divideByLat);
    List<LatLng> leftList = getHalfOf(recList, true);
    List<LatLng> rightList = getHalfOf(recList, false);
    // Get split point and distance to last left and first right point.
    LatLng lastLeft = leftList.get(leftList.size() - 1);
    LatLng firstRight = rightList.get(0);
    double minDistanceToSplitValue;
    double splitValue;
    if (divideByLat) {
        minDistanceToSplitValue = (firstRight.latitude - lastLeft.latitude) / 2;
        splitValue = lastLeft.latitude + Math.abs(minDistanceToSplitValue);
    } else {
        minDistanceToSplitValue = (firstRight.longitude - lastLeft.longitude) / 2;
        splitValue = lastLeft.longitude + Math.abs(minDistanceToSplitValue);
    }
    node.splitValue = splitValue;
    node.minSquareDistance = minDistanceToSplitValue
            * minDistanceToSplitValue;
    /** Call next level */
    depth++;
    node.left = createTreeRecursive(depth, leftList);
    node.right = createTreeRecursive(depth, rightList);
    return node;
}

/**
 * Return a sublist representing the left or right half of a List. Size of
 * recList must be at least 2 !
 * 
 * IMPORTANT !!!!! Note: The original list must not be modified after
 * extracting this sublist, as the returned subList is still backed by the
 * original list.
 */
List<LatLng> getHalfOf(List<LatLng> recList, boolean leftHalf) {
    int mid = recList.size() / 2;
    if (leftHalf) {
        return recList.subList(0, mid);
    } else {
        return recList.subList(mid, recList.size());
    }
}

private void sortRecListByDimension(List<LatLng> recList, boolean sortByLat) {
    Comparator<LatLng> comparator = sortByLat ? latComparator
            : lonComparator;
    Collections.sort(recList, comparator);
}

class LatLonComparator implements Comparator<LatLng> {
    private boolean byLat;

    public LatLonComparator(boolean sortByLat) {
        this.byLat = sortByLat;
    }

    @Override
    public int compare(LatLng lhs, LatLng rhs) {
        double diff;
        if (byLat) {
            diff = lhs.latitude - rhs.latitude;
        } else {
            diff = lhs.longitude - rhs.longitude;
        }
        if (diff > 0) {
            return 1;
        } else if (diff < 0) {
            return -1;
        } else {
            return 0;
        }
    }

}
}

KDTNode:

/** Node of the KDTree */
public class KDTNode {

KDTNode left;
KDTNode right;
boolean isLeaf;
/** latitude or longitude of the nodes division line. */
double splitValue;
/** Distance between division line and first point. */
double minSquareDistance;
/**
 * Depth of the node in the tree. An even depth devides the tree in the
 * latitude-axis, an odd depth devides the tree in the longitude-axis.
 */
int depth;
/** The Waypoint in case the node is a leaf node. */
LatLng wp;

}

KDTResult:

/** Holds the result of a tree traversal. */
public class KDTResult {
LatLng nearestDest;
// I use the square of the distance to avoid square-root operations.
double squareDistance;
}

Please note, that I am using a simplified distance calculation, which works in my case, as I am only interested in very nearby waypoints. For points further apart, this may result in getting not exactly the nearest point. The absolute difference of two longitudes expressed as east-west distance in meters, depends on the latitude, where this difference is measured. This is not taken into account in my algorithm and I am not sure about the relevance of this effect in your case.

user2808624
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  • Thank you very much.. I need to Implement it for sure. if I get any prob will you help me plaese.? – Aishvarya Jaiswal Oct 15 '13 at 17:15
  • Sure I can try to help. But please note my (new) remarks in the last paragraph. I had to edit it compared to my first post. It was a bit too simple. But I hope it still helps, and I think it still works pretty well, if the latitude of the waypoints does not differ too much. You may also search for KD-Tree in SO, and find some discussions about handling of coordinates on a sphere. – user2808624 Oct 15 '13 at 19:15
  • See also my second answer which implements a 3 dimensional KD-Tree and translates the lat/lon coordinates into a cartesian coordinate system. – user2808624 Oct 15 '13 at 20:51
1

An efficient way to search for the smallest distance between a single point (that may change frequently), and a large set of points, in two dimensions is to use a QuadTree. There is a cost to initially build the QuadTree (i.e., add your marker locations to the data structure), so you only want to do this once (or as infrequently as possible). But, once constructed, searches for the closest point will typically be faster than a brute force comparison against all points in the large set.

