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/*
* The JTS Topology Suite is a collection of Java classes that
* implement the fundamental operations required to validate a given
* geo-spatial data set to a known topological specification.
*
* Copyright (C) 2001 Vivid Solutions
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*
* For more information, contact:
*
* Vivid Solutions
* Suite #1A
* 2328 Government Street
* Victoria BC V8T 5G5
* Canada
*
* (250)385-6040
* www.vividsolutions.com
*/
package com.vividsolutions.jts.index.strtree;
import com.vividsolutions.jts.index.strtree.AbstractSTRtree;
import java.io.Serializable;
import java.util.*;
import com.vividsolutions.jts.geom.*;
import com.vividsolutions.jts.util.*;
import com.vividsolutions.jts.util.PriorityQueue;
import com.vividsolutions.jts.index.*;
/**
* A query-only R-tree created using the Sort-Tile-Recursive (STR) algorithm.
* For two-dimensional spatial data.
*
* The STR packed R-tree is simple to implement and maximizes space
* utilization; that is, as many leaves as possible are filled to capacity.
* Overlap between nodes is far less than in a basic R-tree. However, once the
* tree has been built (explicitly or on the first call to #query), items may
* not be added or removed.
*
* Described in: P. Rigaux, Michel Scholl and Agnes Voisard.
* Spatial Databases With Application To GIS.
* Morgan Kaufmann, San Francisco, 2002.
*
* This class is thread-safe. Building the tree is synchronized,
* and querying is stateless.
*
* @version 1.7
*/
public class STRtree extends AbstractSTRtree
implements SpatialIndex, Serializable
{
private static final class STRtreeNode extends AbstractNode
{
private STRtreeNode(int level)
{
super(level);
}
protected Object computeBounds() {
Envelope bounds = null;
for (Iterator i = getChildBoundables().iterator(); i.hasNext(); ) {
Boundable childBoundable = (Boundable) i.next();
if (bounds == null) {
bounds = new Envelope((Envelope)childBoundable.getBounds());
}
else {
bounds.expandToInclude((Envelope)childBoundable.getBounds());
}
}
return bounds;
}
}
/**
*
*/
private static final long serialVersionUID = 259274702368956900L;
private static Comparator xComparator =
new Comparator() {
public int compare(Object o1, Object o2) {
return compareDoubles(
centreX((Envelope)((Boundable)o1).getBounds()),
centreX((Envelope)((Boundable)o2).getBounds()));
}
};
private static Comparator yComparator =
new Comparator() {
public int compare(Object o1, Object o2) {
return compareDoubles(
centreY((Envelope)((Boundable)o1).getBounds()),
centreY((Envelope)((Boundable)o2).getBounds()));
}
};
private static double centreX(Envelope e) {
return avg(e.getMinX(), e.getMaxX());
}
private static double centreY(Envelope e) {
return avg(e.getMinY(), e.getMaxY());
}
private static double avg(double a, double b) { return (a + b) / 2d; }
private static IntersectsOp intersectsOp = new IntersectsOp() {
public boolean intersects(Object aBounds, Object bBounds) {
return ((Envelope)aBounds).intersects((Envelope)bBounds);
}
};
/**
* Creates the parent level for the given child level. First, orders the items
* by the x-values of the midpoints, and groups them into vertical slices.
* For each slice, orders the items by the y-values of the midpoints, and
* group them into runs of size M (the node capacity). For each run, creates
* a new (parent) node.
*/
protected List createParentBoundables(List childBoundables, int newLevel) {
Assert.isTrue(!childBoundables.isEmpty());
int minLeafCount = (int) Math.ceil((childBoundables.size() / (double) getNodeCapacity()));
ArrayList sortedChildBoundables = new ArrayList(childBoundables);
Collections.sort(sortedChildBoundables, xComparator);
List[] verticalSlices = verticalSlices(sortedChildBoundables,
(int) Math.ceil(Math.sqrt(minLeafCount)));
return createParentBoundablesFromVerticalSlices(verticalSlices, newLevel);
}
private List createParentBoundablesFromVerticalSlices(List[] verticalSlices, int newLevel) {
Assert.isTrue(verticalSlices.length > 0);
List parentBoundables = new ArrayList();
for (int i = 0; i < verticalSlices.length; i++) {
parentBoundables.addAll(
createParentBoundablesFromVerticalSlice(verticalSlices[i], newLevel));
}
return parentBoundables;
}
protected List createParentBoundablesFromVerticalSlice(List childBoundables, int newLevel) {
return super.createParentBoundables(childBoundables, newLevel);
}
/**
* @param childBoundables Must be sorted by the x-value of the envelope midpoints
*/
protected List[] verticalSlices(List childBoundables, int sliceCount) {
int sliceCapacity = (int) Math.ceil(childBoundables.size() / (double) sliceCount);
List[] slices = new List[sliceCount];
Iterator i = childBoundables.iterator();
for (int j = 0; j < sliceCount; j++) {
slices[j] = new ArrayList();
int boundablesAddedToSlice = 0;
while (i.hasNext() && boundablesAddedToSlice < sliceCapacity) {
Boundable childBoundable = (Boundable) i.next();
slices[j].add(childBoundable);
boundablesAddedToSlice++;
}
}
return slices;
}
private static final int DEFAULT_NODE_CAPACITY = 10;
/**
* Constructs an STRtree with the default node capacity.
