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

When traveling in the backcountry during winter time, updating yourself on current and recent weather data is important to understand likely avalanche danger.

Weather Data When traveling in the backcountry during winter time, updating yourself on current and recent weather data is important to understand lik

Trevor Allen 0 Jan 02, 2022
OSMnx: Python for street networks. Retrieve, model, analyze, and visualize street networks and other spatial data from OpenStreetMap.

OSMnx OSMnx is a Python package that lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street

Geoff Boeing 4k Jan 08, 2023
Imperial Valley Geomorphology Map

Roughly maps the extent of basins, basin edges, and mountains in the Imperial Valley by grouping terrain classes from the Iwahashi et al. 2021 California terrian classification model.

0 Dec 13, 2022
pure-Python (Numpy optional) 3D coordinate conversions for geospace ecef enu eci

Python 3-D coordinate conversions Pure Python (no prerequistes beyond Python itself) 3-D geographic coordinate conversions and geodesy. API similar to

Geospace code 292 Dec 29, 2022
Geographic add-ons for Django REST Framework. Maintained by the OpenWISP Project.

django-rest-framework-gis Geographic add-ons for Django Rest Framework - Mailing List. Install last stable version from pypi pip install djangorestfra

OpenWISP 981 Jan 03, 2023
iNaturalist observations along hiking trails

This tool reads the route of a hike and generates a table of iNaturalist observations along the trails. It also shows the observations and the route of the hike on a map. Moreover, it saves waypoints

7 Nov 11, 2022
EOReader is a multi-satellite reader allowing you to open optical and SAR data.

Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index.

ICube-SERTIT 152 Dec 30, 2022
A bot that tweets info and location map for new bicycle parking added to OpenStreetMap within a GeoJSON boundary.

Bike parking tweepy bot app A twitter bot app that searches for bicycle parking added to OpenStreetMap. Relies on AWS Lambda/S3, Python3, Tweepy, Flas

Angelo Trivisonno 1 Dec 19, 2021
r.cfdtools 7 Dec 28, 2022
Get-countries-info - A python code that fetches data of any country

Country-info A python code getting countries information including country's map

CODE 2 Feb 21, 2022
Python bindings and utilities for GeoJSON

geojson This Python library contains: Functions for encoding and decoding GeoJSON formatted data Classes for all GeoJSON Objects An implementation of

Jazzband 765 Jan 06, 2023
WebGL2 powered geospatial visualization layers

deck.gl | Website WebGL2-powered, highly performant large-scale data visualization deck.gl is designed to simplify high-performance, WebGL-based visua

Vis.gl 10.5k Jan 08, 2023
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

A ready-to-use curated list of Spectral Indices for Remote Sensing applications. GitHub: https://github.com/davemlz/awesome-ee-spectral-indices Docume

David Montero Loaiza 488 Jan 03, 2023
ArcGIS Python Toolbox for WhiteboxTools

WhiteboxTools-ArcGIS ArcGIS Python Toolbox for WhiteboxTools. This repository is related to the ArcGIS Python Toolbox for WhiteboxTools, which is an A

Qiusheng Wu 190 Dec 30, 2022
LicenseLocation - License Location With Python

LicenseLocation Hi,everyone! ❤ 🧡 💛 💚 💙 💜 This is my first project! ✔ Actual

The Bin 1 Jan 25, 2022
iNaturalist observations along hiking trails

This tool reads the route of a hike and generates a table of iNaturalist observations along the trails. It also shows the observations and the route of the hike on a map. Moreover, it saves waypoints

7 Nov 11, 2022
Processing and interpolating spatial data with a twist of machine learning

Documentation | Documentation (dev version) | Contact | Part of the Fatiando a Terra project About Verde is a Python library for processing spatial da

Fatiando a Terra 468 Dec 20, 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
This is a simple python code to get IP address and its location using python

IP address & Location finder @DEV/ED : Pavan Ananth Sharma Dependencies: ip2geotools Note: use pip install ip2geotools to install this in your termin

Pavan Ananth Sharma 2 Jul 05, 2022
Get Landsat surface reflectance time-series from google earth engine

geextract Google Earth Engine data extraction tool. Quickly obtain Landsat multispectral time-series for exploratory analysis and algorithm testing On

Loïc Dutrieux 50 Dec 15, 2022