Library for performing queries and transformations on GeoJSON data (with emphasis on support for abstract graph representations).
Project description
Library for performing queries and transformations on GeoJSON data (with emphasis on support for abstract graph representations).
Package Installation and Usage
The package is available on PyPI:
python -m pip install geoql
The library can be imported in the usual ways:
import geoql from geoql import geoql
Examples
An example of usage is provided below:
import geojson
from geoql import geoql
import geoleaflet
import requests
url = 'https://raw.githubusercontent.com/Data-Mechanics/geoql/master/examples/'
# Boston ZIP Codes regions.
z = geoql.loads(requests.get(url + 'example_zips.geojson').text, encoding="latin-1")
# Extract of street data.
g = geoql.loads(requests.get(url + 'example_extract.geojson').text, encoding="latin-1")
g = g.properties_null_remove()\
.tags_parse_str_to_dict()\
.keep_by_property({"highway": {"$in": ["residential", "secondary", "tertiary"]}})
g = g.keep_within_radius((42.3551, -71.0656), 0.75, 'miles') # 0.75 miles from Boston Common.
g = g.keep_that_intersect(z) # Only those entries found in a Boston ZIP Code regions.
g = g.node_edge_graph() # Converted into a graph with nodes and edges.
g.dump(open('example_extract.geojson', 'w'))
open('leaflet.html', 'w').write(geoleaflet.html(g)) # Create visualization.
An alternative example of usage is provided below (the below usage is deprecated but will remain supported):
import geojson
import geoql
import geoleaflet
import requests
url = 'https://raw.githubusercontent.com/Data-Mechanics/geoql/master/examples/'
# Boston ZIP Codes regions.
z = geojson.loads(requests.get(url + 'example_zips.geojson').text, encoding="latin-1")
# Extract of street data.
g = geojson.loads(requests.get(url + 'example_extract.geojson').text, encoding="latin-1")
g = geoql.features_properties_null_remove(g)
g = geoql.features_tags_parse_str_to_dict(g)
g = geoql.features_keep_by_property(g, {"highway": {"$in": ["residential", "secondary", "tertiary"]}})
g = geoql.features_keep_within_radius(g, (42.3551, -71.0656), 0.75, 'miles') # Within 0.75 of Boston Common.
g = geoql.features_keep_intersecting_features(g, z) # Only those entries found in a Boston ZIP Code regions.
g = geoql.features_node_edge_graph(g) # Converted into a graph with nodes and edges.
open('example_extract.geojson', 'w').write(geojson.dumps(g))
open('leaflet.html', 'w').write(geoleaflet.html(g)) # Create visualization.
