/*
* GeoTools - The Open Source Java GIS Toolkit
* http://geotools.org
*
* (C) 2014, Open Source Geospatial Foundation (OSGeo)
*
* 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;
* version 2.1 of the License.
*
* 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.
*/
package org.geotools.process.spatialstatistics.autocorrelation;
import java.util.logging.Logger;
import org.geotools.data.simple.SimpleFeatureCollection;
import org.geotools.process.spatialstatistics.core.SSUtils;
import org.geotools.process.spatialstatistics.core.SSUtils.StatEnum;
import org.geotools.process.spatialstatistics.core.SpatialEvent;
import org.geotools.process.spatialstatistics.core.WeightMatrixBuilder;
import org.geotools.process.spatialstatistics.enumeration.DistanceMethod;
import org.geotools.process.spatialstatistics.enumeration.SpatialConcept;
import org.geotools.process.spatialstatistics.enumeration.StandardizationMethod;
import org.geotools.util.logging.Logging;
/**
* Measures spatial autocorrelation based on feature locations and attribute values using the Global Geary's C statistic.
*
* @reference http://www.passagesoftware.net/webhelp/Geary_s_c.htm
*
* @author Minpa Lee, MangoSystem
*
* @source $URL$
*/
public class GlobalGearysCOperation extends AbstractStatisticsOperation {
protected static final Logger LOGGER = Logging.getLogger(GlobalGearysCOperation.class);
public GlobalGearysCOperation() {
// Default Setting
this.setDistanceType(DistanceMethod.Euclidean);
this.setSpatialConceptType(SpatialConcept.InverseDistance);
this.setStandardizationType(StandardizationMethod.None);
}
public GearysC execute(SimpleFeatureCollection inputFeatures, String inputField) {
swMatrix = new WeightMatrixBuilder(getSpatialConceptType(), getStandardizationType());
swMatrix.setDistanceMethod(getDistanceType());
swMatrix.setDistanceBandWidth(getDistanceBand());
swMatrix.setSelfNeighbors(isSelfNeighbors());
swMatrix.buildWeightMatrix(inputFeatures, inputField);
double cijWSum = 0.0;
double wijSum = 0.0; // sum of all weights (wij)
double m2 = 0.0;
double m4 = 0.0;
double sumS1 = 0.0;
double sumS2 = 0.0;
double n = swMatrix.getEvents().size();
double meanX = swMatrix.sumX / n;
for (SpatialEvent source : swMatrix.getEvents()) {
double jwijSum = 0.0;
double jwjiSum = 0.0;
double zi = source.xVal - meanX;
m2 += Math.pow(zi, 2.0);
m4 += Math.pow(zi, 4.0);
for (SpatialEvent target : swMatrix.getEvents()) {
if (!isSelfNeighbors() && source.id == target.id) {
continue;
}
double wij = swMatrix.getWeight(source, target);
double wji = wij;
wij = swMatrix.standardizeWeight(source, wij);
wji = swMatrix.standardizeWeight(target, wji);
if (wij == 0) {
continue;
}
double cij = Math.pow(source.xVal - target.xVal, 2.0);
cijWSum += wij * cij;
wijSum += wij;
jwijSum += wij;
jwjiSum += wji;
sumS1 += Math.pow(wij + wji, 2.0);
}
sumS2 += Math.pow(jwijSum + jwjiSum, 2.0);
}
double dS1 = 0.5 * sumS1;
m2 = m2 / (n - 1.0);
m4 = m4 / (n - 1.0);
// Geary’s c ranges from zero to infinity, with an expected value of 1 under no autocorrelation.
// Values from zero to one indicate positive spatial autocorrelation,
// values above 1 negative spatial autocorrelation.
double b2 = m4 / (m2 * m2);
double dExpected = 1.0;
if (wijSum <= 0.0) {
GearysC gearysC = new GearysC(0d, dExpected, 0d);
gearysC.setConceptualization(getSpatialConceptType());
gearysC.setDistanceMethod(getDistanceType());
gearysC.setRowStandardization(getStandardizationType());
gearysC.setDistanceThreshold(swMatrix.getDistanceBandWidth());
return gearysC;
}
double dObserved = cijWSum / (2.0 * m2 * wijSum);
// variance of c
double W2 = Math.pow(wijSum, 2.0);
double n2 = Math.pow(n, 2.0);
double div = n * (n - 2.0) * (n - 3.0);
double A = ((n - 1) * dS1 * (n2 - (3.0 * n) + 3.0 - ((n - 1) * b2))) / (div * W2);
double B = ((n - 1) * sumS2 * (n2 + (3.0 * n) - 6.0 - ((n2 - n + 2) * b2)))
/ (4.0 * div * W2);
double C = (n2 - 3.0 - (Math.pow(n - 1, 2.0) * b2)) / div;
double zVariance = A - B + C;
// finally build result
GearysC gearysC = new GearysC(dObserved, dExpected, zVariance);
gearysC.setConceptualization(getSpatialConceptType());
gearysC.setDistanceMethod(getDistanceType());
gearysC.setRowStandardization(getStandardizationType());
gearysC.setDistanceThreshold(swMatrix.getDistanceBandWidth());
return gearysC;
}
public static final class GearysC {
double observedIndex = 0.0;
double expectedIndex = 0.0;
double zVariance = 0.0;
SpatialConcept conceptualization = SpatialConcept.InverseDistance;
DistanceMethod distanceMethod = DistanceMethod.Euclidean;
StandardizationMethod rowStandardization = StandardizationMethod.None;
double distanceThreshold = 0.0;
public GearysC() {
}
public GearysC(double observedIndex, double expectedIndex, double zVariance) {
setObservedIndex(observedIndex);
setExpectedIndex(expectedIndex);
setZVariance(zVariance);
}
public void setObservedIndex(double observedIndex) {
this.observedIndex = observedIndex;
}
public double getObservedIndex() {
return observedIndex;
}
public void setExpectedIndex(double expectedIndex) {
this.expectedIndex = expectedIndex;
}
public double getExpectedIndex() {
return expectedIndex;
}
public void setZVariance(double zVariance) {
this.zVariance = zVariance;
}
public double getZVariance() {
return zVariance;
}
public double getZScore() {
double dX = observedIndex - expectedIndex;
double dY = Math.pow(getZVariance(), 0.5);
if (dY == 0)
return 0.0;
return dX / dY;
}
public double getPValue() {
return SSUtils.zProb(this.getZScore(), StatEnum.BOTH);
}
public SpatialConcept getConceptualization() {
return conceptualization;
}
public void setConceptualization(SpatialConcept conceptualization) {
this.conceptualization = conceptualization;
}
public DistanceMethod getDistanceMethod() {
return distanceMethod;
}
public void setDistanceMethod(DistanceMethod distanceMethod) {
this.distanceMethod = distanceMethod;
}
public StandardizationMethod getRowStandardization() {
return rowStandardization;
}
public void setRowStandardization(StandardizationMethod rowStandardization) {
this.rowStandardization = rowStandardization;
}
public double getDistanceThreshold() {
return distanceThreshold;
}
public void setDistanceThreshold(double distanceThreshold) {
this.distanceThreshold = distanceThreshold;
}
}
}