Summarize geospatial raster datasets based on vector geometries

## Project Description

rasterstats

===========

|BuildStatus|_

|CoverageStatus|_

|PyPiVersion|_

|PyPiDownloads|_

The ``rasterstats`` python module provides a fast and flexible

tool to summarize geospatial raster datasets based on vector geometries

(i.e. zonal statistics).

- Raster data support

- Any raster data source supported by GDAL

- Support for continuous and categorical

- Respects null/no-data metadata or takes argument

- Vector data support

- Points, Lines, Polygon and Multi-\* geometries

- Flexible input formats

- Any vector data source supported by OGR

- Python objects that are geojson-like mappings or support the `geo\_interface <https://gist.github.com/sgillies/2217756>`_

- Well-Known Text/Binary (WKT/WKB) geometries

- Depends on GDAL, Shapely and numpy

Install

-------

Using ubuntu 12.04::

sudo apt-get install python-numpy python-gdal

pip install rasterstats

Example Usage

-------------

Given a polygon vector layer and a digitial elevation model (DEM)

raster, calculate the mean elevation of each polygon:

.. figure:: https://github.com/perrygeo/python-raster-stats/raw/master/docs/img/zones_elevation.png

:align: center

:alt: zones elevation

::

>>> from rasterstats import zonal_stats

>>> stats = zonal_stats("tests/data/polygons.shp", "tests/data/elevation.tif")

>>> stats[1].keys()

['__fid__', 'count', 'min', 'max', 'mean']

>>> [(f['__fid__'], f['mean']) for f in stats]

[(1, 756.6057470703125), (2, 114.660084635416666)]

Statistics

^^^^^^^^^^

By default, the ``zonal_stats`` function will return the following statistics

- min

- max

- mean

- count

Optionally, these statistics are also available

- sum

- std

- median

- majority

- minority

- unique

- range

- percentile (see note below for details)

You can specify the statistics to calculate using the ``stats`` argument::

>>> stats = zonal_stats("tests/data/polygons.shp",

"tests/data/elevation.tif"

stats=['min', 'max', 'median', 'majority', 'sum'])

>>> # also takes space-delimited string

>>> stats = zonal_stats("tests/data/polygons.shp",

"tests/data/elevation.tif"

stats="min max median majority sum")

Note that the more complex statistics may require significantly more processing so

performance can be impacted based on which statistics you choose to calculate.

*New in 0.6*.

You can use a percentile statistic by specifying

``percentile_<q>`` where ``<q>`` can be a floating point number between 0 and 100.

User-defined Statistics

^^^^^^^^^^^^^^^^^^^^^^^

*New in 0.6*.

You can define your own aggregate functions using the ``add_stats`` argument.

This is a dictionary with the name(s) of your statistic as keys and the function(s)

as values. For example, to reimplement the `mean` statistic::

from __future__ import division

import numpy as np

def mymean(x):

return np.ma.mean(x)

then use it in your ``zonal_stats`` call like so::

stats = zonal_stats(vector, raster, add_stats={'mymean':mymean})

Specifying Geometries

^^^^^^^^^^^^^^^^^^^^^

In addition to the basic usage above, rasterstats supports other

mechanisms of specifying vector geometries.

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')

>>> zonal_stats(features, '/path/to/elevation.tif')

...

>>> # a single object with a geo_interface

>>> lyr = fiona.open('/path/to/vector.shp')

>>> zonal_stats(lyr.next(), '/path/to/elevation.tif')

...

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

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

...

Feature Properties

^^^^^^^^^^^^^^^^^^

By default, an \_\_fid\_\_ property is added to each feature's results. None of

the other feature attributes/proprties are copied over unless ``copy_properties``

is set to True::

>>> stats = zonal_stats("tests/data/polygons.shp",

"tests/data/elevation.tif"

copy_properties=True)

>>> stats[0].has_key('name') # name field from original shapefile is retained

True

Rasterization Strategy

^^^^^^^^^^^^^^^^^^^^^^

There are two rasterization strategies to consider::

1. (DEFAULT) Rasterize to the line render path or cells having a center point within the polygon

2. The ``ALL_TOUCHED`` strategy which rasterizes the geometry according to every cell that it touches. You can enable this specifying::

>>> zonal_stats(..., all_touched=True)

There is no right or wrong way to rasterize a vector; both approaches are valid and there are tradeoffs to consider. Using the default rasterizer may miss polygons that are smaller than your cell size. Using the ALL_TOUCHED strategy includes many cells along the edges that may not be representative of the geometry and give biased results when your geometries are much larger than your cell size.

