mattmakesmaps

Creating Label Points With OpenStreetMap Data

tl;dr #

-- Use a CTE to create a table of water polygons, aggregating
-- overlapping polygons sharing the same name
WITH polys AS (
    SELECT name, (ST_DUMP(ST_BUFFER(ST_Collect(geometry), 0))).geom AS single_geom
    FROM osm_new_waterareas
    WHERE name IS NOT NULL
    GROUP BY name
)
-- Generate a name and label point for each feature
-- Generate an accurate area attribute by casting to geography
SELECT name, ST_PointOnSurface(single_geom),
       ST_Area(Geography(ST_Transform(single_geom, 4326))) AS area
FROM polys;

Overview #

OpenStreetMap is one the most amazing data projects I know of. The breadth of high-quality, high-precision features contained with the OSM Planet never ceases to impress me. That being said, the freedom of the OSM data model, which allows this high-level of flexibility and openness, is not without its problems. Having participated in the OSM edit-a-thon, I was inspired to deep dive into the OSM tagging ontology, and subsequent rendering workflows. As part of this exploration, one of the major problems I've run into was the generation of label points derived from Open Street Map data.

The following example is illustrative of the primary problem faced when generating label points, overlapping geometries. The SQL statement below returns all features within an imposm database's osm_new_waterareas table named, 'Lake Chelan'. Three geometries are returned, one representing the entire lake, and two others representing northern and southern sections of the lake. All three share a name of, Lake Chelan, but two are of type water, and one is of type reservoir.

SELECT name, type, osm_id
FROM osm_new_waterareas
WHERE name = 'Lake Chelan';

    name     |   type    |  osm_id   
-------------+-----------+-----------
 Lake Chelan | reservoir |    446718
 Lake Chelan | water     | 135297050
 Lake Chelan | water     |  52045429

Click the map below to view each of the three geometries.

gist 03d7f78a1591971ed2d8 chelan.geojson

Rendering label points using PostGIS's ST_PointOnSurface() function would yield three separate points, as illustrated below. I however, would like only a single label point for each feature.

gist 9cf8b49844cb43dfae81 chelan.geojson

Step By Step #

In addition to a single label point, I'd also like to attribute that label point with an area value, to be used as a toggle for display at varying zoom levels.

The query at the beginning of the post generates this output table. Let's walk through the major parts that make up the single_geom parameter, step-by-step.

Step One: ST_Collect() #

The ST_Collect() in PostGIS is an aggregate function. Just as a GROUP BY clause can be used to aggregate features sharing a common attribute value, ST_Collect() can be used to group geometries. In this case, we'll use it to group geometries that are associated with the same name. See the code snippet below.

-- Execute ST_Collect() Function, creating a MULTIPOLYGON
-- for all features sharing a common name.
WITH collected as (
    SELECT name, ST_Collect(geometry) AS geometry
    FROM osm_new_waterareas
    WHERE name = 'Lake Chelan'
    GROUP BY name
) SELECT name, ST_GeometryType(geometry), ST_NRings(geometry)
FROM collected;

-- Results
    name     | st_geometrytype | st_nrings 
-------------+-----------------+-----------
 Lake Chelan | ST_MultiPolygon |         4

This produces the following MULTIPOLYGON result.

gist 8d4cdbc307deb6d7977f

Step Two: ST_Buffer() #

The ST_Buffer() function is used as somewhat of a hack. It has the special quality of creating from its inputs, a new OGC-compliant geometry. This means that when given a set of input geometries, and a '0' buffer-distance value, we are essentially left with a geometry clean geometry that represents the outer-most ring of our MULTIPOLYGON created during the ST_Collect() operation.

-- Add a 0-distance buffer to our above result, creating a
-- new single polygon representing the outer ring.
WITH coll_buff as (
    SELECT name, ST_Buffer(ST_Collect(geometry), 0) AS geometry
    FROM osm_new_waterareas
    WHERE name = 'Lake Chelan'
    GROUP BY name
) SELECT name, ST_GeometryType(geometry), ST_NRings(geometry)
FROM coll_buff;

-- Results
    name     | st_geometrytype | st_nrings 
-------------+-----------------+-----------
 Lake Chelan | ST_Polygon      |         1

Now we're really looking good. We've got a single polygon that represents our water feature.

gist b76fd3997f99086f6d24

I could run ST_PointOnSurface() against the geometry above, and return a single label point. So what more needs to be done? The example above works because all records within my OSM planet file with water features named Lake Chelan happen to occur in the same spot. It's a very unique name. What happens when I run the same query against a more common name, Twin Lakes?

-- Re-execute with a much more common name
WITH coll_buff as (
    SELECT name, ST_Buffer(ST_Collect(geometry), 0) AS geometry
    FROM osm_new_waterareas
    WHERE name = 'Twin Lakes'
    GROUP BY name
) SELECT name, ST_GeometryType(geometry), ST_NRings(geometry)
FROM coll_buff;

-- Our results generate a MULTIPOLYGON, not a POLYGON
    name    | st_geometrytype | st_nrings 
------------+-----------------+-----------
 Twin Lakes | ST_MultiPolygon |       272

A single record is created, however it is the aggregate MULTIPOLYGON of all features in the globe that happen to be named Twin Lakes. Executing ST_PointOnSurface() would return a single point geometry, not the 272 unique label points we need.

