GeoSpark---ST_Area的使用

GeoSpark计算某个区域的面积：

测试数据如下：

10.5,32.11,30.50,60.21,33.50,60.21,10.5,32.11,china1
9.51,30.11,32.50,62.21,34.50,62.21,9.51,30.11,china2
11.5,32.11,31.50,64.21,33.50,64.21,11.5,32.11,china3
10.5,31.16,32.51,63.21,35.51,63.21,10.5,31.16,china4
11.5,32.11,30.50,59.21,33.50,59.21,11.5,32.11,china5

测试代码如下：

package txt_demo

import GeoSpark.geoRangeQueryForPolygon.createPolygonRDD
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.sql.SparkSession
import org.datasyslab.geospark.enums.FileDataSplitter
import org.datasyslab.geospark.serde.GeoSparkKryoRegistrator
import org.datasyslab.geospark.spatialRDD.PolygonRDD
import org.datasyslab.geosparksql.utils.{Adapter, GeoSparkSQLRegistrator}
import org.datasyslab.geosparkviz.core.Serde.GeoSparkVizKryoRegistrator

object area_demo {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().
      setAppName("SpatialRangeQueryApp").setMaster("local[*]").
      set("spark.serializer", classOf[KryoSerializer].getName).
      set("spark.kryo.registrator", classOf[GeoSparkKryoRegistrator].getName)
    implicit val sc = new SparkContext(conf)

    var sparkSession = SparkSession.builder()
      .master("local[*]") // Delete this if run in cluster mode
      .appName("readTestScala") // Change this to a proper name
      // Enable GeoSpark custom Kryo serializer
      .config("spark.serializer", classOf[KryoSerializer].getName)
      .config("spark.kryo.registrator", classOf[GeoSparkKryoRegistrator].getName)
      .config("spark.serializer", classOf[KryoSerializer].getName)
      .config("spark.kryo.registrator", classOf[GeoSparkVizKryoRegistrator].getName)
      .getOrCreate()

    GeoSparkSQLRegistrator.registerAll(sparkSession)

    val polygonRDD = createPolygonRDD
    polygonRDD.rawSpatialRDD.rdd.collect().foreach(println(_))

    val spatialDf = Adapter.toDf(polygonRDD,sparkSession)
    spatialDf.printSchema()
    spatialDf.show()
    spatialDf.createOrReplaceTempView("p_view")
    val p_view = sparkSession.sql(""" select _c1,ST_GeomFromWKT(p_view.geometry) as area from p_view """)
    p_view.show(truncate = false)

    p_view.createOrReplaceTempView("area_view")
    val areaDf = sparkSession.sql(""" select _c1,ST_Area(area_view.area) from area_view """)
    areaDf.show(truncate = false)

  }

  def createPolygonRDD(implicit sc:SparkContext):PolygonRDD={
    val polygonRDDInputLocation = "D:\\idea\\demo_spark\\es_demo\\src\\data\\area.csv"
    val polygonRDDStartOffset = 0
    val polygonRDDEndOffset = 7
    val polygonRDDSplitter = FileDataSplitter.CSV // or use  FileDataSplitter.TSV
    val carryOtherAttributes = true
    val objectRDD = new PolygonRDD(sc, polygonRDDInputLocation, polygonRDDStartOffset, polygonRDDEndOffset, polygonRDDSplitter, carryOtherAttributes)
    objectRDD
  }
}

测试结果如下：

+------+------------------+
|_c1   |st_area(area)     |
+------+------------------+
|china1|42.150000000000034|
|china2|32.10000000000002 |
|china3|32.099999999999966|
|china4|48.07499999999999 |
|china5|40.650000000000034|
+------+------------------+