（三）Geospark空间查询

2021-04-29

Geospark空间查询java代码示例。

Geospark空间查询

GeoSpark空间索引

GeoSpark提供两种空间索引：Quad-Tree和R-Tree
和上一节一样，我们初始化一个SparkContext，并调用GeoSpark的ShapefileReader，将我们的Shape文件导入。

SparkConf conf = new SparkConf();
conf.setAppName("GeoSpark02");
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);

// Get SpatialRDD
String shapeInputLocation = Learn02.class.getResource("/parks").toString();
SpatialRDD rdd = ShapefileReader.readToGeometryRDD(sc, shapeInputLocation);

构建索引

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// 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
rdd.buildIndex(IndexType.QUADTREE, buildOnSpatialPartitionedRDD);

使用索引查询

// 查询
GeometryFactory geometryFactory = new GeometryFactory();
Coordinate[] coordinates = new Coordinate[5];
coordinates[0] = new Coordinate(-123.1,49.2);
coordinates[1] = new Coordinate(-123.1,49.3);
coordinates[2] = new Coordinate(-123.2,49.3);
coordinates[3] = new Coordinate(-123.2,29.2);
coordinates[4] = coordinates[0]; // The last coordinate is the same as the first coordinate in order to compose a closed ring
Polygon polygonObject = geometryFactory.createPolygon(coordinates);
boolean usingIndex = true;  // 使用索引
JavaRDD<Geometry> queryResult = RangeQuery.SpatialRangeQuery(rdd, polygonObject, false, usingIndex);
System.out.println(String.format("查询结果总数为: %d",queryResult.count()));

1	查询结果总数为: 62

3. 输出查询结果

// 遍历查询结果
queryResult.foreach(new VoidFunction<Geometry>() {
    @Override
    public void call(Geometry geometry) throws Exception {
        System.out.println(geometry);
    }
});

POLYGON ((-123.15566057081632 49.26206733490204, -123.15564728017853 49.26241791476514, -123.15548939905344 49.262415429329856, -123.15550257747702 49.26206484963618, -123.15566057081632 49.26206733490204))  1   -9999       Kitsilano           N       
POLYGON ((-123.15760176703519 49.261936547646954, -123.15718706338478 49.2619299178749, -123.15719832396375 49.26162160945501, -123.15761313807661 49.26162814910161, -123.15760218456263 49.26192530535148, -123.15760176703519 49.261936547646954))   2   208 Rosemary Brown Park Kitsilano   W 11th Avenue   Vine Street N   N   N
.................................
POLYGON ((-123.12325003271694 49.290529597005786, -123.12325184999034 
POLYGON ((-123.11921795166444 49.288179012132034, -123.11889234917355 49.28806261407178, -123.11905901714364 49.28781953241384, -123.11954592548769 49.28796238352621, -123.11921795166444 49.288179012132034)) 80  27  Portal Park Downtown    W Hastings Street   Thurlow Street  N   N   N

KNN临近查询

KNN临近查询用于查询距指定点最近的K个Geometry，在本案例中我们指定中心点为（-123.1,49.2），我们来查询距离这个点最近的5个公园的分布。

注意：QTREE索引不支持KNN查询，所以在使用KNN的时候，可以选择R-Tree索引。

// 临近查询(KNN)
rdd.buildIndex(IndexType.RTREE, buildOnSpatialPartitionedRDD);  // QTREE不支持KNN查询
Point pointObject = geometryFactory.createPoint(new Coordinate(-123.1,49.2));
int K = 5; // K Nearest Neighbors
List<Geometry> result = KNNQuery.SpatialKnnQuery(rdd, pointObject, K, usingIndex);

距离点(-123.1,49.2)最近的五个公园是:
1: MULTIPOLYGON...  3   141 Tea Swamp Park  Mount Pleasant  E 15th Avenue   Sophia Street   N   N   N
2: POLYGON ...  23  140 Robson Park Mount Pleasant  Kingsway    St. George Street   N   Y   N
4: POLYGON...   18  138 Major Matthews Park Mount Pleasant  W 11th Avenue   Manitoba Street N   N   N
5: POLYGON ...  24  136 Guelph Park Mount Pleasant  E 7th Avenue    Brunswick Street    N   N   N

