Python bindings and utilities for GeoJSON

Overview

geojson

GitHub Actions Codecov Jazzband

This Python library contains:

Table of Contents

Installation

geojson is compatible with Python 3.6 - 3.9. The recommended way to install is via pip:

pip install geojson

GeoJSON Objects

This library implements all the GeoJSON Objects described in The GeoJSON Format Specification.

All object keys can also be used as attributes.

The objects contained in GeometryCollection and FeatureCollection can be indexed directly.

Point

>>> from geojson import Point

>>> Point((-115.81, 37.24))  # doctest: +ELLIPSIS
{"coordinates": [-115.8..., 37.2...], "type": "Point"}

Visualize the result of the example above here. General information about Point can be found in Section 3.1.2 and Appendix A: Points within The GeoJSON Format Specification.

MultiPoint

>>> from geojson import MultiPoint

>>> MultiPoint([(-155.52, 19.61), (-156.22, 20.74), (-157.97, 21.46)])  # doctest: +ELLIPSIS
{"coordinates": [[-155.5..., 19.6...], [-156.2..., 20.7...], [-157.9..., 21.4...]], "type": "MultiPoint"}

Visualize the result of the example above here. General information about MultiPoint can be found in Section 3.1.3 and Appendix A: MultiPoints within The GeoJSON Format Specification.

LineString

>>> from geojson import LineString

>>> LineString([(8.919, 44.4074), (8.923, 44.4075)])  # doctest: +ELLIPSIS
{"coordinates": [[8.91..., 44.407...], [8.92..., 44.407...]], "type": "LineString"}

Visualize the result of the example above here. General information about LineString can be found in Section 3.1.4 and Appendix A: LineStrings within The GeoJSON Format Specification.

MultiLineString

>>> from geojson import MultiLineString

>>> MultiLineString([
...     [(3.75, 9.25), (-130.95, 1.52)],
...     [(23.15, -34.25), (-1.35, -4.65), (3.45, 77.95)]
... ])  # doctest: +ELLIPSIS
{"coordinates": [[[3.7..., 9.2...], [-130.9..., 1.52...]], [[23.1..., -34.2...], [-1.3..., -4.6...], [3.4..., 77.9...]]], "type": "MultiLineString"}

Visualize the result of the example above here. General information about MultiLineString can be found in Section 3.1.5 and Appendix A: MultiLineStrings within The GeoJSON Format Specification.

Polygon

>>> from geojson import Polygon

>>> # no hole within polygon
>>> Polygon([[(2.38, 57.322), (23.194, -20.28), (-120.43, 19.15), (2.38, 57.322)]])  # doctest: +ELLIPSIS
{"coordinates": [[[2.3..., 57.32...], [23.19..., -20.2...], [-120.4..., 19.1...]]], "type": "Polygon"}

>>> # hole within polygon
>>> Polygon([
...     [(2.38, 57.322), (23.194, -20.28), (-120.43, 19.15), (2.38, 57.322)],
...     [(-5.21, 23.51), (15.21, -10.81), (-20.51, 1.51), (-5.21, 23.51)]
... ])  # doctest: +ELLIPSIS
{"coordinates": [[[2.3..., 57.32...], [23.19..., -20.2...], [-120.4..., 19.1...]], [[-5.2..., 23.5...], [15.2..., -10.8...], [-20.5..., 1.5...], [-5.2..., 23.5...]]], "type": "Polygon"}

Visualize the results of the example above here. General information about Polygon can be found in Section 3.1.6 and Appendix A: Polygons within The GeoJSON Format Specification.

MultiPolygon

>>> from geojson import MultiPolygon

>>> MultiPolygon([
...     ([(3.78, 9.28), (-130.91, 1.52), (35.12, 72.234), (3.78, 9.28)],),
...     ([(23.18, -34.29), (-1.31, -4.61), (3.41, 77.91), (23.18, -34.29)],)
... ])  # doctest: +ELLIPSIS
{"coordinates": [[[[3.7..., 9.2...], [-130.9..., 1.5...], [35.1..., 72.23...]]], [[[23.1..., -34.2...], [-1.3..., -4.6...], [3.4..., 77.9...]]]], "type": "MultiPolygon"}

Visualize the result of the example above here. General information about MultiPolygon can be found in Section 3.1.7 and Appendix A: MultiPolygons within The GeoJSON Format Specification.

