Summarize geospatial raster datasets based on vector geometries
Project description
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
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:
>>> from rasterstats import raster_stats
>>> stats = raster_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 raster_stats function will return the following statistics
min
max
mean
count
Optionally, these statistics are also available
sum
std
median
majority
minority
unique
range
You can specify the statistics to calculate using the stats argument:
>>> stats = raster_stats("tests/data/polygons.shp",
"tests/data/elevation.tif"
stats=['min', 'max', 'median', 'majority', 'sum'])
>>> # also takes space-delimited string
>>> stats = raster_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.
Specifying Geometries
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')
...
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 = raster_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
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):
>>> raster_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 == 32) for reporting.
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
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