Working with categorical rasters

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

You can treat rasters as categorical (i.e. raster values represent

discrete classes) if you're only interested in the counts of unique pixel

values.

For example, you may have a raster vegetation dataset and want to summarize

vegetation by polygon. Statistics such as mean, median, sum, etc. don't make much sense in this context

(What's the sum of ``oak + grassland``?).

The polygon below is comprised of 12 pixels of oak (raster value

32) and 78 pixels of grassland (raster value 33)::

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

>>> [{'__fid__': 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`` is key ``32``) for reporting.

"Mini-Rasters"

^^^^^^^^^^^^^^^

Internally, we create a masked raster dataset for each feature in order to

calculate statistics. Optionally, we can include these data in the output

of ``zonal_stats`` using the ``raster_out`` argument::

stats = zonal_stats(vector, raster, raster_out=True)

Which gives us three additional keys for each feature::

``mini_raster`` : Numpy ndarray

``mini_raster_GT`` : Six-tuple defining the geotransform (GDAL ordering)

``mini_raster_NDV`` : Nodata value in the returned array

Keep in mind that having ndarrays in your stats dictionary means it is more

difficult to serialize to json and other text formats.

Issues

------

Find a bug? Report it via github issues by providing

- a link to download the smallest possible raster and vector dataset necessary to reproduce the error

- python code or command to reproduce the error

- information on your environment: versions of python, gdal and numpy and system memory

.. |BuildStatus| image:: https://api.travis-ci.org/perrygeo/python-raster-stats.png

.. _BuildStatus: https://travis-ci.org/perrygeo/python-raster-stats

.. |CoverageStatus| image:: https://coveralls.io/repos/perrygeo/python-raster-stats/badge.png

.. _CoverageStatus: https://coveralls.io/r/perrygeo/python-raster-stats

.. |PyPiVersion| image:: https://pypip.in/v/rasterstats/badge.png

.. _PyPiVersion: http://pypi.python.org/pypi/rasterstats

.. |PyPiDownloads| image:: https://pypip.in/d/rasterstats/badge.png

.. _PyPiDownloads: http://pypi.python.org/pypi/rasterstats

===========

|BuildStatus|_

|CoverageStatus|_

|PyPiVersion|_

|PyPiDownloads|_

The ``rasterstats`` python module provides a fast and flexible

tool to summarize geospatial raster datasets based on vector geometries

(i.e. zonal statistics).

- Raster data support

- Any raster data source supported by GDAL

- Support for continuous and categorical

- Respects null/no-data metadata or takes argument

- Vector data support

- Points, Lines, Polygon and Multi-\* geometries

- Flexible input formats

- Any vector data source supported by OGR

- Python objects that are geojson-like mappings or support the `geo\_interface <https://gist.github.com/sgillies/2217756>`_

- Well-Known Text/Binary (WKT/WKB) geometries

- Depends on GDAL, Shapely and numpy

Install

-------

Using ubuntu 12.04::

sudo apt-get install python-numpy python-gdal

pip install rasterstats

Example Usage

-------------

Given a polygon vector layer and a digitial elevation model (DEM)

raster, calculate the mean elevation of each polygon:

.. figure:: https://github.com/perrygeo/python-raster-stats/raw/master/docs/img/zones_elevation.png

:align: center

:alt: zones elevation

::

>>> from rasterstats import zonal_stats

>>> stats = zonal_stats("tests/data/polygons.shp", "tests/data/elevation.tif")

>>> stats[1].keys()

['__fid__', 'count', 'min', 'max', 'mean']

>>> [(f['__fid__'], f['mean']) for f in stats]

[(1, 756.6057470703125), (2, 114.660084635416666)]

Statistics

^^^^^^^^^^

By default, the ``zonal_stats`` function will return the following statistics

- min

- max

- mean

- count

Optionally, these statistics are also available

- sum

- std

- median

- majority

- minority

- unique

- range

- percentile (see note below for details)

You can specify the statistics to calculate using the ``stats`` argument::

>>> stats = zonal_stats("tests/data/polygons.shp",

"tests/data/elevation.tif"

stats=['min', 'max', 'median', 'majority', 'sum'])

>>> # also takes space-delimited string

>>> stats = zonal_stats("tests/data/polygons.shp",

"tests/data/elevation.tif"

stats="min max median majority sum")

Note that the more complex statistics may require significantly more processing so

performance can be impacted based on which statistics you choose to calculate.