GeometryCollection

>>> from geojson import GeometryCollection, Point, LineString

>>> my_point = Point((23.532, -63.12))

>>> my_line = LineString([(-152.62, 51.21), (5.21, 10.69)])

>>> geo_collection = GeometryCollection([my_point, my_line])

>>> geo_collection  # doctest: +ELLIPSIS
{"geometries": [{"coordinates": [23.53..., -63.1...], "type": "Point"}, {"coordinates": [[-152.6..., 51.2...], [5.2..., 10.6...]], "type": "LineString"}], "type": "GeometryCollection"}

>>> geo_collection[1]
{"coordinates": [[-152.62, 51.21], [5.21, 10.69]], "type": "LineString"}

>>> geo_collection[0] == geo_collection.geometries[0]
True

Visualize the result of the example above here. General information about GeometryCollection can be found in Section 3.1.8 and Appendix A: GeometryCollections within The GeoJSON Format Specification.

Feature

>>> from geojson import Feature, Point

>>> my_point = Point((-3.68, 40.41))

>>> Feature(geometry=my_point)  # doctest: +ELLIPSIS
{"geometry": {"coordinates": [-3.68..., 40.4...], "type": "Point"}, "properties": {}, "type": "Feature"}

>>> Feature(geometry=my_point, properties={"country": "Spain"})  # doctest: +ELLIPSIS
{"geometry": {"coordinates": [-3.68..., 40.4...], "type": "Point"}, "properties": {"country": "Spain"}, "type": "Feature"}

>>> Feature(geometry=my_point, id=27)  # doctest: +ELLIPSIS
{"geometry": {"coordinates": [-3.68..., 40.4...], "type": "Point"}, "id": 27, "properties": {}, "type": "Feature"}

Visualize the results of the examples above here. General information about Feature can be found in Section 3.2 within The GeoJSON Format Specification.

FeatureCollection

>>> from geojson import Feature, Point, FeatureCollection

>>> my_feature = Feature(geometry=Point((1.6432, -19.123)))

>>> my_other_feature = Feature(geometry=Point((-80.234, -22.532)))

>>> feature_collection = FeatureCollection([my_feature, my_other_feature])

>>> feature_collection # doctest: +ELLIPSIS
{"features": [{"geometry": {"coordinates": [1.643..., -19.12...], "type": "Point"}, "properties": {}, "type": "Feature"}, {"geometry": {"coordinates": [-80.23..., -22.53...], "type": "Point"}, "properties": {}, "type": "Feature"}], "type": "FeatureCollection"}

>>> feature_collection.errors()
[]

>>> (feature_collection[0] == feature_collection['features'][0], feature_collection[1] == my_other_feature)
(True, True)

Visualize the result of the example above here. General information about FeatureCollection can be found in Section 3.3 within The GeoJSON Format Specification.

GeoJSON encoding/decoding

All of the GeoJSON Objects implemented in this library can be encoded and decoded into raw GeoJSON with the geojson.dump, geojson.dumps, geojson.load, and geojson.loads functions. Note that each of these functions is a wrapper around the core json function with the same name, and will pass through any additional arguments. This allows you to control the JSON formatting or parsing behavior with the underlying core json functions.

>>> import geojson

>>> my_point = geojson.Point((43.24, -1.532))

>>> my_point  # doctest: +ELLIPSIS
{"coordinates": [43.2..., -1.53...], "type": "Point"}

>>> dump = geojson.dumps(my_point, sort_keys=True)

>>> dump  # doctest: +ELLIPSIS
'{"coordinates": [43.2..., -1.53...], "type": "Point"}'

>>> geojson.loads(dump)  # doctest: +ELLIPSIS
{"coordinates": [43.2..., -1.53...], "type": "Point"}

Custom classes

This encoding/decoding functionality shown in the previous can be extended to custom classes using the interface described by the __geo_interface__ Specification.

