Sedona (GeoSpark) 空间数据可视化过程不太复杂，主要是每个空间对象向对应栅格空间的映射，和矢量转栅格类似。直接上代码：
// 已经通过Sedona创建了类型为LineStringRDD的slineRDD对象
import org.apache.sedona.viz.core.ImageGenerator
import org.apache.sedona.viz.extension.visualizationEffect.ScatterPlot
import org.apache.sedona.viz.utils.ImageType
import java.awt.Color
// 创建新的对象
var visualizationOperator = new ScatterPlot(10000, 10000, slineRDD.boundaryEnvelope, false)
visualizationOperator.CustomizeColor(255, 255, 255, 255, Color.blue, true)
// 具体的可视化过程实现
visualizationOperator.Visualize(sc, slineRDD)
// 将结果保存为PNG图像
var imageGenerator = new ImageGenerator
imageGenerator.SaveRasterImageAsLocalFile(visualizationOperator.rasterImage, "/home/sparkl/visual", ImageType.PNG)其中具体的可视化操作在visualizationOperator.Visualize(sc, slineRDD)函数中，分为visualize、colorize和renderImage三步，具体操作如下：
public boolean Visualize(JavaSparkContext sparkContext, SpatialRDD spatialRDD)
throws Exception
{
// 返回值为JavaPairRDD<Pixel, Double>
// 栅格图中的每个像素对应一个Pixel，包含x、y信息，Double和属性值相关
this.Rasterize(sparkContext, spatialRDD, true);
// 修改value的值，通过value值赋颜色
this.Colorize();
this.RenderImage(sparkContext);
return true;
}Rasterize
Rasterize函数是最主要的矢量向栅格的转换函数，先通过flatMapToPair算子计算每个空间对象对应的像素位置和像素值，然后filter掉超出范围的像素值。Sedona的实现中value没有考虑空间实体的属性，每个像素点的value直接赋值的1.0。
protected JavaPairRDD<Pixel, Double> Rasterize(JavaSparkContext sparkContext,
SpatialRDD spatialRDD, boolean useSparkDefaultPartition)
{
JavaRDD<Object> rawSpatialRDD = spatialRDD.rawSpatialRDD;
// rawSpatialRDD中包含对应的空间对象，通过flatMapToPair算子计算每个空间对象对应的Pixel，得到<Pixel, Double> 类型的键值对
JavaPairRDD<Pixel, Double> spatialRDDwithPixelId = rawSpatialRDD.flatMapToPair(new PairFlatMapFunction<Object, Pixel, Double>(){
@Override
public Iterator<Tuple2<Pixel, Double>> call(Object spatialObject) throws Exception {
// 分不同的数据类型进行处理
if (spatialObject instanceof Point) {
return RasterizationUtils.FindPixelCoordinates(resolutionX, resolutionY, datasetBoundary, (Point) spatialObject, colorizeOption, reverseSpatialCoordinate).iterator();
}
else if (spatialObject instanceof Polygon) {
return RasterizationUtils.FindPixelCoordinates(resolutionX, resolutionY, datasetBoundary, (Polygon) spatialObject, reverseSpatialCoordinate).iterator();
}
else if (spatialObject instanceof LineString) {
// 计算每个空间对象对应栅格图中的<像素位置,像素值>，会得到一个像素值列表
return RasterizationUtils.FindPixelCoordinates(resolutionX, resolutionY, datasetBoundary, (LineString) spatialObject, reverseSpatialCoordinate).iterator();
}
else {
// 只支持上面的三种数据类型
throw new Exception("[Sedona-VizViz][Rasterize] Unsupported spatial object types. Sedona-VizViz only supports Point, Polygon, LineString");
}
}
});
// 去除不在范围内的点（这个在上一步直接处理比较合适？）
spatialRDDwithPixelId = spatialRDDwithPixelId.filter(new Function<Tuple2<Pixel, Double>, Boolean>()
{
@Override
public Boolean call(Tuple2<Pixel, Double> pixelCount)
throws Exception
{
return !(pixelCount._1().getX() < 0) && !(pixelCount._1().getX() > resolutionX) && !(pixelCount._1().getY() < 0) && !(pixelCount._1().getY() > resolutionY);
}
});
this.distributedRasterCountMatrix = spatialRDDwithPixelId;