*New in 0.6*.

You can use a percentile statistic by specifying

``percentile_<q>`` where ``<q>`` can be a floating point number between 0 and 100.

User-defined Statistics

^^^^^^^^^^^^^^^^^^^^^^^

*New in 0.6*.

You can define your own aggregate functions using the ``add_stats`` argument.

This is a dictionary with the name(s) of your statistic as keys and the function(s)

as values. For example, to reimplement the `mean` statistic::

from __future__ import division

import numpy as np

def mymean(x):

return np.ma.mean(x)

then use it in your ``zonal_stats`` call like so::

stats = zonal_stats(vector, raster, add_stats={'mymean':mymean})

Specifying Geometries

^^^^^^^^^^^^^^^^^^^^^

In addition to the basic usage above, rasterstats supports other

mechanisms of specifying vector geometries.

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')

>>> zonal_stats(features, '/path/to/elevation.tif')

...

>>> # a single object with a geo_interface

>>> lyr = fiona.open('/path/to/vector.shp')

>>> zonal_stats(lyr.next(), '/path/to/elevation.tif')

...

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

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

...

Feature Properties

^^^^^^^^^^^^^^^^^^

By default, an \_\_fid\_\_ property is added to each feature's results. None of

the other feature attributes/proprties are copied over unless ``copy_properties``

is set to True::

>>> stats = zonal_stats("tests/data/polygons.shp",

"tests/data/elevation.tif"

copy_properties=True)

>>> stats[0].has_key('name') # name field from original shapefile is retained

True

Rasterization Strategy

^^^^^^^^^^^^^^^^^^^^^^

There are two rasterization strategies to consider::

1. (DEFAULT) Rasterize to the line render path or cells having a center point within the polygon

2. The ``ALL_TOUCHED`` strategy which rasterizes the geometry according to every cell that it touches. You can enable this specifying::

>>> zonal_stats(..., all_touched=True)

There is no right or wrong way to rasterize a vector; both approaches are valid and there are tradeoffs to consider. Using the default rasterizer may miss polygons that are smaller than your cell size. Using the ALL_TOUCHED strategy includes many cells along the edges that may not be representative of the geometry and give biased results when your geometries are much larger than your cell size.

Working with categorical rasters

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

You can treat rasters as categorical (i.e. raster values represent

discrete classes) if you're only interested in the counts of unique pixel

values.

For example, you may have a raster vegetation dataset and want to summarize

vegetation by polygon. Statistics such as mean, median, sum, etc. don't make much sense in this context

(What's the sum of ``oak + grassland``?).

The polygon below is comprised of 12 pixels of oak (raster value

32) and 78 pixels of grassland (raster value 33)::

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

>>> [{'__fid__': 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`` is key ``32``) for reporting.

"Mini-Rasters"

^^^^^^^^^^^^^^^

Internally, we create a masked raster dataset for each feature in order to

calculate statistics. Optionally, we can include these data in the output

of ``zonal_stats`` using the ``raster_out`` argument::

stats = zonal_stats(vector, raster, raster_out=True)

Which gives us three additional keys for each feature::

``mini_raster`` : Numpy ndarray

``mini_raster_GT`` : Six-tuple defining the geotransform (GDAL ordering)

``mini_raster_NDV`` : Nodata value in the returned array

Keep in mind that having ndarrays in your stats dictionary means it is more

difficult to serialize to json and other text formats.

Issues

------

Find a bug? Report it via github issues by providing

- a link to download the smallest possible raster and vector dataset necessary to reproduce the error

- python code or command to reproduce the error

- information on your environment: versions of python, gdal and numpy and system memory

.. |BuildStatus| image:: https://api.travis-ci.org/perrygeo/python-raster-stats.png

.. _BuildStatus: https://travis-ci.org/perrygeo/python-raster-stats

.. |CoverageStatus| image:: https://coveralls.io/repos/perrygeo/python-raster-stats/badge.png

.. _CoverageStatus: https://coveralls.io/r/perrygeo/python-raster-stats

.. |PyPiVersion| image:: https://pypip.in/v/rasterstats/badge.png

.. _PyPiVersion: http://pypi.python.org/pypi/rasterstats

.. |PyPiDownloads| image:: https://pypip.in/d/rasterstats/badge.png

.. _PyPiDownloads: http://pypi.python.org/pypi/rasterstats

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