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

Logging the position of the car on an sdcard

audi-mmi-3g-gps-logging Logging the position of the car on an sdcard, startup script origin not clear to me, logging setup and time change is what I d

2 May 31, 2022
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
Mmdb-server - An open source fast API server to lookup IP addresses for their geographic location

mmdb-server mmdb-server is an open source fast API server to lookup IP addresses

Alexandre Dulaunoy 67 Nov 25, 2022
A package built to support working with spatial data using open source python

EarthPy EarthPy makes it easier to plot and manipulate spatial data in Python. Why EarthPy? Python is a generic programming language designed to suppo

Earth Lab 414 Dec 23, 2022
Download and process satellite imagery in Python using Sentinel Hub services.

Description The sentinelhub Python package allows users to make OGC (WMS and WCS) web requests to download and process satellite images within your Py

Sentinel Hub 659 Dec 23, 2022
Hapi is a Python library for building Conceptual Distributed Model using HBV96 lumped model & Muskingum routing method

Current build status All platforms: Current release info Name Downloads Version Platforms Hapi - Hydrological library for Python Hapi is an open-sourc

Mostafa Farrag 15 Dec 26, 2022
Software for Advanced Spatial Econometrics

GeoDaSpace Software for Advanced Spatial Econometrics GeoDaSpace current version 1.0 (32-bit) Development environment: Mac OSX 10.5.x (32-bit) wxPytho

GeoDa Center 38 Jan 03, 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
Track International space station with python

NASA-ISS-tracker Track International space station with python Modules import json import turtle import urllib.request import time import webbrowser i

Nikhil Yadav 8 Aug 12, 2021
GetOSM is an OpenStreetMap tile downloader written in Python that is agnostic of GUI frameworks.

GetOSM GetOSM is an OpenStreetMap tile downloader written in Python that is agnostic of GUI frameworks. It is used with tkinter by ProjPicker. Require

Huidae Cho 3 May 20, 2022
A Python framework for building geospatial web-applications

Hey there, this is Greppo... A Python framework for building geospatial web-applications. Greppo is an open-source Python framework that makes it easy

Greppo 304 Dec 27, 2022
Spatial Interpolation Toolbox is a Python-based GUI that is able to interpolate spatial data in vector format.

Spatial Interpolation Toolbox This is the home to Spatial Interpolation Toolbox, a graphical user interface (GUI) for interpolating geographic vector

Michael Ward 2 Nov 01, 2021
Python script to locate mobile number

Python script to locate mobile number How to use this script run the command to install the required libraries pip install -r requirements.txt run the

Shekhar Gupta 8 Oct 10, 2022
A GUI widget for Linux to show current time in different timezones.

A GUI widget to show current time in different timezones (under development). To use this widget: Run scripts/startup.py Select a country. A list of t

B.Jothin kumar 11 Nov 10, 2022
GebPy is a Python-based, open source tool for the generation of geological data of minerals, rocks and complete lithological sequences.

GebPy is a Python-based, open source tool for the generation of geological data of minerals, rocks and complete lithological sequences. The data can be generated randomly or with respect to user-defi

Maximilian Beeskow 16 Nov 29, 2022
Tool to suck data from ArcGIS Server and spit it into PostgreSQL

chupaESRI About ChupaESRI is a Python module/command line tool to extract features from ArcGIS Server map services. Name? Think "chupacabra" or "Chupa

John Reiser 34 Dec 04, 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
This program analizes films database with adresses, and creates a folium map with closest films to the coordinates

Films-map-project UCU CS lab 1.2, 1st year This program analizes films database with adresses, and creates a folium map with closest films to the coor

Artem Moskovets 1 Feb 09, 2022
Using SQLAlchemy with spatial databases

GeoAlchemy GIS Support for SQLAlchemy. Introduction GeoAlchemy is an extension of SQLAlchemy. It provides support for Geospatial data types at the ORM

109 Dec 01, 2022
A utility to search, download and process Landsat 8 satellite imagery

Landsat-util Landsat-util is a command line utility that makes it easy to search, download, and process Landsat imagery. Docs For full documentation v

Development Seed 681 Dec 07, 2022