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A Python Protocol for Geospatial Data

Author: Sean Gillies Version: 1.0

Abstract

This document describes a GeoJSON-like protocol for geo-spatial (GIS) vector data.

Introduction

Python has a number of built-in protocols (descriptors, iterators, etc). A very simple and familiar one involves string representations of objects. The built-in str() function calls the __str__() method of its single argument. By implementing __str__(), instances of any class can be printed by any other Python program. :

>>> class A(object):
...     def __str__(self):
...         return "Eh!"
... 
>>> a = A()
>>> str(a)
'Eh!'
>>> "%s" % a
'Eh!'

What if we could do something like this for geo-spatial 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 some_analytic_module module would access relevant data of its single argument using an agreed upon method or attribute.


Following the lead of numpy's Array Interface1, 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 format2 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 geo-spatial 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

Python programs and packages that you have heard of – and made be a frequent user of – already implement this protocol:

  • ArcPy3
  • descartes4
  • geojson5
  • PySAL6

Shapely

Shapely7 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. https://tools.ietf.org/html/rfc7946

  3. https://desktop.arcgis.com/en/arcmap/latest/analyze/arcpy-functions/asshape.htm

  4. https://bitbucket.org/sgillies/descartes/src/f97e54f3b8d4/descartes/patch.py#cl-14

  5. http://pypi.python.org/pypi/geojson/

  6. https://pysal.readthedocs.io/en/latest/users/tutorials/shapely.html

  7. https://github.com/Toblerity/Shapely

@ChrisBarker-NOAA
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BTW: with regard to tuples vs lists:

Python has a very flexible type model -- so I'd think the way to go would be to say "sequence". I know I 'like to use Nx2 numpy arrays as a list of coordinates.

Or be a bit more confining and say "anything that you can pass into json.dump and get geoJSON out.

@eric-spitler
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@sgillies GeoPandas also uses the __geo_interface__ when loading Features into a GeoDataFrame
https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.from_features.html

@NoharaMasato
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Hello @sgillies, It looks like links for [4] and [6] in references are outdated.

@sthagen
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sthagen commented Apr 10, 2023

Hello @sgillies, It looks like links for [4] and [6] in references are outdated.

I would suggest to replace with as helper for random visitors (but Sean will know better replacements):

[4]: Descartes ... but then the source link at PyPI - a fork seems to be benjimin/descartes

[6]: Shapely tutorial per wayback machine snapshot of 2019-Jun-19 ... or try to map to whatever PySAL.org now offers ...

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