return this.distributedRasterCountMatrix;
}像素值的计算将LineString拆分为两个点构成的线段进行计算，先计算每个点在栅格图中的位置，然后计算两个栅格图坐标练成线段时中间穿过的点。
public static List<Tuple2<Pixel, Double>> FindPixelCoordinates(int resolutionX, int resolutionY, Envelope datasetBoundary, LineString spatialObject, boolean reverseSpatialCoordinate) {
List<Tuple2<Pixel, Double>> result = new ArrayList<Tuple2<Pixel, Double>>();
for (int i = 0; i < spatialObject.getCoordinates().length - 1; i++) {
Tuple2<Integer, Integer> pixelCoordinate1 = null;
Tuple2<Integer, Integer> pixelCoordinate2 = null;
try {
// 两个点在栅格图中的位置
pixelCoordinate1 = FindOnePixelCoordinate(resolutionX, resolutionY, datasetBoundary, spatialObject.getCoordinates()[i], reverseSpatialCoordinate);
pixelCoordinate2 = FindOnePixelCoordinate(resolutionX, resolutionY, datasetBoundary, spatialObject.getCoordinates()[i + 1], reverseSpatialCoordinate);
}
catch (Exception e) {
// This line segment is out of boundary, Should be ignored.
continue;
}
// 通过两个栅格图的坐标位置计算连成线段后经过的其它栅格点，并将结果加入到result
result.addAll(FindPixelCoordinates(resolutionX, resolutionY, pixelCoordinate1, pixelCoordinate2, reverseSpatialCoordinate));
}
return result;
}
// 计算点在栅格图像中的位置
public static Tuple2<Integer, Integer> FindOnePixelCoordinate(int resolutionX, int resolutionY, Envelope datasetBoundaryOriginal, Coordinate spatialCoordinateOriginal, boolean reverseSpatialCoordinate) {
Coordinate spatialCoordinate;
Envelope datasetBoundary;
if (reverseSpatialCoordinate) {
spatialCoordinate = new Coordinate(spatialCoordinateOriginal.y, spatialCoordinateOriginal.x);
datasetBoundary = new Envelope(datasetBoundaryOriginal.getMinY(), datasetBoundaryOriginal.getMaxY(), datasetBoundaryOriginal.getMinX(), datasetBoundaryOriginal.getMaxX());
}
else {
spatialCoordinate = spatialCoordinateOriginal;
datasetBoundary = datasetBoundaryOriginal;
}
Double pixelXDouble = ((spatialCoordinate.x - datasetBoundary.getMinX()) / (datasetBoundary.getMaxX() - datasetBoundary.getMinX())) * resolutionX;
Double xRemainder = (spatialCoordinate.x - datasetBoundary.getMinX()) % (datasetBoundary.getMaxX() - datasetBoundary.getMinX());
Double pixelYDouble = ((spatialCoordinate.y - datasetBoundary.getMinY()) / (datasetBoundary.getMaxY() - datasetBoundary.getMinY())) * resolutionY;
Double yRemainder = (spatialCoordinate.y - datasetBoundary.getMinY()) % (datasetBoundary.getMaxY() - datasetBoundary.getMinY());
int pixelX = pixelXDouble.intValue();
int pixelY = pixelYDouble.intValue();
if (xRemainder == 0.0 && pixelXDouble != 0.0) {
pixelX--;
}
if (pixelX >= resolutionX) {
pixelX--;
}
if (yRemainder == 0.0 && pixelYDouble != 0) {
pixelY--;
}
if (pixelY >= resolutionY) {
pixelY--;
}
return new Tuple2<Integer, Integer>(pixelX, pixelY);
}
// 通过两个栅格图上的点，计算连线线段经过的点
public static List<Tuple2<Pixel, Double>> FindPixelCoordinates(int resolutionX, int resolutionY, Tuple2<Integer, Integer> pixelCoordinate1, Tuple2<Integer, Integer> pixelCoordinate2, boolean reverseSpatialCoordinate) {
// This function uses Bresenham's line algorithm to plot pixels touched by a given line segment.