Step Three: ST_Dump() #

Now that our ST_Collect() + ST_Buffer() combo has given us a mixed bag of POLYGON and MULTIPOLYGON geometries, it's explode these guys into separate features. For that, we'll be using the ST_Dump() function. In the example of our Lake Chelan polygon, ST_Dump() will create for us our same polygon. However, for our example of the Twin Lakes MULTIPOLYGON with 272 rings, we'll get back 272 unique records. This will allow us to create a label point for each instance of Twin Lakes

ST_Dump is a bit tricky in that it doesn't simply return the exploded geom (MULTIPOLYGON --> POLYGON), but rather a special geometry_dump data type. From that resulting datatype, we'll select out the geom field, giving us our POLYGON.

-- ST_Dump - Lake Chelan Example
-- NOTE: Wrapping ST_Dump() call in parentheses, and returning the geom
-- field from the resulting geometry_dump.
WITH coll_buff as (
    SELECT name, (ST_Dump(ST_Buffer(ST_Collect(geometry), 0))).geom AS geometry
    FROM osm_new_waterareas
    WHERE name = 'Lake Chelan'
    GROUP BY name
) SELECT name, ST_GeometryType(geometry), ST_NRings(geometry)
FROM coll_buff;

-- Returns a single feature as expected.
    name     | st_geometrytype | st_nrings 
-------------+-----------------+-----------
 Lake Chelan | ST_Polygon      |         1


-- ST_Dump - Twin Lakes Example
WITH coll_buff as (
    SELECT name, (ST_Dump(ST_Buffer(ST_Collect(geometry), 0))).geom AS geometry
    FROM osm_new_waterareas
    WHERE name = 'Twin Lakes'
    GROUP BY name
) SELECT name, ST_GeometryType(geometry), ST_NRings(geometry)
FROM coll_buff;

-- Returns each of the 272 rings of the MULTIPOLYGON as
-- unique polygon features.
    name    | st_geometrytype | st_nrings 
------------+-----------------+-----------
 Twin Lakes | ST_Polygon      |         1
 Twin Lakes | ST_Polygon      |         1
 Twin Lakes | ST_Polygon      |         1
 Twin Lakes | ST_Polygon      |         1
-- NOTE: Selection of results shown.

Step Four: Generate An Area Attribute #

In order to have some type of filtration attribute to filter the display of our labels at different zoom levels, we'll be calculating an area attribute. Since our data are in the Spherical Mercator projection common to all web maps, we have inherit problems generating accurate area calculations directly. One solution to this is to CAST our geometries into a PostGIS geography data type.

As a prerequisite to the Geometry --> Geography cast, we need to first use ST_Transform to convert our data from the Spherical Mercator projection to WGS84 Lat/Lng.

The area calculation snippet looks like this

ST_Area(Geography(ST_Transform(single_geom, 4326))) AS area

And wrapped into our larger label point SQL statement, looks like this.

WITH polys AS (
    SELECT name, (ST_DUMP(ST_BUFFER(ST_Collect(geometry), 0))).geom AS single_geom
    FROM osm_new_waterareas
    WHERE name = 'Lake Chelan' 
    GROUP BY name
)
-- Generate a name and label point for each feature
-- Generate an accurate area attribute by casting to geography
SELECT name, ST_PointOnSurface(single_geom),
       ST_Area(Geography(ST_Transform(single_geom, 4326))) AS area
FROM polys;

Just to drive home the importance of accurate area calculations, our call using the CAST to geography returns a value of 132.028 square kilometers, while using the raw Spherical Mercator geometry returns an area value of 295.106 square kilometers! Our geography-based area calculation is much more in line with the 135 square kilometer value reported by wikipedia.

Wrap-up #

And finally, after all that effort, we get the point shown below.

gist cb1f750979bbcf40a8e5

Speeding Up With Indexes #

While this exercise is extremely fast on our small test dataset, scaling this up to an entire imposm OSM planet import is another thing entirely. To facilitate this process, both spatial and non-spatial indexes can be utilized.

I created a partial btree index on the name column, a functional index on the transformation of geometric data to EPSG 4326, and a gist index already existed on the geometry column as part of the imposm import.

-- Create a partial index on names that are not null.
CREATE INDEX idx_waterareas_name_partial ON osm_new_waterareas (name) WHERE name IS NOT NULL;
-- Create a functional index on the transformation
CREATE INDEX idx_waterareas_4326 ON osm_new_waterareas USING gist(ST_Transform(geometry, 4326));

This process, after applying indexes, took about 5.4 minutes to create. That is, to create a global set of label points for ~185,000 named water features within the OSM planet file, with both a name and dynamically calculated area attribute. This test was performed on modest desktop hardware, on a PostGIS instance that was already hot (had been running, been used for querying water area data as part of this experiment).