using osm conflate, cf_audit and josm to syndicate and moderate open spatial data releases to osm
https://wiki.openstreetmap.org/wiki/Import/London/Greater_London_Authority/Charging_Stations
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84 lines
3.6 KiB
84 lines
3.6 KiB
# profile to use with OSM_Conflator
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# https://wiki.openstreetmap.org/wiki/OSM_Conflator
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# examples at https://github.com/mapsme/osm_conflate/tree/master/profiles
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# command to use:
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# conflate --changes preview-improved-version.json --output results-improved-version.osm charging-stations-improved-version.py
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# source data preview
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# https://maps.london.gov.uk/geoserver/gis/wms?service=WMS&version=1.1.0&request=GetMap&layers=gis%3Alondon_charging_stations&bbox=-0.49845770990034466%2C51.29667980345268%2C0.2505595121779243%2C51.68268223925517&width=768&height=395&srs=EPSG%3A4326&format=application/openlayers
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# some related tags for charging stations
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# operator=*
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# capacity=*
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# ref=*
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# socket=*
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# amperage=*
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# voltage=*
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# fee=yes/no
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# parking:fee=yes/no/interval
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# opening_hours=*
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# payment=*
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# source tag is audit trail back to originator
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source = 'greater-london-authority'
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# A fairly unique id of the dataset to query OSM, used for "ref:mos_parking" tags
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# If you omit it, set explicitly "no_dataset_id = True"
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dataset_id = 'london_charging_stations'
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# Tags for querying with overpass api
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query = [('amenity', 'charging_station')]
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# Use bbox from dataset points (default). False = query whole world, [minlat, minlon, maxlat, maxlon] to override
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bbox = True
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# How close OSM point should be to register a match, in meters. Default is 100
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max_distance = 50
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# Delete objects that match query tags but not dataset? False is the default
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delete_unmatched = False
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# Dataset points that are closer than this distance (in meters) will be considered duplicates of each other.
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duplicate_distance = 0.5
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download_url = 'https://maps.london.gov.uk/geoserver/gis/ows'
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def dataset(download_url):
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import requests
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params = dict(
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srsName='EPSG:4326',
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service='WFS',
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version='1.0.0',
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request='GetFeature',
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typeName='gis:env_ev_charging_site',
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propertyName='(location,site_type,operation_time,operator,access,operator_type,nb_of_rcps,operation_time2)(type)',
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outputFormat='application/json',
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maxFeatures=500
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)
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download_url = 'https://maps.london.gov.uk/geoserver/gis/ows'
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r = requests.get(url=download_url, params=params)
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data = []
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counter = 1
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for el in r.json()['features']:
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# some entries were found to have no coordinates
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# e.g. "sitename":"Canary Wharf (location tbd)"
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if len(el['geometry']['coordinates']) == 2:
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row = {
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## NOTE the id has to be numeric to create, but can be later replaced in the
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# tags.ref:london_charging_stations tag with something like the location field, which is more uniquely identifiying
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"id": counter,
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"lat": float(el['geometry']['coordinates'][1]),
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"lon": float(el['geometry']['coordinates'][0]),
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"tags": {
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"ref":el['properties']['location'],
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"opening_hours": el['properties']['operation_time2'],
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"operator": el['properties']['operator'],
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"amenity": "charging_station",
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"capacity": int(el['properties']['nb_of_rcps']),
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# NOTE - this implementation uses the
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# access:private and taxi:yes tags
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# to show whether a station is restricted to taxis
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"access":"private" if el['properties']['access'] == 'Taxi only' else '',
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"taxi":"yes" if el['properties']['access'] == 'Taxi only' else '',
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}
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}
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line = SourcePoint(row['id'],row['lat'],row['lon'],row['tags'])
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data.append(line)
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counter += 1
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return data
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