int x1 = pixelCoordinate1._1;
int y1 = pixelCoordinate1._2;
int x2 = pixelCoordinate2._1;
int y2 = pixelCoordinate2._2;
int dx = x2 - x1;
int dy = y2 - y1;
int ux = dx > 0 ? 1 : -1; // x direction
int uy = dy > 0 ? 1 : -1; // y direction
int x = x1, y = y1;
int eps = 0; //cumulative errors
dx = Math.abs(dx);
dy = Math.abs(dy);
List<Tuple2<Pixel, Double>> result = new ArrayList<Tuple2<Pixel, Double>>();
if (dx > dy) {
for (x = x1; x != x2; x += ux) {
try {
Pixel newPixel = new Pixel(x, y, resolutionX, resolutionY);
result.add(new Tuple2<Pixel, Double>(newPixel, 1.0));
}
catch (Exception e) {
// This spatial object is out of the given dataset boudanry. It is ignored here.
}
eps += dy;
if ((eps << 1) >= dx) { // x值每次+1，y值在eps/2 > dx时+1
y += uy;
eps -= dx;
}
}
}
else {
for (y = y1; y != y2; y += uy) {
try {
Pixel newPixel = new Pixel(x, y, resolutionX, resolutionY);
result.add(new Tuple2<Pixel, Double>(newPixel, 1.0));
}
catch (Exception e) {
// This spatial object is out of the given dataset boudanry. It is ignored here.
}
eps += dx;
if ((eps << 1) >= dy) {
x += ux;
eps -= dy;
}
}
}
return result;
}colorize
上面的Visualize函数得到了一个distributedRasterCountMatrix的RDD，key为Pixel像素位置，value为表示点数量的Double类型（感觉这个Double类型可以直接用Integer）。colorize使用了一次mapValues操作，将value值归一化后赋值为Integer类型的颜色值。
this.distributedRasterColorMatrix = this.distributedRasterCountMatrix.mapValues(new Function<Double, Integer>()
{
@Override
public Integer call(Double pixelCount)
throws Exception
{
Double currentPixelCount = pixelCount;
if (currentPixelCount > maxWeight) {
currentPixelCount = maxWeight;
}
Double normalizedPixelCount = (currentPixelCount - minWeight) * 255 / (maxWeight - minWeight);
Integer pixelColor = EncodeToRGB(normalizedPixelCount.intValue());
return pixelColor;
}
});
//logger.debug("[Sedona-VizViz][Colorize]output count "+this.distributedRasterColorMatrix.count());
logger.info("[Sedona-VizViz][Colorize][Stop]");
return true;RenderImage
RenderImage函数将上一步中得到的<Pixel, Integer>键值对转换成图像，以ImageSerializableWrapper的形式得到结果。函数中parallelRenderImage参数默认为false，直接对每个partition并行处理得到完整的图像，得到<Integer, ImageSerializablewrapper>结果，其中Integer都为0，然后ReduceByKey将所有的图像叠加得到结果。
ParalleRenderImage选项为true时，首先对上一步的distributedRasterColorMatrix进行空间分区，使每个partition对应结果图像中的一部分，并行处理各自对应的区域。得到的结果存储在distributedRasterImage。
parallelRenderImage参数为false的方法的shuffle过程将每个分区产生的完整结果图像传送到同一个节点后叠加计算，当结果图像较大时会造成大量shuffle IO。参数为true时，shuffle过程主要来自对原始数据的空间划分，对空间划分的效果有较高的要求。
protected boolean RenderImage(JavaSparkContext sparkContext) throws Exception {
if (this.parallelRenderImage == true) {
if (this.hasBeenSpatialPartitioned == false) {
this.spatialPartitioningWithoutDuplicates();
this.hasBeenSpatialPartitioned = true;
}
this.distributedRasterImage = this.distributedRasterColorMatrix.mapPartitionsToPair(
new PairFlatMapFunction<Iterator<Tuple2<Pixel, Integer>>, Integer, ImageSerializableWrapper>()
{
@Override
public Iterator<Tuple2<Integer, ImageSerializableWrapper>> call(Iterator<Tuple2<Pixel, Integer>> currentPartition)
throws Exception
{
BufferedImage imagePartition = new BufferedImage(partitionIntervalX, partitionIntervalY, BufferedImage.TYPE_INT_ARGB);
Tuple2<Pixel, Integer> pixelColor = null;
while (currentPartition.hasNext()) {
//Render color in this image partition pixel-wise.
