用于对geojson数据执行查询和转换的库（重点是支持抽象图形表示）。
geoql的Python项目详细描述
用于对geojson数据执行查询和转换的库（重点是支持抽象图形表示）。
软件包安装和使用
PYPI上提供该软件包：
python -m pip install geoql
库可以用通常的方式导入：
import geoql from geoql import geoql
示例
用法示例如下：
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": {"$in": ["residential", "secondary", "tertiary"]}})
g = g.keep_within_radius((42.3551, -71.0656), 0.75, 'miles') # 0.75 miles from Boston Common.
g = g.keep_that_intersect(z) # Only those entries found in a Boston ZIP Code regions.
g = g.node_edge_graph() # Converted into a graph with nodes and edges.
g.dump(open('example_extract.geojson', 'w'))
open('leaflet.html', 'w').write(geoleaflet.html(g)) # Create visualization.
下面提供了另一个用法示例（下面的用法已弃用，但仍受支持）：
import geojson
import geoql
import geoleaflet
import requests
url = 'https://raw.githubusercontent.com/Data-Mechanics/geoql/master/examples/'
# Boston ZIP Codes regions.
z = geojson.loads(requests.get(url + 'example_zips.geojson').text, encoding="latin-1")
# Extract of street data.
g = geojson.loads(requests.get(url + 'example_extract.geojson').text, encoding="latin-1")
g = geoql.features_properties_null_remove(g)
g = geoql.features_tags_parse_str_to_dict(g)
g = geoql.features_keep_by_property(g, {"highway": {"$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.
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