>>> import geojson

>>> class MyPoint():
...     def __init__(self, x, y):
...         self.x = x
...         self.y = y
...
...     @property
...     def __geo_interface__(self):
...         return {'type': 'Point', 'coordinates': (self.x, self.y)}

>>> point_instance = MyPoint(52.235, -19.234)

>>> geojson.dumps(point_instance, sort_keys=True)  # doctest: +ELLIPSIS
'{"coordinates": [52.23..., -19.23...], "type": "Point"}'

Default and custom precision

GeoJSON Object-based classes in this package have an additional precision attribute which rounds off coordinates to 6 decimal places (roughly 0.1 meters) by default and can be customized per object instance.

>>> from geojson import Point

>>> Point((-115.123412341234, 37.123412341234))  # rounded to 6 decimal places by default
{"coordinates": [-115.123412, 37.123412], "type": "Point"}

>>> Point((-115.12341234, 37.12341234), precision=8)  # rounded to 8 decimal places
{"coordinates": [-115.12341234, 37.12341234], "type": "Point"}

Helpful utilities

coords

geojson.utils.coords yields all coordinate tuples from a geometry or feature object.

>>> import geojson

>>> my_line = LineString([(-152.62, 51.21), (5.21, 10.69)])

>>> my_feature = geojson.Feature(geometry=my_line)

>>> list(geojson.utils.coords(my_feature))  # doctest: +ELLIPSIS
[(-152.62..., 51.21...), (5.21..., 10.69...)]

map_coords

geojson.utils.map_coords maps a function over all coordinate values and returns a geometry of the same type. Useful for scaling a geometry.

>>> import geojson

>>> new_point = geojson.utils.map_coords(lambda x: x/2, geojson.Point((-115.81, 37.24)))

>>> geojson.dumps(new_point, sort_keys=True)  # doctest: +ELLIPSIS
'{"coordinates": [-57.905..., 18.62...], "type": "Point"}'

map_tuples

geojson.utils.map_tuples maps a function over all coordinates and returns a geometry of the same type. Useful for changing coordinate order or applying coordinate transforms.

>>> import geojson

>>> new_point = geojson.utils.map_tuples(lambda c: (c[1], c[0]), geojson.Point((-115.81, 37.24)))

>>> geojson.dumps(new_point, sort_keys=True)  # doctest: +ELLIPSIS
'{"coordinates": [37.24..., -115.81], "type": "Point"}'

map_geometries

geojson.utils.map_geometries maps a function over each geometry in the input.

>>> import geojson

>>> new_point = geojson.utils.map_geometries(lambda g: geojson.MultiPoint([g["coordinates"]]), geojson.GeometryCollection([geojson.Point((-115.81, 37.24))]))

>>> geojson.dumps(new_point, sort_keys=True)
'{"geometries": [{"coordinates": [[-115.81, 37.24]], "type": "MultiPoint"}], "type": "GeometryCollection"}'

validation

is_valid property provides simple validation of GeoJSON objects.

>>> import geojson

>>> obj = geojson.Point((-3.68,40.41,25.14,10.34))
>>> obj.is_valid
False

errors method provides collection of errors when validation GeoJSON objects.

>>> import geojson

>>> obj = geojson.Point((-3.68,40.41,25.14,10.34))
>>> obj.errors()
'a position must have exactly 2 or 3 values'

generate_random

geojson.utils.generate_random yields a geometry type with random data

>>> import geojson

>>> geojson.utils.generate_random("LineString")  # doctest: +ELLIPSIS
{"coordinates": [...], "type": "LineString"}

>>> geojson.utils.generate_random("Polygon")  # doctest: +ELLIPSIS
{"coordinates": [...], "type": "Polygon"}

Development

To build this project, run python setup.py build. To run the unit tests, run python setup.py test. To run the style checks, run flake8 (install flake8 if needed).

Credits

Automated download of LANDSAT data from USGS website

LANDSAT-Download It seems USGS has changed the structure of its data, and so far, I have not been able to find the direct links to the products? Help

Olivier Hagolle 197 Dec 30, 2022
A library to access OpenStreetMap related services

OSMPythonTools The python package OSMPythonTools provides easy access to OpenStreetMap (OSM) related services, among them an Overpass endpoint, Nomina

Franz-Benjamin Mocnik 342 Dec 31, 2022
Replace MSFS2020's bing map to google map

English verison here 中文 免责声明 本教程提到的方法仅用于研究和学习用途。我不对使用、拓展该教程及方法所造成的任何法律责任和损失负责。 背景 微软模拟飞行2020的地景使用了Bing的卫星地图,然而卫星地图比较老旧,很多地区都是几年前的图设置直接是没有的。这种现象在全球不同地区

hesicong 272 Dec 24, 2022
Blender addons to make the bridge between Blender and geographic data

