https://github.com/yannforget/osmxtract

Fetch OpenStreetMap features and export them as GeoJSON.
https://github.com/yannforget/osmxtract

gis openstreetmap

Last synced: about 1 month ago
JSON representation

Fetch OpenStreetMap features and export them as GeoJSON.

• Host: GitHub
• URL: https://github.com/yannforget/osmxtract
• Owner: yannforget
• License: mit
• Created: 2018-06-12T18:36:21.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-06-06T11:03:03.000Z (over 5 years ago)
• Last Synced: 2024-10-05T11:08:07.643Z (3 months ago)
• Topics: gis, openstreetmap
• Language: Python
• Homepage:
• Size: 14.6 KB
• Stars: 17
• Watchers: 7
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
# OSMxtract

## Description

Note: in development, do not use in production.

**OSMxtract** is a simple Python package that uses the [Overpass API](https://wiki.openstreetmap.org/wiki/Overpass_API) to fetch [OpenStreetMap](https://www.openstreetmap.org) features and export them in a GeoJSON file.

## Installation

Using `pip`:

```sh

pip install osmxtract

```

## Command-line interface

### Usage

**OSMxtract** can guess the extent of your query based on three different options:

* `--fromfile`: use the bounds from the input vector or raster file ;

* `--latlon` and `--buffer`: use the bounds of a buffer around a given point ;

* `--address` and `--buffer`: use the bounds of a buffer around a geocoded address.

```

Usage: osmxtract [OPTIONS] OUTPUT

  Extract GeoJSON features from OSM with the Overpass API.

Options:

  --fromfile PATH                 Bounding box from input file.

  --latlon FLOAT...               Space-separated lat/lon coordinates.

  --address TEXT                  Address to geocode.

  --buffer INTEGER                Buffer size in meters around lat/lon or

                                  address.

  --tag TEXT                      OSM tag of interest (ex: "highway").

  --values TEXT                   Comma-separated list of possible values (ex:

                                  "tertiary,primary").

  --case-insensitive              Make the first character of each value case

                                  insensitive.

  --geom [point|linestring|polygon|multipolygon]

                                  Output geometry type.

  --help                          Show this message and exit.

```

### Examples

```bash

# buildings around the "Université Libre de Bruxelles" as polygons

# save features in the file `buildings.geojson`. since no values

# are provided, all non-null values are accepted for the tag

# "highway" are accepted.

osmxtract --address "Université Libre de Bruxelles" --buffer 5000 \

          --tag building --geom polygon buildings.geojson

# primary, secondary and tertiary roads based on the extent

# of an existing raster. save the result as linestrings in the

# `major_roads.geojson` file. we use the `--case-insensitive`

# flag to get roads tagged as "primary" as well as "Primary".

osmxtract --fromfile map.tif --tag highway \

          --values "primary,secondary,tertiary" \

          --case-insensitive --geom linestring \

          major_roads.geojson

# cafes and bars near "Atomium, Brussels" 

osmxtract --address "atomium, brussels" --buffer 1000 \

          --tag amenity --values "cafe,bar" --geom point \

          cafes_and_bars.geojson

```

## API

``` python

import json

from osmxtract import overpass, location

import geopandas as gpd

# Get bounding box coordinates from a 2km buffer

# around the Atomium in Brussels

lat, lon = location.geocode('Atomium, Brussels')

bounds = location.from_buffer(lat, lon, buffer_size=2000)

# Build an overpass QL query and get the JSON response

query = overpass.ql_query(bounds, tag='amenity', values=['cafe', 'bar'])

response = overpass.request(query)

# Process response manually...

for elem in response['elements']:

    print(elem['tags'].get('name'))

# Output:

# Au Bon Coin

# Aux 4 Coins du Monde

# Excelsior

# Welcome II

# Heymbos

# Games Café

# Stadium

# Le Beau Rivage

# The Corner

# None

# Expo

# Koning

# Centrum

# St. Amands

# Bij Manu

# ...or parse them as GeoJSON

feature_collection = overpass.as_geojson(response, 'point')

# Write as GeoJSON

with open('cafes_and_bars.geojson', 'w') as f:

    json.dump(feature_collection, f)

# To GeoPandas GeoDataFrame:

geodataframe = gpd.GeoDataFrame.from_features(feature_collection)

```