*/
public STRtree()
{
this(DEFAULT_NODE_CAPACITY);
}
/**
* Constructs an STRtree with the given maximum number of child nodes that
* a node may have.
*
* The minimum recommended capacity setting is 4.
*
*/
public STRtree(int nodeCapacity) {
super(nodeCapacity);
}
protected AbstractNode createNode(int level) {
return new STRtreeNode(level);
}
protected IntersectsOp getIntersectsOp() {
return intersectsOp;
}
/**
* Inserts an item having the given bounds into the tree.
*/
public void insert(Envelope itemEnv, Object item) {
if (itemEnv.isNull()) { return; }
super.insert(itemEnv, item);
}
/**
* Returns items whose bounds intersect the given envelope.
*/
public List query(Envelope searchEnv) {
//Yes this method does something. It specifies that the bounds is an
//Envelope. super.query takes an Object, not an Envelope. [Jon Aquino 10/24/2003]
return super.query(searchEnv);
}
/**
* Returns items whose bounds intersect the given envelope.
*/
public void query(Envelope searchEnv, ItemVisitor visitor) {
//Yes this method does something. It specifies that the bounds is an
//Envelope. super.query takes an Object, not an Envelope. [Jon Aquino 10/24/2003]
super.query(searchEnv, visitor);
}
/**
* Removes a single item from the tree.
*
* @param itemEnv the Envelope of the item to remove
* @param item the item to remove
* @return true if the item was found
*/
public boolean remove(Envelope itemEnv, Object item) {
return super.remove(itemEnv, item);
}
/**
* Returns the number of items in the tree.
*
* @return the number of items in the tree
*/
public int size()
{
return super.size();
}
/**
* Returns the number of items in the tree.
*
* @return the number of items in the tree
*/
public int depth()
{
return super.depth();
}
protected Comparator getComparator() {
return yComparator;
}
/**
* Finds the two nearest items in the tree,
* using {@link ItemDistance} as the distance metric.
* A Branch-and-Bound tree traversal algorithm is used
* to provide an efficient search.
*
* @param itemDist a distance metric applicable to the items in this tree
* @return the pair of the nearest items
*/
public Object[] nearestNeighbour(ItemDistance itemDist)
{
BoundablePair bp = new BoundablePair(this.getRoot(), this.getRoot(), itemDist);
return nearestNeighbour(bp);
}
/**
* Finds the item in this tree which is nearest to the given {@link Object},
* using {@link ItemDistance} as the distance metric.
* A Branch-and-Bound tree traversal algorithm is used
* to provide an efficient search.
*
* The query object does not have to be
* contained in the tree, but it does
* have to be compatible with the itemDist
* distance metric.
*
* @param env the envelope of the query item
* @param item the item to find the nearest neighbour of
* @param itemDist a distance metric applicable to the items in this tree and the query item
* @return the nearest item in this tree
*/
public Object nearestNeighbour(Envelope env, Object item, ItemDistance itemDist)
{
Boundable bnd = new ItemBoundable(env, item);
BoundablePair bp = new BoundablePair(this.getRoot(), bnd, itemDist);
return nearestNeighbour(bp)[0];
}
/**
* Finds the item in this tree which is nearest to the given {@link Object},
* using {@link ItemDistance} as the distance metric.
* A Branch-and-Bound tree traversal algorithm is used
* to provide an efficient search.
*
* The query object does not have to be
* contained in the tree, but it does
* have to be compatible with the itemDist
* distance metric.
*
* @param env the envelope of the query item
* @param item the item to find the nearest neighbour of
* @param itemDist a distance metric applicable to the items in this tree and the query item
* @param k the K nearest items in KNN
* @return the K nearest items in this tree
*/
public Object[] kNearestNeighbour(Envelope env, Object item, ItemDistance itemDist,int k)
{
Boundable bnd = new ItemBoundable(env, item);
BoundablePair bp = new BoundablePair(this.getRoot(), bnd, itemDist);
return nearestNeighbour(bp,k);
}
/**
* Finds the two nearest items from this tree
* and another tree,
* using {@link ItemDistance} as the distance metric.
* A Branch-and-Bound tree traversal algorithm is used
* to provide an efficient search.
* The result value is a pair of items,
* the first from this tree and the second
* from the argument tree.
