sgillies/geo_interface.rst

## __geo_interface__.rst

      
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              __geo_interface__.rst
            
          
Abstract

This document describes a GeoJSON-like protocol for geospatial vector data.
Introduction
============Author: Sean Gillies

Abstract

This document describes a GeoJSON-like protocol for geospatial vector data.

Introduction

Python has a number of built-in protocols (descriptor, iterator, etc). A very
simple one involves string representations of objects. The built-in str()
function calls the __str__() method of its single argument. Example:
>>> class A(object):
...     def __str__(self):
...         return "Eh!"
...
>>> a = A()
>>> str(a)
'Eh!'
>>> "%s" % a
'Eh!'

By implementing __str__(), instances of any class can be printed by any
other Python program. What if we could do something like this for geospatial
(GIS) objects? It might, for example, let any object be analyzed using any
other hypothetical software package like this:
>>> from some_analytic_module import as_geometry
>>> as_geometry(obj).buffer(1.0).area   # obj is a "point" of some kind
3.1365484905459389

The hypothetical as_geometry() function of the hypothetical module would
access relevant data of its single argument using an agreed upon method or
attribute.

__geo_interface__

Following the lead of numpy's Array Interface [1]_, let's agree on
a __geo_interface__ property. To avoid creating even more protocols, let's
make the value of this attribute a Python mapping. To further minimize
invention, let's borrow from the GeoJSON format [2]_ for the structure of this
mapping.
The keys are:

type (required)
A string indicating the geospatial type. Possible values are "Feature" or
a geometry type: "Point", "LineString", "Polygon", etc.
bbox (optional)
A tuple of floats that describes the geospatial bounds of the object: (left,
bottom, right, top) or (west, south, east, north).
properties (optional)
A mapping of feature properties (labels, populations ... you name it.
Dependent on the data). Valid for "Feature" types only.
geometry (optional)
The geometric object of a "Feature" type, also as a mapping.
coordinates (required)
Valid only for geometry types. This is an (x, y) or (longitude,
latitude) tuple in the case of a "Point", a list of such tuples in the
"LineString" case, or a list of lists in the "Polygon" case. See the GeoJSON
spec for details.


Examples

First, a toy class with a point representation:
>>> class Pointy(object):
...     __geo_interface__ = {'type': 'Point', 'coordinates': (0.0, 0.0)}
...
>>> as_geometry(Pointy()).buffer(1.0).area
3.1365484905459389

Next, a toy class with a feature representation:
>>> class Placemark(object):
...     __geo_interface__ = {
...         'type': 'Feature',
...         'properties': {'name': 'Phoo'},
...         'geometry': Pointy.__geo_interface__ }
>>> from my_analytic_module import as_feature
>>> as_feature(Placemark())['properties']['name']
'Phoo'


Implementations


ArcPy
descartes
PySAL


Shapely

Shapely [3]_ provides a shape() function that makes Shapely geometries from
objects that provide __geo_interface__ and a mapping() function that
writes geometries out as dictionaries:
>>> from shapely.geometry import Point
>>> from shapely.geometry import mapping, shape
>>> Point(0.0, 0.0).__geo_interface__
{'type': 'Point', 'coordinates': (0.0, 0.0)}
>>> shape(Point(0.0, 0.0))
<shapely.geometry.point.Point object at 0x...>
>>> mapping(Point(0.0, 0.0))
{'type': 'Point', 'coordinates': (0.0, 0.0)}

The Shapely version of the example in the introduction is:
>>> from shapely.geometry import shape
>>> shape(obj).buffer(1.0).area
3.1365484905459389

where obj could be a geometry object from ArcPy or PySAL, or even a mapping
directly:
>>> shape({'type': 'Point', 'coordinates': (0.0, 0.0)}).buffer(1.0).area
3.1365484905459389


References


[1] http://docs.scipy.org/doc/numpy/reference/arrays.interface.html


[2] http://geojson.org


[3] https://github.com/Toblerity/Shapely


Python has a number of built-in protocols (descriptor, iterator, etc). A very simple one involves string representations of objects. The built-in str() function calls the __str__() method of its single argument. Example:
>>> class A(object):
...     def __str__(self):
...         return "Eh!"
...
>>> a = A()
>>> str(a)
'Eh!'
>>> "%s" % a
'Eh!'

By implementing __str__(), instances of any class can be printed by any other Python program. What if we could do something like this for geospatial (GIS) objects? It might, for example, let any object be analyzed using any other hypothetical software package like
this:
>>> from some_analytic_module import as_geometry
>>> as_geometry(obj).buffer(1.0).area   # obj is a "point" of some kind
3.1365484905459389

The hypothetical as_geometry() function of the hypothetical module would access relevant data of its single argument using an agreed upon method or attribute.

__geo_interface__

Following the lead of numpy's Array Interface [1]_, let's agree on a __geo_interface__ property. To avoid creating
even more protocols, let's make the value of this attribute a Python mapping. To further minimize invention, let's
borrow from the GeoJSON format [2]_ for the structure of this mapping.
The keys are:

type (required)
A string indicating the geospatial type. Possible values are "Feature" or a geometry type: "Point", "LineString", "Polygon", etc.
bbox (optional)
A tuple of floats that describes the geospatial bounds of the object: (left, bottom, right, top) or (west, south, east, north).
properties (optional)
A mapping of feature properties (labels, populations ... you name it. Dependent on the data). Valid for "Feature" types only.
geometry (optional)
The geometric object of a "Feature" type, also as a mapping.
coordinates (required)
Valid only for geometry types. This is an (x, y) or (longitude, latitude) tuple in the case of a "Point", a list of such tuples in the "LineString" case, or a list of lists in the "Polygon" case. See the GeoJSON spec for details.


Examples

First, a toy class with a point representation:
>>> class Pointy(object):
...     __geo_interface__ = {'type': 'Point', 'coordinates': (0.0, 0.0)}
...
>>> as_geometry(Pointy()).buffer(1.0).area
3.1365484905459389

Next, a toy class with a feature representation:
>>> class Placemark(object):
...     __geo_interface__ = {
...         'type': 'Feature',
...         'properties': {'name': 'Phoo'},
...         'geometry': Pointy.__geo_interface__ }
>>> from my_analytic_module import as_feature
>>> as_feature(Placemark())['properties']['name']
'Phoo'


Implementations


ArcPy
descartes
PySAL


Shapely

Shapely [3]_ provides a shape() function that makes Shapely geometries from objects
that provide __geo_interface__ and a mapping() function that writes geometries
out as dictionaries:
>>> from shapely.geometry import Point
>>> from shapely.geometry import mapping, shape
>>> Point(0.0, 0.0).__geo_interface__
{'type': 'Point', 'coordinates': (0.0, 0.0)}
>>> shape(Point(0.0, 0.0))
<shapely.geometry.point.Point object at 0x...>
>>> mapping(Point(0.0, 0.0))
{'type': 'Point', 'coordinates': (0.0, 0.0)}

The Shapely version of the example in the introduction is:
>>> from shapely.geometry import shape
>>> shape(obj).buffer(1.0).area
3.1365484905459389

where obj could be a geometry object from ArcPy or PySAL, or even a mapping directly:
>>> shape({'type': 'Point', 'coordinates': (0.0, 0.0)}).buffer(1.0).area
3.1365484905459389


References


[1] http://docs.scipy.org/doc/numpy/reference/arrays.interface.html


[2] http://geojson.org


[3] https://github.com/Toblerity/Shapely