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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  def apply(pts: Array[Coordinate], geomExtent: Extent, ld: LayoutDefinition, tileCols: Int, tileRows: Int, cellType: CellType = DoubleConstantNoDataCellType)(implicit sc: SparkContext): RDD[(SpatialKey, Tile)]

   Generates a Euclidean distance tile RDD from a small collection of points.

   Generates a Euclidean distance tile RDD from a small collection of points.

   The standard EuclideanDistance object apply method is meant to build Euclidean distance tiles from a very large, dense set of points where the generation of the triangulation itself is a significant performance bottleneck (i.e., when the point set is 10s or 100s of millions strong or more). In the event that one wishes to generate a set of Euclidean distance tiles over a large area from a small or sparse set of points which would exhibit artifacts under the assumptions employed by the dense EuclideanDistance operator, this apply method will accomplish the desired end. Be aware, however, that if there are many points, this operation will have a lengthy runtime.

5.  final def asInstanceOf[T0]: T0

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6.  def clone(): AnyRef

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7.  final def eq(arg0: AnyRef): Boolean

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8.  def equals(arg0: Any): Boolean

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9.  def finalize(): Unit

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10.  final def getClass(): Class[_]

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11.  def hashCode(): Int

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12.  final def isInstanceOf[T0]: Boolean

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13.  final def ne(arg0: AnyRef): Boolean

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14.  final def notify(): Unit

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15.  final def notifyAll(): Unit

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16.  final def synchronized[T0](arg0: ⇒ T0): T0

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17.  def toString(): String

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18.  final def wait(): Unit

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19.  final def wait(arg0: Long, arg1: Int): Unit

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20.  final def wait(arg0: Long): Unit

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