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Sep 3, 2019
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Docs/fix vector job heap space #328

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c2f5f50
Reduce job size and name chunk on vector data page
vpipkt Sep 2, 2019
5bcbb79
Line continuation in doc chunk
vpipkt Sep 2, 2019
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15 changes: 9 additions & 6 deletions pyrasterframes/src/main/python/docs/vector-data.pymd
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Expand Up @@ -79,18 +79,21 @@ from pyrasterframes.rasterfunctions import st_centroid
df.select(df.state_code, inefficient_centroid(df.geometry), st_centroid(df.geometry))
```

The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example, we use the @ref:[built-in Landsat catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter to those within approximately 500 km of a selected point.
The RasterFrames vector functions and GeoMesa functions also provide a variety of spatial relations that are useful in combination with the geometric properties of projected rasters. In this example, we use the @ref:[built-in Landsat catalog](raster-catalogs.md#using-built-in-experimental-catalogs) which provides an extent. We will convert the extent to a polygon and filter to those within approximately 50 km of a selected point.

```python, evaluate=True
```python, spatial_relation, evaluate=True
from pyrasterframes.rasterfunctions import st_geometry, st_bufferPoint, st_intersects, st_point
from pyspark.sql.functions import lit
l8 = spark.read.format('aws-pds-l8-catalog').load()

l8 = l8.withColumn('geom', st_geometry(l8.bounds_wgs84))
l8 = l8.withColumn('paducah', st_point(lit(-88.6275), lit(37.072222)))
l8 = l8.withColumn('geom', st_geometry(l8.bounds_wgs84)) # extent to polygon
l8 = l8.withColumn('paducah', st_point(lit(-88.628), lit(37.072))) # col of points

l8_filtered = l8.filter(st_intersects(l8.geom, st_bufferPoint(l8.paducah, lit(500000.0))))
l8_filtered.select('product_id', 'entity_id', 'acquisition_date', 'cloud_cover_pct')
l8_filtered = l8 \
.filter(st_intersects(l8.geom, st_bufferPoint(l8.paducah, lit(50000.0)))) \
.filter(l8.acquisition_date > '2018-02-01') \
.filter(l8.acquisition_date < '2018-04-01')
l8_filtered.select('product_id', 'entity_id', 'acquisition_date', 'cloud_cover_pct').toPandas()
```

[GeoPandas]: http://geopandas.org
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