def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
for row in reader:
p = PARKING[utils.idify(row["Name"])]
graph.add((p, RDF.type, PARKING_ONT["ParkingSite"]))
graph.add((p, utils.RDFS['label'], Literal(row["Name"])))
graph.add((p, PARKING_ONT['parkingType'], PARKING_TYPE_ONT[utils.idify(row["Type"])]))
graph.add((p, PARKING_ONT['informationPage'], Literal(row["URL"])))
p_operator = PARKING["operator/" + utils.idify(row["Operator"])]
graph.add((p, PARKING_ONT['operator'], p_operator))
graph.add((p_operator, RDF.type, PARKING_ONT["ParkingSiteOperator"]))
graph.add((p_operator, utils.RDFS['label'], Literal(row["Operator"])))
graph.add((p_operator, utils.ADMINGEO["MetropolitanDistrict"], URIRef("http://data.ordnancesurvey.co.uk/id/7000000000018805")))
graph.add((p, utils.GEO["lat"], Literal(row["Latitude"])))
graph.add((p, utils.GEO["long"], Literal(row["Longitude"])))
address = utils.idify(row["Address"])
graph.add((p, utils.VCARD['hasAddress'], URIRef("http://data.gmdsp.org.uk/def/council/parking/address/"+address)))
# now add the address VCARD
vcard = PARKING["address/"+address]
graph.add((vcard, RDF.type, utils.VCARD["Location"]))
graph.add((vcard, utils.RDFS['label'], Literal(row["Name"])))
graph.add((vcard, utils.VCARD['street-address'], Literal(row["Address"])))
graph.add((vcard, utils.VCARD['postal-code'], Literal(row["Postcode"])))
graph.add((vcard, utils.POST['postcode'], URIRef(utils.convertpostcodeto_osuri(row["Postcode"]))))
def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
for row in reader:
allotment = al[utils.idify(row["Name"])]
graph.add((allotment, RDF.type, al_ont['Allotment']))
graph.add((allotment, utils.RDFS['label'], Literal("Allotment site " + row["Name"])))
# geo info
graph.add((allotment, utils.OS["northing"], Literal(row["Northing"])))
graph.add((allotment, utils.OS["easting"], Literal(row["Easting"])))
# add conversion for lat/long
lat_long = utils.ENtoLL84(float(row["Easting"]), float(row["Northing"]))
graph.add((allotment, utils.GEO["long"], Literal(lat_long[0])))
graph.add((allotment, utils.GEO["lat"], Literal(lat_long[1])))
address = utils.idify(row["Address"])
graph.add((allotment, utils.VCARD['hasAddress'], al["address/"+address]))
street_address, address_postcode = utils.postcode_helper(row["Address"])
# now add the address VCARD
vcard = al["address/"+address]
graph.add((vcard, RDF.type, utils.VCARD["Location"]))
graph.add((vcard, utils.RDFS['label'], Literal("Address of allotment site " + row["Name"])))
graph.add((vcard, utils.VCARD['street-address'], Literal(street_address)))
if address_postcode is not None:
graph.add((vcard, utils.VCARD['postal-code'], Literal(address_postcode)))
graph.add((vcard, utils.POST['postcode'], URIRef(utils.convertpostcodeto_osuri(address_postcode))))
def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
for row in reader:
rc = RECYCLING[utils.idify(row["Location"])]
graph.add((rc, RDF.type, RECYCLING_ONT["RecyclingSite"]))
graph.add((rc, utils.RDFS['label'],
Literal("Recycling Site at " + row["Location"])))
address = utils.idify(row["Address"])
graph.add(
(rc, utils.VCARD['hasAddress'], RECYCLING["address/" + address]))
# now add the address VCARD
vcard = RECYCLING["address/" + address]
graph.add((vcard, RDF.type, utils.VCARD["Location"]))
graph.add((vcard, utils.RDFS['label'],
Literal("Address of Recycling Site at " + row["Location"])))
graph.add(
(vcard, utils.VCARD['street-address'], Literal(row["Address"])))
graph.add(
(vcard, utils.VCARD['postal-code'], Literal(row["Postcode"])))
graph.add((vcard, utils.POST['postcode'],
URIRef(utils.convertpostcodeto_osuri(row["Postcode"]))))
# location information
graph.add((rc, utils.OS["northing"], Literal(row["Northings"])))
