22 Using GeoJSON Geographic Data
GeoJSON objects are JSON objects that represent geographic data. Examples are provided of creating GeoJSON data, indexing it, and querying it.
GeoJSON Objects: Geometry, Feature, Feature Collection
GeoJSON uses JSON objects that represent various geometrical entities and combinations of these together with user-defined properties.
A position is an array of two or more spatial (numerical) coordinates, the first three of which generally represent longitude, latitude, and altitude.
A geometry object has a type
field and (except for a geometry-collection object) a coordinates
field, as shown in Table 22-1.
A geometry collection is a geometry object with type
GeometryCollection
. Instead of a coordinates
field it has a geometries
field, whose value is an array of geometry objects other than GeometryCollection
objects.
Table 22-1 GeoJSON Geometry Objects Other Than Geometry Collections
type Field
|
coordinates Field
|
---|---|
Point |
A position. |
MultiPoint |
An array of positions. |
LineString |
An array of two or more positions. |
MultiLineString |
An array of LineString arrays of positions.
|
Polygon |
A MultiLineString , each of whose arrays is a LineString whose first and last positions coincide (are equivalent). If the array of a polygon contains more than one array then the first represents the outside polygon and the others represent holes inside it.
|
MultiPolygon |
An array of Polygon arrays, that is, multidimensional array of positions.
|
A feature object has a type
field of value Feature
, a geometry
field whose value is a geometric object, and a properties
field whose value can be any JSON object.
A feature collection object has a type
field of value FeatureCollection
, and it has a features
field whose value is an array of feature objects.
Example 22-1 presents a feature-collection object whose features
array has three features. The geometry
of the first feature is of type Point
; that of the second is of type LineString
; and that of the third is of type Polygon
.
Query and Index GeoJSON Data
You can use SQL/JSON query functions and conditions to examine GeoJSON data or to project parts of it as non-JSON data, including as Oracle Spatial and Graph SDO_GEOMETRY
object-type instances. This is illustrated in Example 22-2, Example 22-3, and Example 22-5.
To improve query performance, you can create an Oracle Spatial and Graph index (type MDSYS.SPATIAL_INDEX
) on function json_value
applied to GeoJSON data. This is illustrated by Example 22-4.
Example 22-4 indexes only one particular element of an array of geometry features (the first element). A B-tree index on function json_value
can target only a scalar value. To improve the performance of queries, such as that of Example 22-3, that target any number of array elements, you can do the following:
-
Create an on-statement, refreshable materialized view of the array data, and place that view in memory.
-
Create a spatial index on the array data.
This is shown in Example 22-6 and Example 22-7.
SDO_GEOMETRY Object-Type Instances and Spatial Operations
You can convert Oracle Spatial and Graph SDO_GEOMETRY
object-type instances to GeoJSON objects and GeoJSON objects to SDO_GEOMETRY
instances.
You can use Oracle Spatial and Graph operations on SDO_GEOMETRY
objects that you obtain from GeoJSON objects. For example, you can use operator sdo_distance
in PL/SQL package SDO_GEOM
to compute the minimum distance between two geometry objects. This is the distance between the closest two points or two segments, one point or segment from each object. This is illustrated by Example 22-5.
See Also:
-
Oracle Spatial and Graph Developer's Guide for information about using GeoJSON data with Oracle Spatial and Graph
-
Oracle Spatial and Graph Developer's Guide for information about Oracle Spatial and Graph and
SDO_GEOMETRY
object type -
GeoJSON.org for information about GeoJSON
-
The GeoJSON Format Specification for details about GeoJSON data
Example 22-1 A Table With GeoJSON Data
This example creates table j_geo
, which has a column, geo_doc
of GeoJSON documents.
Only one such document is inserted here. It contains a GeoJSON object of type
FeatureCollection
, and a features
array of objects of type
Feature
. Those objects have geometry
, respectively, of type
Point
, LineString
, and Polygon
.
CREATE TABLE j_geo
(id VARCHAR2 (32) NOT NULL,
geo_doc VARCHAR2 (4000) CHECK (geo_doc IS JSON));
INSERT INTO j_geo
VALUES (1,
'{"type" : "FeatureCollection",
"features" : [{"type" : "Feature",
"geometry" : {"type" : "Point",
"coordinates" : [-122.236111, 37.482778]},
"properties" : {"Name" : Redwood City"}},
{"type" : "Feature",
"geometry" : {"type" : "LineString",
"coordinates" : [[102.0, 0.0],
[103.0, 1.0],
[104.0, 0.0],
[105.0, 1.0]]},
"properties" : {"prop0" : "value0",
"prop1" : 0.0}},
{"type" : "Feature",
"geometry" : {"type" : "Polygon",
"coordinates" : [[[100.0, 0.0],
[101.0, 0.0],
[101.0, 1.0],
[100.0, 1.0],
[100.0, 0.0]]]},
"properties" : {"prop0" : "value0",
"prop1" : {"this" : "that"}}}]}');
Example 22-2 Selecting a geometry Object From a GeoJSON Feature As an SDO_GEOMETRY Instance
This example uses SQL/JSON function json_value
to select the value of field geometry
from the first element of array features
. The value is returned as Oracle Spatial and Graph data, not as JSON data, that is, as an instance of PL/SQL object type SDO_GEOMETRY
, not as a SQL string or LOB instance.