*
* @param tree another tree
* @param itemDist a distance metric applicable to the items in the trees
* @return the pair of the nearest items, one from each tree
*/
public Object[] nearestNeighbour(STRtree tree, ItemDistance itemDist)
{
BoundablePair bp = new BoundablePair(this.getRoot(), tree.getRoot(), itemDist);
return nearestNeighbour(bp);
}
private Object[] nearestNeighbour(BoundablePair initBndPair)
{
return nearestNeighbour(initBndPair, Double.POSITIVE_INFINITY);
}
private Object[] nearestNeighbour(BoundablePair initBndPair, int k)
{
return nearestNeighbour(initBndPair, Double.POSITIVE_INFINITY,k);
}
private Object[] nearestNeighbour(BoundablePair initBndPair, double maxDistance)
{
double distanceLowerBound = maxDistance;
BoundablePair minPair = null;
// initialize internal structures
PriorityQueue priQ = new PriorityQueue();
// initialize queue
priQ.add(initBndPair);
while (! priQ.isEmpty() && distanceLowerBound > 0.0) {
// pop head of queue and expand one side of pair
BoundablePair bndPair = (BoundablePair) priQ.poll();
double currentDistance = bndPair.getDistance();
/**
* If the distance for the first node in the queue
* is >= the current minimum distance, all other nodes
* in the queue must also have a greater distance.
* So the current minDistance must be the true minimum,
* and we are done.
*/
if (currentDistance >= distanceLowerBound)
break;
/**
* If the pair members are leaves
* then their distance is the exact lower bound.
* Update the distanceLowerBound to reflect this
* (which must be smaller, due to the test
* immediately prior to this).
*/
if (bndPair.isLeaves()) {
// assert: currentDistance < minimumDistanceFound
distanceLowerBound = currentDistance;
minPair = bndPair;
}
else {
// testing - does allowing a tolerance improve speed?
// Ans: by only about 10% - not enough to matter
/*
double maxDist = bndPair.getMaximumDistance();
if (maxDist * .99 < lastComputedDistance)
return;
//*/
/**
* Otherwise, expand one side of the pair,
* (the choice of which side to expand is heuristically determined)
* and insert the new expanded pairs into the queue
*/
bndPair.expandToQueue(priQ, distanceLowerBound);
}
}
// done - return items with min distance
return new Object[] {
((ItemBoundable) minPair.getBoundable(0)).getItem(),
((ItemBoundable) minPair.getBoundable(1)).getItem()
};
}
private Object[] nearestNeighbour(BoundablePair initBndPair, double maxDistance, int k)
{
/*
* This method implements the KNN algorithm described in the following paper:
* Roussopoulos, Nick, Stephen Kelley, and Frédéric Vincent. "Nearest neighbor queries." ACM sigmod record. Vol. 24. No. 2. ACM, 1995.
* We only use the minDistance and ignore minmaxDistance.
*/
double distanceLowerBound = maxDistance;
// initialize internal structures
PriorityQueue priQ = new PriorityQueue();
// initialize queue
priQ.add(initBndPair);
java.util.PriorityQueue kNearestNeighbors = new java.util.PriorityQueue(k, new BoundablePairComparator(false));
while (! priQ.isEmpty() && distanceLowerBound >= 0.0) {
// pop head of queue and expand one side of pair
BoundablePair bndPair = (BoundablePair) priQ.poll();
double currentDistance = bndPair.getDistance();
/**
* If the distance for the first node in the queue
* is >= the current maximum distance in the k queue , all other nodes
* in the queue must also have a greater distance.
* So the current minDistance must be the true minimum,
* and we are done.
*/
if (currentDistance >= distanceLowerBound){
break;
}
/**
* If the pair members are leaves
* then their distance is the exact lower bound.
* Update the distanceLowerBound to reflect this
* (which must be smaller, due to the test
* immediately prior to this).
*/
if (bndPair.isLeaves()) {
// assert: currentDistance < minimumDistanceFound
if(kNearestNeighbors.size()currentDistance)
{
kNearestNeighbors.poll();
kNearestNeighbors.add(bndPair);
}
/*
* minDistance should be the farthest point in the K nearest neighbor queue.
*/
distanceLowerBound = kNearestNeighbors.peek().getDistance();
}
}
else {
// testing - does allowing a tolerance improve speed?
// Ans: by only about 10% - not enough to matter
/*
double maxDist = bndPair.getMaximumDistance();
if (maxDist * .99 < lastComputedDistance)
return;
//*/
/**
* Otherwise, expand one side of the pair,
* (the choice of which side to expand is heuristically determined)
* and insert the new expanded pairs into the queue
*/
bndPair.expandToQueue(priQ, distanceLowerBound);
}
}
// done - return items with min distance
Object[] result = new Object[kNearestNeighbors.size()];
Iterator resultIterator = kNearestNeighbors.iterator();
int count=0;
while(resultIterator.hasNext())
{
result[count]=((ItemBoundable)resultIterator.next().getBoundable(0)).getItem();
count++;
}
return result;
}
/**
* This method is to find the boundaries of leaf nodes.
* @return Return the list of boundaries we find.
*/
public List queryBoundary()
{
return super.queryBoundary();
}
}