最近项目原因需要用到克里金和等直线算法 ，在网上找了许久都不太满意，不是话说到一半就是 示例不正确，要不就是没库等等，捣鼓许久弄出来，把示例代码发出来分享一下。

首先了说明一下为什么要使用到克里金算法 因为气象方面很多气象站点零散的分布在地图各个位置，在这零散的位置上就可能会产生区域覆盖不完整的情况，这个时候就需要做插值覆盖。而等直线的意义跟插值业务一样，因为在地图上热力图的绘制需要大量格点数据来支撑绘制所以，就会出现在绘制热力图的时候光是下载格点数据都是很大的，这个取决于格点密度，密度越大那么数据量就会很大，在网络传输过程中光是下载这个数据都需要花费很多时间，更别说绘制了。所以等值线数据就尤为的重要。例如：

根据区域划分成勾勒出线的坐标，其内的值都是等值范围这样的数据就不会很多，当然等值范围越精确数据也会递增。

这次示例使用到的库wContour和geo，因为代码不复杂 加上代码也有注释，所以我就不细致解释下去了直接上demo

package kriginDemo.test;

import java.io.BufferedReader;

import java.io.File;

import java.io.FileInputStream;

import java.io.IOException;

import java.io.InputStreamReader;

import java.util.ArrayList;

import java.util.List;

import org.json.simple.JSONObject;

import com.alibaba.fastjson.JSON;

import krigingDemo.util.CommonMethod;

import krigingDemo.util.KrigingUtil;

import wContour.Contour;

import wContour.Interpolate;

import wContour.Global.Border;

import wContour.Global.PointD;

import wContour.Global.PolyLine;

import wContour.Global.Polygon;

public class Test {

/**

* 通过本地文件访问json并读取

*

* @param path：json文件路径

* @return：json文件的内容

*/

public static String readFile(String path) {

StringBuffer laststr = new StringBuffer();

File file = new File(path);// 打开文件

BufferedReader reader = null;

try {

FileInputStream in = new FileInputStream(file);

reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));// 读取文件

String tempString = null;

while ((tempString = reader.readLine()) != null) {

laststr = laststr.append(tempString);

}

reader.close();

} catch (IOException e) {

e.printStackTrace();

} finally {

if (reader != null) {

try {

reader.close();

} catch (IOException el) {

}

}

}

return laststr.toString();

}

private static String getPolygonGeoJson(List cPolygonList) {

String geo = null;

String geometry = " { \"type\":\"Feature\",\"geometry\":";

String properties = ",\"properties\":{ \"hvalue\":";

String head = "{\"type\": \"FeatureCollection\"," + "\"features\": [";

String end = " ] }";

if (cPolygonList == null || cPolygonList.size() == 0) {

return null;

}

try {

for (Polygon pPolygon : cPolygonList) {

List ptsTotal = new ArrayList();

List pts = new ArrayList();

PolyLine pline = pPolygon.OutLine;

for (PointD ptD : pline.PointList) {

List pt = new ArrayList();

pt.add(ptD.X);

pt.add(ptD.Y);

pts.add(pt);

}

ptsTotal.add(pts);

if (pPolygon.HasHoles()) {

for (PolyLine cptLine : pPolygon.HoleLines) {

List cpts = new ArrayList();

for (PointD ccptD : cptLine.PointList) {

List pt = new ArrayList();

pt.add(ccptD.X);

pt.add(ccptD.Y);

cpts.add(pt);

}

if (cpts.size() > 0) {

ptsTotal.add(cpts);

}

}

}

JSONObject js = new JSONObject();

js.put("type", "Polygon");

js.put("coordinates", ptsTotal);

double hv = pPolygon.HighValue;

double lv = pPolygon.LowValue;

if (hv == lv) {

if (pPolygon.IsClockWise) {

if (!pPolygon.IsHighCenter) {

hv = hv - 0.1;

lv = lv - 0.1;

}

} else {

if (!pPolygon.IsHighCenter) {

hv = hv - 0.1;

lv = lv - 0.1;

}

}

} else {

if (!pPolygon.IsClockWise) {

lv = lv + 0.1;

} else {

if (pPolygon.IsHighCenter) {

hv = hv - 0.1;

}

}

}

geo = geometry + js.toString() + properties + hv

+ ", \"lvalue\":" + lv + "} }" + "," + geo;

}

if (geo.contains(",")) {

geo = geo.substring(0, geo.lastIndexOf(","));

}

geo = head + geo + end;

} catch (Exception e) {

e.printStackTrace();

return geo;

}

return geo;

}

public static void main(String[] args) {

//雅安样本数据

double[][] trainData = JSON.parseObject(readFile("G:\\t.json"), double[][].class);

KrigingUtil kriging = new KrigingUtil(KrigingUtil.SPHERICAL_MODEL, 0, 100);

kriging.train(trainData[2], trainData[0], trainData[1]);

//经度起点

double left = 101.91;

//经度结束点

double right =103.395;

//维度起点

double bottom = 28.845;

//维度结束点

double top = 30.935;

double[] bottomLeft = {left, bottom};

double[] topLeft = {left, top};

double[] topRight = {right, top};

double[] bottomRight = {right, bottom};

double[][][] polygons = {{bottomLeft, topLeft, topRight, bottomRight}};

//计算经纬度格点距离

double xWidth = Math.abs(right - left) / 297;

double yWidth = Math.abs(top - bottom) / 418;

//克里金插值

KrigingUtil.Grid grid = kriging.grid(polygons, xWidth, yWidth);

//等值线阈值

double[] dataInterval = new double[]{10,20,25,30,35};

//异常数据样本

double _undefData = -9999.0;

double[] _X = new double[420];

double[] _Y = new double[298];

Interpolate.createGridXY_Num(101.91,28.845,103.395,30.935, _X, _Y);

int[][] S1 = new int[grid.A.length][grid.A[0].length];

//绘线

List _borders = Contour.tracingBorders(grid.A, _X, _Y,S1, _undefData);

List cPolylineList = new ArrayList();

cPolylineList = Contour.tracingContourLines(grid.A, _X, _Y, dataInterval.length, dataInterval, _undefData, _borders, S1);// 生成等值线

cPolylineList = Contour.smoothLines(cPolylineList);// 平滑

List cPolygonList = new ArrayList();

cPolygonList = Contour.tracingPolygons(grid.A, cPolylineList,_borders, dataInterval);

String geojsonpogylon = getPolygonGeoJson(cPolygonList);

CommonMethod cm = new CommonMethod();

cm.append2File("G:\\data.json",geojsonpogylon);

}

}

double[][] trainData = JSON.parseObject(readFile("G:\\t.json"), double[][].class);

这个数据是我的样本数据

//计算经纬度格点距离

double xWidth = Math.abs(right - left) / 297;

double yWidth = Math.abs(top - bottom) / 418;

这个297和418 是我自己计算的值意思是从开始经纬度 和结束经纬度 以0.005为格点距离计算出来的数值

CommonMethod cm = new CommonMethod();

cm.append2File("G:\\data.json",geojsonpogylon);

最后将JSON 输出到文件 因为数据也是比较大的

来源：oschina

链接：https://my.oschina.net/umbrellall1/blog/4256500