SELECT json_value(geo_doc, '$.features[0].geometry'
RETURNING SDO_GEOMETRY
ERROR ON ERROR)
FROM j_geo;
The value returned is this, which represents a point with longitude and latitude (coordinates) -122.236111 and 37.482778, respectively.
SDO_GEOMETRY(2001, 4326, SDO_POINT_TYPE(-122.236111, 37.482778, NULL), NULL, NULL)
See Also:
Oracle Database SQL Language Reference for information about SQL/JSON function json_value
Example 22-3 Retrieving Multiple geometry Objects From a GeoJSON Feature As SDO_GEOMETRY
This example uses SQL/JSON function json_table
to project the value of field geometry
from each element of array features
, as column sdo_val
of a virtual table. The retrieved data is returned as SDO_GEOMETRY
.
SELECT jt.*
FROM j_geo,
json_table(geo_doc, '$.features[*]'
COLUMNS (sdo_val SDO_GEOMETRY PATH '$.geometry')) jt;
See Also:
Oracle Database SQL Language Reference for information about SQL/JSON function json_table
The following three rows are returned for the query. The first represents the same Point
as in Example 22-2. The second represents the LineString
array. The third represents the Polygon
.
SDO_GEOMETRY(2001, 4326, SDO_POINT_TYPE(-122.236111, 37.482778, NULL), NULL, NULL)
SDO_GEOMETRY(2002, 4326, NULL, SDO_ELEM_INFO_ARRAY(1, 2, 1),
SDO_ORDINATE_ARRAY(102, 0, 103, 1, 104, 0, 105, 1))
SDO_GEOMETRY(2003, 4326, NULL, SDO_ELEM_INFO_ARRAY(1, 1003, 1),
SDO_ORDINATE_ARRAY(100, 0, 101, 0, 101, 1, 100, 1, 100, 0))
The second and third elements of attribute SDO_ELEM_INFO_ARRAY
specify how to interpret the coordinates provided by attribute SDO_ORDINATE_ARRAY
. They show that the first row returned represents a line string (2) with straight segments (1), and the second row represents a polygon (2003) of straight segments (1).
Example 22-4 Creating a Spatial Index For Scalar GeoJSON Data
This example creates a json_value
function-based index of type MDSYS.SPATIAL_INDEX
on field geometry
of the first element of array features
. This can improve the performance of queries that use json_value
to retrieve that value.
CREATE INDEX geo_first_feature_idx
ON j_geo (json_value(geo_doc, '$.features[0].geometry'
RETURNING SDO_GEOMETRY))
INDEXTYPE IS MDSYS.SPATIAL_INDEX;
Example 22-5 Using GeoJSON Geometry With Spatial Operators
This example selects the documents (there is only one in this table) for which the geometry
field of the first features
element is within 100 kilometers of a given point. The point is provided literally here (its coordinates
are the longitude and latitude of San Francisco, California). The distance is computed from this point to each geometry object.
The query orders the selected documents by the calculated distance. The tolerance in meters for the distance calculation is provided in this query as the literal argument 100.
SELECT id,
json_value(geo_doc, '$features[0].properties.Name') "Name",
SDO_GEOM.sdo_distance(
json_value(geo_doc, '$features[0].geometry') RETURNING SDO_GEOMETRY,
SDO_GEOMETRY(2001,
4326,
SDO_POINT_TYPE(-122.416667, 37.783333, NULL),
NULL,
NULL),
100, -- Tolerance in meters
'unit=KM') "Distance in kilometers"
FROM j_geo
WHERE sdo_within_distance(
json_value(geo_doc, '$.features[0].geometry' RETURNING SDO_GEOMETRY),
SDO_GEOMETRY(2001,
4326,
SDO_POINT_TYPE(-122.416667, 37.783333, NULL),
NULL,
NULL),
'distance=100 unit=KM')
= 'TRUE';
See Also:
Oracle Database SQL Language Reference for information about SQL/JSON function json_value
The query returns a single row:
ID Name Distance in kilometers
----- -------------- ----------------------
1 Redwood City 26.9443035
Example 22-6 Creating a Materialized View Over GeoJSON Data
CREATE OR REPLACE MATERIALIZED VIEW geo_doc_view
BUILD IMMEDIATE
REFRESH FAST ON STATEMENT WITH ROWID
AS SELECT g.rowid, jt.*
FROM j_geo g,
json_table(geo_doc, '$.features[*]'
COLUMNS (sdo_val SDO_GEOMETRY PATH '$.geometry')) jt;
Example 22-7 Creating a Spatial Index on a Materialized View Over GeoJSON Data
This example first prepares for the creation of the spatial index by populating some spatial-indexing metadata. It then creates the index on the SDO_GEOMETRY
column, sdo_val
, of materialized view geo_doc_view
, which is created in Example 22-6. Except for the view and column names, the code for populating the indexing metadata is fixed — use it each time you need to create a spatial index on a materialized view.
-- Populate spatial-indexing metadata
INSERT INTO USER_SDO_GEOM_METADATA
VALUES ('GEO_DOC_VIEW',
'SDO_VAL',
MDSYS.sdo_dim_array(
MDSYS.sdo_dim_element('Longitude', -180, 180, 0.05),
MDSYS.sdo_dim_element('Latitude', -90, 90, 0.05)),
7
4326);
-- Create spatial index on geometry column of materialized view
CREATE INDEX geo_all_features_idx ON geo_doc_view(sdo_val)
INDEXTYPE IS MDSYS.SPATIAL_INDEX V2;
Related Topics
Parent topic: GeoJSON Geographic Data