空间关联查询（Spatial Join Query)

空间关联查询：创建一个表联接（类似于SQL语句中的join关联），其中根据两层中特性的相对位置，将一个图层属性表中的字段追加到另一个图层层属性表中。

现在我们有两个图层，一个是公园（polygon），另外一个是公园里一些点（point），现在我们要把这些点所在公园的属性赋给这些点，用到的就是空间关联查询。

// 空间关联查询
shapeInputLocation = Learn02.class.getResource("/point").toString();
SpatialRDD pointRdd = ShapefileReader.readToGeometryRDD(sc, shapeInputLocation);
// analyze方法主要用来计算边界
pointRdd.analyze();
parkRdd.analyze();
// spark中的分区操作,空间关联查询前必须进行
parkRdd.spatialPartitioning(GridType.KDBTREE);
pointRdd.spatialPartitioning(parkRdd.getPartitioner());

boolean considerBoundaryIntersection = true;  // 因为是以点为基础,因此必须考虑边界
usingIndex = false;
JavaPairRDD<Geometry, HashSet<Geometry>> joinResult = JoinQuery.SpatialJoinQuery(parkRdd, pointRdd, usingIndex, considerBoundaryIntersection);
System.out.println("空间关联查询结果为:");
joinResult.foreach((kv)->{
    System.out.println(String.format("{%s}--{%s}", kv._1.getUserData().toString(), kv._2.toString()));
});

SpatialJoinQuery返回一个类似于Map集合的RDD，其中Key是SpatialJoinQuery中的第一个RDD，也就是我们的pointRDD，Value是一个Set集合，集合里面每个元素都是来自于SpatialJoinQuery中的第二个RDD，也就是parkRDD，在我们这个案例下，我们的key、value都是一一对应的，但是当我们反过来执行JoinQuery.SpatialJoinQuery(pointRdd, parkRdd, usingIndex, considerBoundaryIntersection)，那此时key和value就是一对多关系了，因此一个公园里会有很多个点，但是一个点只会属于一个公园（边界上的点除外）。

{5}--{[POLYGON...   62  201 English Bay Beach Park  West End    Beach Avenue    Bidwell Street  N   Y   Y]}
{10}--{[POLYGON ... 48  110 Hadden Park Kitsilano   Ogden Avenue    Cypress Street  N   Y   Y]}
{13}--{[POLYGON ... 76  24  Marina Square   Downtown    Bayshore Drive  Denman Street   N   N   Y]}
{14}--{[POLYGON...  77  11  Cardero Park    Downtown    Bayshore Drive  Cardero Street  N   N   Y]}
{3}--{[POLYGON ...  74  206 Stanley Park    West End    W Georgia Street    Chilco Street   N   Y   Y]}
{2}--{[POLYGON...   61  -9999   Sunset Beach Park   West End            Y       ]}
{0}--{[POLYGON ...  73  -9999   Nelson Park West End            Y       ]}
{1}--{[POLYGON ...  74  206 Stanley Park    West End    W Georgia Street    Chilco Street   N   Y   Y]}
{15}--{[POLYGON ... 75  18  Devonian Harbour Park   Downtown    W Georgia Street    Denman Street   N   N   Y]}
{19}--{[POLYGON... 35   28  CRAB Park at Portside   Downtown    E Waterfront Road   Main Street Y   Y   N]}
{8}--{[POLYGON ...  73  -9999   Nelson Park West End            Y       ]}
{6}--{[POLYGON ...  62  201 English Bay Beach Park  West End    Beach Avenue    Bidwell Street  N   Y   Y]}
{12}--{[POLYGON ... 79  -9999   Harbour Green Park  Downtown            N       ]}
{11}--{[POLYGON ... 78  13  Coal Harbour Park   Downtown    W Hastings Street   Broughton Street    N   N   N]}