rasterstats 0.1 (experimental)

Summarize geospatial raster datasets based on vector geometries

Python interface

In addition to the basic usage above, rasterstats supports other mechanisms of specifying vector geometeries.

It integrates with other python objects that support the geo_interface (e.g. Fiona, Shapely, ArcPy, PyShp, GeoDjango):

>>> import fiona

>>> # an iterable of objects with geo_interface
>>> lyr = fiona.open('/path/to/vector.shp')
>>> features = (x for x in lyr if x['properties']['state'] == 'CT')
>>> raster_stats(features, '/path/to/elevation.tif')
...

>>> # a single object with a geo_interface
>>> lyr = fiona.open('/path/to/vector.shp')
>>> raster_stats(lyr.next(), '/path/to/elevation.tif')
...

Or by using with geometries in "Well-Known" formats:

>>> raster_stats('POINT(-124 42)', '/path/to/elevation.tif')
...

Working with categorical rasters (e.g. vegetation map)

You can treat rasters as categorical (i.e. raster values represent discrete classes) if only interested in the counts of unique pixel values.

For example, this polygon is comprised of 12 pixels of oak (raster value 32) and 78 pixels of grassland (raster value 33):

>>> raster_stats(lyr.next(), '/path/to/vegetation.tif', categorical=True)

>>> [{'id': 1, 32: 12, 33: 78}]

Keep in mind that rasterstats just reports on the pixel values as keys; It is up to the programmer to associate the pixel values with their appropriate meaning (e.g. oak == 32) for reporting.