Merge pull request #53 from hotosm/fix/holes-in-tasks

Merge polygons with no features with neighboring polygons

fmtm_splitter/fmtm_algorithm.sql

@@ -229,10 +229,10 @@ CREATE TABLE clusteredbuildings AS (
     ),
 
     -- Cluster the buildings within each splitpolygon. The second term in the
-    -- call to the ST_ClusterKMeans function is the number of clusters to create,
-    -- so we're dividing the number of features by a constant (10 in this case)
-    -- to get the number of clusters required to get close to the right number
-    -- of features per cluster.
+    -- call to the ST_ClusterKMeans function is the number of clusters to 
+    -- create, so we're dividing the number of features by a constant 
+    -- (10 in this case) to get the number of clusters required to get close
+    -- to the right number of features per cluster.
     -- TODO: This should certainly not be a hardcoded, the number of features
     --       per cluster should come from a project configuration table
     buildingstocluster AS (
@@ -380,13 +380,80 @@ CREATE TABLE taskpolygons AS (
 );
 
 -- ALTER TABLE taskpolygons ADD PRIMARY KEY(taskid);
+
+-- Step 1: Identify polygons without any buildings
+DROP TABLE IF EXISTS no_building_polygons;
+CREATE TABLE no_building_polygons AS (
+    SELECT
+        tp.*,
+        tp.geom AS no_building_geom
+    FROM taskpolygons AS tp
+    LEFT JOIN buildings AS b ON ST_INTERSECTS(tp.geom, b.geom)
+    WHERE b.geom IS NULL
+);
+
+-- Step 2: Identify neighboring polygons
+DROP TABLE IF EXISTS neighboring_polygons;
+CREATE TABLE neighboring_polygons AS (
+    SELECT
+        nb.*,
+        nb.geom AS neighbor_geom,
+        nb_building_count,
+        nbp.no_building_geom
+    FROM taskpolygons AS nb
+    INNER JOIN no_building_polygons AS nbp
+        -- Finds polygons that touch each other
+        ON ST_TOUCHES(nbp.no_building_geom, nb.geom)
+    LEFT JOIN (
+        -- Step 3: Count buildings in the neighboring polygons
+        SELECT
+            nb.geom,
+            COUNT(b.geom) AS nb_building_count
+        FROM taskpolygons AS nb
+        LEFT JOIN buildings AS b ON ST_INTERSECTS(nb.geom, b.geom)
+        GROUP BY nb.geom
+    ) AS building_counts
+        ON nb.geom = building_counts.geom
+);
+
+-- Step 4: Find the optimal neighboring polygon to avoid,
+-- same polygons with the smallest number of buildings merging into
+-- multiple neighboring polygons
+DROP TABLE IF EXISTS optimal_neighbors;
+CREATE TABLE optimal_neighbors AS (
+    SELECT
+        nbp.no_building_geom,
+        nbp.neighbor_geom
+    FROM neighboring_polygons AS nbp
+    WHERE nbp.nb_building_count = (
+        SELECT MIN(nb_building_count)
+        FROM neighboring_polygons AS np
+        WHERE np.no_building_geom = nbp.no_building_geom
+    )
+);
+
+-- Step 5: Merge the small polygons with their optimal neighboring polygons
+UPDATE taskpolygons tp
+SET geom = ST_UNION(tp.geom, nbp.no_building_geom)
+FROM optimal_neighbors AS nbp
+WHERE tp.geom = nbp.neighbor_geom;
+DELETE FROM taskpolygons
+WHERE geom IN (
+    SELECT no_building_geom
+    FROM no_building_polygons
+);
+
+
+DROP TABLE IF EXISTS no_building_polygons;
+DROP TABLE IF EXISTS neighboring_polygons;
+
 SELECT POPULATE_GEOMETRY_COLUMNS('public.taskpolygons'::regclass);
 CREATE INDEX taskpolygons_idx
 ON taskpolygons
 USING gist (geom);
 -- VACUUM ANALYZE taskpolygons;
 
-
+-- Generate GeoJSON output
 SELECT
     JSONB_BUILD_OBJECT(
         'type', 'FeatureCollection',

fmtm_splitter/splitter.py

@@ -29,6 +29,7 @@
 import geojson
 import numpy as np
 from geojson import Feature, FeatureCollection, GeoJSON
+from osm_rawdata.postgres import PostgresClient
 from psycopg2.extensions import connection
 from shapely.geometry import Polygon, shape
 from shapely.geometry.geo import mapping
@@ -42,7 +43,6 @@
     drop_tables,
     insert_geom,
 )
-from osm_rawdata.postgres import PostgresClient
 
 # Instantiate logger
 log = logging.getLogger(__name__)