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    SparkConf conf = new SparkConf();
    conf.setAppName("GeoSpark03");
    conf.setMaster("local[*]");
    conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
    conf.set("spark.kryo.registrator", "org.datasyslab.geospark.serde.GeoSparkKryoRegistrator");
    JavaSparkContext sc = new JavaSparkContext(conf);

    SpatialRDD parkRdd = ShapefileReader.readToGeometryRDD(sc, "D:/bigdata/data/parks");
    // 构建索引
    boolean buildOnSpatialPartitionedRDD = false; // 如果只需要在做空间分析的时候构建索引,则设置为true
    // 临近查询(KNN)
    parkRdd.buildIndex(IndexType.RTREE, buildOnSpatialPartitionedRDD);  // QTREE不支持KNN查询
    //写入
    parkRdd.indexedRawRDD.saveAsObjectFile("hdfs://localhost:8020/parkrdd");

    //读取
    SpatialRDD savedRDD = new SpatialRDD<Geometry>();
    savedRDD.indexedRawRDD = sc.objectFile("hdfs://localhost:8020/parkrdd");
    GeometryFactory geometryFactory = new GeometryFactory();
    boolean usingIndex = true;  // 使用索引
    Point pointObject = geometryFactory.createPoint(new Coordinate(-123.1,49.2));
    int K = 5; // K Nearest Neighbors
    List<Geometry> result = KNNQuery.SpatialKnnQuery(savedRDD, pointObject, K, usingIndex);  这里报错
    // 输出结果
    System.out.println("距离点(-123.1,49.2)最近的五个公园是:");
    for (int i = 0; i < result.size(); i++){
        System.out.println(String.format("%d: %s",i+1, result.get(i).toString()));
    }
    sc.stop();