pixelColor = currentPartition.next();
if (pixelColor._1().getX() < 0 || pixelColor._1().getX() >= resolutionX || pixelColor._1().getY() < 0 || pixelColor._1().getY() >= resolutionY) {
pixelColor = null;
continue;
}
imagePartition.setRGB((int) pixelColor._1().getX() % partitionIntervalX, (partitionIntervalY - 1) - (int) pixelColor._1().getY() % partitionIntervalY, pixelColor._2);
}
List<Tuple2<Integer, ImageSerializableWrapper>> result = new ArrayList<Tuple2<Integer, ImageSerializableWrapper>>();
if (pixelColor == null) {
// No pixels in this partition. Skip this subimage
return result.iterator();
}
logger.info("[Sedona-VizViz][Render]add a image partition into result set " + pixelColor._1().getCurrentPartitionId());
result.add(new Tuple2<Integer, ImageSerializableWrapper>(pixelColor._1().getCurrentPartitionId(), new ImageSerializableWrapper(imagePartition)));
return result.iterator();
}
});
}
else if (this.parallelRenderImage == false) {
// Draw full size image in parallel
this.distributedRasterImage = this.distributedRasterColorMatrix.mapPartitionsToPair(
new PairFlatMapFunction<Iterator<Tuple2<Pixel, Integer>>, Integer, ImageSerializableWrapper>()
{
@Override
public Iterator<Tuple2<Integer, ImageSerializableWrapper>> call(Iterator<Tuple2<Pixel, Integer>> currentPartition)
throws Exception
{
BufferedImage imagePartition = new BufferedImage(resolutionX, resolutionY, BufferedImage.TYPE_INT_ARGB);
Tuple2<Pixel, Integer> pixelColor = null;
while (currentPartition.hasNext()) {
//Render color in this image partition pixel-wise.
pixelColor = currentPartition.next();
if (pixelColor._1().getX() < 0 || pixelColor._1().getX() >= resolutionX || pixelColor._1().getY() < 0 || pixelColor._1().getY() >= resolutionY) {
pixelColor = null;
continue;
}
imagePartition.setRGB((int) pixelColor._1().getX(), (resolutionY - 1) - (int) pixelColor._1().getY(), pixelColor._2);
}
List<Tuple2<Integer, ImageSerializableWrapper>> result = new ArrayList<Tuple2<Integer, ImageSerializableWrapper>>();
if (pixelColor == null) {
// No pixels in this partition. Skip this subimage
return result.iterator();
}
result.add(new Tuple2<Integer, ImageSerializableWrapper>(1, new ImageSerializableWrapper(imagePartition)));
return result.iterator();
}
});
// Merge images together using reduce
this.distributedRasterImage = this.distributedRasterImage.reduceByKey(new Function2<ImageSerializableWrapper, ImageSerializableWrapper, ImageSerializableWrapper>()
{
@Override
public ImageSerializableWrapper call(ImageSerializableWrapper image1, ImageSerializableWrapper image2)
throws Exception
{
// The combined image should be a full size image
BufferedImage combinedImage = new BufferedImage(resolutionX, resolutionY, BufferedImage.TYPE_INT_ARGB);
Graphics graphics = combinedImage.getGraphics();
graphics.drawImage(image1.image, 0, 0, null);
graphics.drawImage(image2.image, 0, 0, null);
return new ImageSerializableWrapper(combinedImage);
}
});
List<Tuple2<Integer, ImageSerializableWrapper>> imageList = this.distributedRasterImage.collect();
this.rasterImage = imageList.get(0)._2().image;
}
logger.info("[Sedona-VizViz][RenderImage][Stop]");
return true;
}
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