BBN's OpenMap project has an open-source QuadTree Java implementation that I believe should work on Android that has a get(float lat, float lon) method to return the closest point.

Google's android-maps-utils library also has an open-source implementation of a QuadTree intended to run on Android, but as it is currently written it only supports a search(Bounds bounds) operation to return a set of points in a given bounding box, and not the point closest to an input point. But, it could be modified to perform the closest point search.

If you have a relatively small number of points (70-80 may be sufficiently small), then in real-world performance a brute-force comparison may execute in a similar amount of time to the QuadTree solution. But, it also depends on how frequently you intended on re-calculating the closest point - if frequent, a QuadTree may be a better choice.

Sean Barbeau
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1

I thought it should not be too difficult to extend my KDTree (see my other answer) also to a 3 dimensional version, and here is the result. But as I do not use this version myself so far, take it with care. I added a unit-test, which shows that it works at least for your example.

/** 3 dimensional implementation of a KDTree for LatLng coordinates. */
public class KDTree {

private XYZComparator xComparator = new XYZComparator(0);
private XYZComparator yComparator = new XYZComparator(1);
private XYZComparator zComparator = new XYZComparator(2);
private XYZComparator[] comparators = { xComparator, yComparator,
        zComparator };

/**
 * Create a KDTree from a list of lat/lon coordinates. Returns the root-node
 * of the tree.
 */
public KDTNode createTree(List<LatLng> recList) {
    List<XYZ> xyzList = convertTo3Dimensions(recList);
    return createTreeRecursive(0, xyzList);
}

/**
 * Traverse the tree and find the point nearest to wp.
 */
static public LatLng findNearestWp(KDTNode root, LatLng wp) {
    KDTResult result = new KDTResult();
    XYZ xyz = convertTo3Dimensions(wp);
    findNearestWpRecursive(root, xyz, result);
    return result.nearestWp;
}

/** Convert lat/lon coordinates into a 3 dimensional xyz system. */
private static XYZ convertTo3Dimensions(LatLng wp) {
    // See e.g.
    // http://stackoverflow.com/questions/8981943/lat-long-to-x-y-z-position-in-js-not-working
    double cosLat = Math.cos(wp.latitude * Math.PI / 180.0);
    double sinLat = Math.sin(wp.latitude * Math.PI / 180.0);
    double cosLon = Math.cos(wp.longitude * Math.PI / 180.0);
    double sinLon = Math.sin(wp.longitude * Math.PI / 180.0);
    double rad = 6378137.0;
    double f = 1.0 / 298.257224;
    double C = 1.0 / Math.sqrt(cosLat * cosLat + (1 - f) * (1 - f) * sinLat
            * sinLat);
    double S = (1.0 - f) * (1.0 - f) * C;
    XYZ result = new XYZ();
    result.x = (rad * C) * cosLat * cosLon;
    result.y = (rad * C) * cosLat * sinLon;
    result.z = (rad * S) * sinLat;
    result.wp = wp;
    return result;
}

private List<XYZ> convertTo3Dimensions(List<LatLng> recList) {
    List<XYZ> result = new ArrayList<KDTree.XYZ>();
    for (LatLng latLng : recList) {
        XYZ xyz = convertTo3Dimensions(latLng);
        result.add(xyz);
    }
    return result;
}

private static void findNearestWpRecursive(KDTNode node, XYZ wp,
        KDTResult result) {
    /* If a leaf node, calculate distance and return. */
    if (node.isLeaf) {
        double xDiff = node.xyz.x - wp.x;
        double yDiff = node.xyz.y - wp.y;
        double zDiff = node.xyz.z - wp.z;
        double squareDist = xDiff * xDiff + yDiff * yDiff + zDiff * zDiff;
        // Replace a previously found nearestDest only if the new one is
        // nearer.
        if (result.nearestWp == null || result.squareDistance > squareDist) {
            result.nearestWp = node.xyz.wp;
            result.squareDistance = squareDist;
        }
        return;
    }
    int devidedByDimension = node.depth % 3;
    boolean goLeft;
    /* Check whether left or right is more promising. */
    if (devidedByDimension == 0) {
        goLeft = wp.x < node.splitValue;
    } else if (devidedByDimension == 1) {
        goLeft = wp.y < node.splitValue;
    } else {
        goLeft = wp.z < node.splitValue;
    }
    KDTNode child = goLeft ? node.left : node.right;
    findNearestWpRecursive(child, wp, result);
    /*
     * Check whether result needs to be checked also against the less
     * promising side.
     */
    if (result.squareDistance > node.minSquareDistance) {
        KDTNode otherChild = goLeft ? node.right : node.left;
        findNearestWpRecursive(otherChild, wp, result);
    }