Blender GIS Blender minimal version : 2.8 Mac users warning : currently the addon does not work on Mac with Blender 2.80 to 2.82. Please do not report

5.9k Jan 02, 2023
Read images to numpy arrays

mahotas-imread: Read Image Files IO with images and numpy arrays. Mahotas-imread is a simple module with a small number of functions: imread Reads an

Luis Pedro Coelho 67 Jan 07, 2023
Python bindings to libpostal for fast international address parsing/normalization

pypostal These are the official Python bindings to https://github.com/openvenues/libpostal, a fast statistical parser/normalizer for street addresses

openvenues 651 Dec 16, 2022
Location field and widget for Django. It supports Google Maps, OpenStreetMap and Mapbox

django-location-field Let users pick locations using a map widget and store its latitude and longitude. Stable version: django-location-field==2.1.0 D

Caio Ariede 481 Dec 29, 2022
Tile Map Service and OGC Tiles API for QGIS Server

Tiles API Add tiles API to QGIS Server Tiles Map Service API OGC Tiles API Tile Map Service API - TMS The TMS API provides these URLs: /tms/? to get i

3Liz 6 Dec 01, 2021
GeoIP Legacy Python API

MaxMind GeoIP Legacy Python Extension API Requirements Python 2.5+ or 3.3+ GeoIP Legacy C Library 1.4.7 or greater Installation With pip: $ pip instal

MaxMind 230 Nov 10, 2022
A Django application that provides country choices for use with forms, flag icons static files, and a country field for models.

Django Countries A Django application that provides country choices for use with forms, flag icons static files, and a country field for models. Insta

Chris Beaven 1.2k Jan 03, 2023
python toolbox for visualizing geographical data and making maps

geoplotlib is a python toolbox for visualizing geographical data and making maps data = read_csv('data/bus.csv') geoplotlib.dot(data) geoplotlib.show(

Andrea Cuttone 976 Dec 11, 2022
Calculate the area inside of any GeoJSON geometry. This is a port of Mapbox's geojson-area for Python

geojson-area Calculate the area inside of any GeoJSON geometry. This is a port of Mapbox's geojson-area for Python. Installation $ pip install area U

Alireza 87 Dec 14, 2022
Computer Vision in Python

Mahotas Python Computer Vision Library Mahotas is a library of fast computer vision algorithms (all implemented in C++ for speed) operating over numpy

Luis Pedro Coelho 792 Dec 20, 2022
Fiona reads and writes geographic data files

Fiona Fiona reads and writes geographic data files and thereby helps Python programmers integrate geographic information systems with other computer s

987 Jan 04, 2023
Construct and use map tile grids in different projection.

Morecantile +-------------+-------------+ ymax | | | | x: 0 | x: 1 | | y: 0 | y: 0

Development Seed 67 Dec 23, 2022
Pure Python NetCDF file reader and writer

Pyncf Pure Python NetCDF file reading and writing. Introduction Inspired by the pyshp library, which provides simple pythonic and dependency free data

Karim Bahgat 14 Sep 30, 2022
Minimum Bounding Box of Geospatial data

BBOX Problem definition: The spatial data users often are required to obtain the coordinates of the minimum bounding box of vector and raster data in

Ali Khosravi Kazazi 1 Sep 08, 2022
Summary statistics of geospatial raster datasets based on vector geometries.

rasterstats rasterstats is a Python module for summarizing geospatial raster datasets based on vector geometries. It includes functions for zonal stat

Matthew Perry 437 Dec 23, 2022
User friendly Rasterio plugin to read raster datasets.

rio-tiler User friendly Rasterio plugin to read raster datasets. Documentation: https://cogeotiff.github.io/rio-tiler/ Source Code: https://github.com

372 Dec 23, 2022
ESMAC diags - Earth System Model Aerosol-Cloud Diagnostics Package

Earth System Model Aerosol-Cloud Diagnostics Package This Earth System Model (ES

Pacific Northwest National Laboratory 1 Jan 04, 2022