Geotrellis，获取多边形网格Rdd中的点。

Question

我需要计算多边形网格中点的平均值。

就像基于条件Polyogon.contains(点)的连接1到多个

//In:
val pointValueRdd : RDD[Feature[Point,Double]] 

val squareGridRdd : RDD[Feature[Polygon]]

//Needed Out:

val squareGridRdd : RDD[Feature[Polygon,(accum:Double,count:Int)]]
//or
val squareGridRdd : RDD[Feature[Polygon,average:Double]]

是否可以使用四叉树索引？

我读到:剪辑到网格，但我不知道，如果它是正确的工具。

http://geotrellis.readthedocs.io/en/latest/guide/vectors.html#cliptogrid

下一张图像以蓝色显示网格，以及点

​

​

一些我们欢迎的建议

Answer 1

如果您的多边形网格可以在LayoutDefinition中表示为GeoTrellis，那么有一种简单的方法可以实现这一点。LayoutDefinition定义了GeoTrellis层用来表示大量栅格瓷砖集合的瓷砖网格。它还可以用于执行网格空间中的网格键(SpatialKey)和映射空间中的边界框(Extents)之间的转换。

我不会假设您可以用LayoutDefinition来表示网格，而是展示一个解决更一般情况的示例。如果您可以用LayoutDefinition表示多边形网格，那么这种方法将更加简单。然而，这里有一个更通用方法的代码片段。这是编译的，但没有测试，所以如果你发现它的问题，请告诉我。我们在doc-examples项目中的示例中包含了这个PR：https://github.com/locationtech/geotrellis/pull/2489

import geotrellis.raster._
import geotrellis.spark._
import geotrellis.spark.tiling._
import geotrellis.vector._

import org.apache.spark.HashPartitioner
import org.apache.spark.rdd.RDD

import java.util.UUID

// see https://stackoverflow.com/questions/47359243/geotrellis-get-the-points-that-fall-in-a-polygon-grid-rdd

val pointValueRdd : RDD[Feature[Point,Double]] = ???
val squareGridRdd : RDD[Polygon] = ???

// Since we'll be grouping by key and then joining, it's work defining a partitioner
// up front.
val partitioner = new HashPartitioner(100)

// First, we'll determine the bounds of the Polygon grid
// and the average height and width, to create a GridExtent
val (extent, totalHeight, totalWidth, totalCount) =
  squareGridRdd
    .map { poly =>
      val e = poly.envelope
      (e, e.height, e.width, 1)
    }
    .reduce { case ((extent1, height1, width1, count1), (extent2, height2, width2, count2)) =>
      (extent1.combine(extent2), height1 + height2, width1 + width2, count1 + count2)
    }

val gridExtent = GridExtent(extent, totalHeight / totalCount, totalWidth / totalCount)

// We then use this for to construct a LayoutDefinition, that represents "tiles"
// that are 1x1.
val layoutDefinition = LayoutDefinition(gridExtent, tileCols = 1, tileRows = 1)

// Now that we have a layout, we can cut the polygons up per SpatialKey, as well as
// assign points to a SpatialKey, using ClipToGrid

// In order to keep track of which polygons go where, we'll assign a UUID to each
// polygon, so that they can be reconstructed. If we were dealing with PolygonFeatures,
// we could store the feature data as well. If those features already had IDs, we could
// also just use those IDs instead of UUIDs.
// We'll also store the original polygon, as they are not too big and it makes
// the reconstruction process (which might be prone to floating point errors) a little
// easier. For more complex polygons this might not be the most performant strategy.
// We then group by key to produce a set of polygons that intersect each key.
val cutPolygons: RDD[(SpatialKey, Iterable[Feature[Geometry, (Polygon, UUID)]])] =
  squareGridRdd
    .map { poly => Feature(poly, (poly, UUID.randomUUID)) }
    .clipToGrid(layoutDefinition)
    .groupByKey(partitioner)

// While we could also use clipToGrid for the points, we can
// simply use the mapTransform on the layout to determien what SpatialKey each
// point should be assigned to.
// We also group this by key to produce the set of points that intersect the key.
val pointsToKeys: RDD[(SpatialKey, Iterable[PointFeature[Double]])] =
  pointValueRdd
    .map { pointFeature =>
      (layoutDefinition.mapTransform.pointToKey(pointFeature.geom), pointFeature)
    }
    .groupByKey(partitioner)

// Now we can join those two RDDs and perform our point in polygon tests.
// Use a left outer join so that polygons with no points can be recorded.
// Once we have the point information, we key the RDD by the UUID and
// reduce the results.
val result: RDD[Feature[Polygon, Double]] =
  cutPolygons
    .leftOuterJoin(pointsToKeys)
    .flatMap { case (_, (polyFeatures, pointsOption)) =>
      pointsOption match {
        case Some(points) =>
          for(
            Feature(geom, (poly, uuid)) <- polyFeatures;
            Feature(point, value) <- points if geom.intersects(point)
          ) yield {
            (uuid, Feature(poly, (value, 1)))
          }
        case None =>
          for(Feature(geom, (poly, uuid)) <- polyFeatures) yield {
            (uuid, Feature(poly, (0.0, 0)))
          }
      }
    }
    .reduceByKey { case (Feature(poly1, (accum1, count1)), Feature(poly2, (accum2, count2))) =>
      Feature(poly1, (accum1 + accum2, count1 + count2))
    }
    .map { case (_, feature) =>
      // We no longer need the UUID; also compute the mean
      feature.mapData { case (acc, c) => acc / c }
    }