}

private KDTNode createTreeRecursive(int depth, List<XYZ> recList) {
    KDTNode node = new KDTNode();
    node.depth = depth;
    if (recList.size() == 1) {
        // Leafnode found
        node.isLeaf = true;
        node.xyz = recList.get(0);
        return node;
    }
    int dimension = node.depth % 3;
    sortWayPointListByDimension(recList, dimension);
    List<XYZ> leftList = getHalfOf(recList, true);
    List<XYZ> rightList = getHalfOf(recList, false);
    // Get split point and distance to last left and first right point.
    XYZ lastLeft = leftList.get(leftList.size() - 1);
    XYZ firstRight = rightList.get(0);
    double minDistanceToSplitValue;
    double splitValue;
    if (dimension == 0) {
        minDistanceToSplitValue = (firstRight.x - lastLeft.x) / 2;
        splitValue = lastLeft.x + Math.abs(minDistanceToSplitValue);
    } else if (dimension == 1) {
        minDistanceToSplitValue = (firstRight.y - lastLeft.y) / 2;
        splitValue = lastLeft.y + Math.abs(minDistanceToSplitValue);
    } else {
        minDistanceToSplitValue = (firstRight.z - lastLeft.z) / 2;
        splitValue = lastLeft.z + Math.abs(minDistanceToSplitValue);
    }
    node.splitValue = splitValue;
    node.minSquareDistance = minDistanceToSplitValue
            * minDistanceToSplitValue;
    /** Call next level */
    depth++;
    node.left = createTreeRecursive(depth, leftList);
    node.right = createTreeRecursive(depth, rightList);
    return node;
}

/**
 * Return a sublist representing the left or right half of a List. Size of
 * recList must be at least 2 !
 * 
 * IMPORTANT !!!!! Note: The original list must not be modified after
 * extracting this sublist, as the returned subList is still backed by the
 * original list.
 */
List<XYZ> getHalfOf(List<XYZ> xyzList, boolean leftHalf) {
    int mid = xyzList.size() / 2;
    if (leftHalf) {
        return xyzList.subList(0, mid);
    } else {
        return xyzList.subList(mid, xyzList.size());
    }
}

private void sortWayPointListByDimension(List<XYZ> wayPointList, int sortBy) {
    XYZComparator comparator = comparators[sortBy];
    Collections.sort(wayPointList, comparator);
}

class XYZComparator implements Comparator<XYZ> {
    private int sortBy;

    public XYZComparator(int sortBy) {
        this.sortBy = sortBy;
    }

    @Override
    public int compare(XYZ lhs, XYZ rhs) {
        double diff;
        if (sortBy == 0) {
            diff = lhs.x - rhs.x;
        } else if (sortBy == 1) {
            diff = lhs.y - rhs.y;
        } else {
            diff = lhs.z - rhs.z;
        }
        if (diff > 0) {
            return 1;
        } else if (diff < 0) {
            return -1;
        } else {
            return 0;
        }
    }

}

/** 3 Dimensional coordinates of a waypoint. */
static class XYZ {
    double x;
    double y;
    double z;
    // Keep also the original waypoint
    LatLng wp;
}

/** Node of the KDTree */
public static class KDTNode {

    KDTNode left;
    KDTNode right;
    boolean isLeaf;
    /** latitude or longitude of the nodes division line. */
    double splitValue;
    /** Distance between division line and first point. */
    double minSquareDistance;
    /**
     * Depth of the node in the tree. Depth 0,3,6.. devides the tree in the
     * x-axis, depth 1,4,7,.. devides the tree in the y-axis and depth
     * 2,5,8... devides the tree in the z axis.
     */
    int depth;
    /** The Waypoint in case the node is a leaf node. */
    XYZ xyz;

}

/** Holds the result of a tree traversal. */
static class KDTResult {
    LatLng nearestWp;
    // We use the square of the distance to avoid square-root operations.
    double squareDistance;
}
}

And here is the unit test:

public void testSOExample() {
    KDTree tree = new KDTree();
    LatLng Bangalore = new LatLng(12.971599, 77.594563);
    LatLng Delhi = new LatLng(28.635308, 77.224960);
    LatLng Mumbai = new LatLng(19.075984, 72.877656);
    LatLng Chennai = new LatLng(13.052414, 80.250825);
    LatLng Kolkata = new LatLng(22.572646, 88.363895);
    List<LatLng> cities = Arrays.asList(new LatLng[] { Bangalore, Delhi,
            Mumbai, Chennai, Kolkata });

    KDTree.KDTNode root = tree.createTree(cities);
    LatLng Hyderabad = new LatLng(17.385044, 78.486671);
    LatLng nearestWp = tree.findNearestWp(root, Hyderabad);

    assertEquals(nearestWp, Bangalore);
}
user2808624
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1

Here, I got a way to do that Using databases. This is a calculate distance function:

public void calculateDistance() {

  if (latitude != 0.0 && longitude != 0.0) {
    for(int i=0;i<97;i++) { 
      Location myTargetLocation=new Location("");
      myTargetLocation.setLatitude(targetLatitude[i]);
      myTargetLocation.setLongitude(targetLongitude[i]);
      distance[i]=myCurrentLocation.distanceTo(myTargetLocation);
      distance[i]=distance[i]/1000;
      mdb.insertDetails(name[i],targetLatitude[i], targetLongitude[i], distance[i]);
    }

    Cursor c1= mdb.getallDetail();
    while (c1.moveToNext()) {
      String station_name=c1.getString(1);
      double latitude=c1.getDouble(2);
      double longitude=c1.getDouble(3);
      double dis=c1.getDouble(4);
      //Toast.makeText(getApplicationContext(),station_name+" & "+latitude+" &  "+longitude+" &  "+dis,1).show();
    }
    Arrays.sort(distance);
    double nearest_distance=distance[0];
    Cursor c2=mdb.getNearestStationName();
    {
        while (c2.moveToNext()) {

            double min_dis=c2.getDouble(4);
            if(min_dis==nearest_distance)
            {
                String nearest_stationName=c2.getString(1);
                if(btn_clicked.equals("source"))
                {
                source.setText(nearest_stationName);
                break;
                }
                else if(btn_clicked.equals("dest"))
                {
                    destination.setText(nearest_stationName);
                    break;
                }
                else
                {

                }
            }
        }
    }
     }
     else
     {
         Toast.makeText(this, "GPS is Not Working Properly,, please check Gps and  Wait for few second", 1).show(); 
     }
}

All we have to do is Create an array named targetLatitude[i] and targetLongitude[i] containing Lats and Longs of all the places you want to calculate distance from. Then create a database as shown below:

public class MyDataBase {
    SQLiteDatabase sdb;
    MyHelper mh;
    MyDataBase(Context con)
    {
    mh = new MyHelper(con, "Metro",null, 1);
    }

public void open() {
try
{
sdb=mh.getWritableDatabase();
}
catch(Exception e)
{

}


}

public void insertDetails(String name,double latitude,double longitude,double distance) {

ContentValues cv=new ContentValues();
cv.put("name", name);
cv.put("latitude", latitude);
cv.put("longitude",longitude);
cv.put("distance", distance);
sdb.insert("stations", null, cv);
}

public void insertStops(String stop,double latitude,double logitude)
{
    ContentValues cv=new ContentValues();
    cv.put("stop", stop);
    cv.put("latitude", latitude);
    cv.put("logitude", logitude);
    sdb.insert("stops", null, cv);

}



public Cursor getallDetail()
{   
    Cursor c=sdb.query("stations",null,null,null,null,null,null);
    return c;
}
public Cursor getNearestStationName() {
    Cursor c=sdb.query("stations",null,null,null,null,null,null);
    return c;
}


public Cursor getStops(String stop)
{
    Cursor c;
    c=sdb.query("stops",null,"stop=?",new String[]{stop},null, null, null);
    return c;
}

class MyHelper extends SQLiteOpenHelper
{

    public MyHelper(Context context, String name, CursorFactory factory,
            int version) {
        super(context, name, factory, version);
        // TODO Auto-generated constructor stub
    }

    @Override
    public void onCreate(SQLiteDatabase db) {
        // TODO Auto-generated method stub
        db.execSQL("Create table stations(_id integer primary key,name text," +
                " latitude double, longitude double, distance double );");
        db.execSQL("Create table stops(_id integer primary key,stop text," +
                "latitude double,logitude double);");

    }

    @Override
    public void onUpgrade(SQLiteDatabase db, int oldVersion, int newVersion) {
        // TODO Auto-generated method stub

    }

}
public void deleteDetail() {
    sdb.delete("stations",null,null);
    sdb.delete("stops",null,null);

}

public void close() {
    sdb.close();

}

}

Then execute the CalculateDistance function wherever you want and you can get the nearest station name.

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Aishvarya Jaiswal
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