graph.add((rc, utils.OS["easting"], Literal(row["Eastings"])))
# add conversion for lat/long
lat_long = utils.ENtoLL84(float(row["Eastings"]),
float(row["Northings"]))
graph.add((rc, utils.GEO["long"], Literal(lat_long[0])))
graph.add((rc, utils.GEO["lat"], Literal(lat_long[1])))
# recycling informationxs
# maps the CSV header to the recycling facility concept schema
facility_map = {
"Cardboard": "cardboard",
"Paper": "paper",
"Cartons": "cartons",
"Shoes": "shoes",
"Glass": "glass",
"Textiles": "textiles",
"Cans": "cans",
"Plastic Bottles": "plastic",
"Aerosols": "aerosols",
}
for facility in facility_map:
if row[facility]:
graph.add((rc, RECYCLING_ONT['recyclingType'],
RECYCLING_TYPE_ONT[facility_map[facility]]))
def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
for row in reader:
rc = RECYCLING[utils.idify(row["Location"])]
graph.add((rc, RDF.type, RECYCLING_ONT["RecyclingSite"]))
graph.add((rc, utils.RDFS['label'], Literal("Recycling Site at " + row["Location"])))
address = utils.idify(row["Address"])
graph.add((rc, utils.VCARD['hasAddress'], RECYCLING["address/"+address]))
# now add the address VCARD
vcard = RECYCLING["address/"+address]
graph.add((vcard, RDF.type, utils.VCARD["Location"]))
graph.add((vcard, utils.RDFS['label'], Literal("Address of Recycling Site at " + row["Location"])))
graph.add((vcard, utils.VCARD['street-address'], Literal(row["Address"])))
graph.add((vcard, utils.VCARD['postal-code'], Literal(row["Postcode"])))
graph.add((vcard, utils.POST['postcode'], URIRef(utils.convertpostcodeto_osuri(row["Postcode"]))))
# location information
graph.add((rc, utils.OS["northing"], Literal(row["Northings"])))
graph.add((rc, utils.OS["easting"], Literal(row["Eastings"])))
# add conversion for lat/long
lat_long = utils.ENtoLL84(float(row["Eastings"]), float(row["Northings"]))
graph.add((rc, utils.GEO["long"], Literal(lat_long[0])))
graph.add((rc, utils.GEO["lat"], Literal(lat_long[1])))
# recycling informationxs
# maps the CSV header to the recycling facility concept schema
facility_map = {
"Cardboard": "cardboard",
"Paper": "paper",
"Cartons": "cartons",
"Shoes": "shoes",
"Glass": "glass",
"Textiles": "textiles",
"Cans": "cans",
"Plastic Bottles": "plastic",
"Aerosols": "aerosols",
}
for facility in facility_map:
if row[facility]:
graph.add((rc, RECYCLING_ONT['recyclingType'], RECYCLING_TYPE_ONT[facility_map[facility]]))
def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
for row in reader:
allotment = al[utils.idify(row["Name"])]
graph.add((allotment, RDF.type, al_ont['Allotment']))
graph.add((allotment, utils.RDFS['label'],
Literal("Allotment site " + row["Name"])))
# geo info
graph.add((allotment, utils.OS["northing"], Literal(row["Northing"])))
graph.add((allotment, utils.OS["easting"], Literal(row["Easting"])))
# add conversion for lat/long
lat_long = utils.ENtoLL84(float(row["Easting"]),
float(row["Northing"]))
graph.add((allotment, utils.GEO["long"], Literal(lat_long[0])))
graph.add((allotment, utils.GEO["lat"], Literal(lat_long[1])))
address = utils.idify(row["Address"])
graph.add(
(allotment, utils.VCARD['hasAddress'], al["address/" + address]))
street_address, address_postcode = utils.postcode_helper(
row["Address"])
# now add the address VCARD
vcard = al["address/" + address]
graph.add((vcard, RDF.type, utils.VCARD["Location"]))
graph.add((vcard, utils.RDFS['label'],
Literal("Address of allotment site " + row["Name"])))
graph.add(
(vcard, utils.VCARD['street-address'], Literal(street_address)))
if address_postcode is not None:
graph.add(
(vcard, utils.VCARD['postal-code'], Literal(address_postcode)))
graph.add(
(vcard, utils.POST['postcode'],
URIRef(utils.convertpostcodeto_osuri(address_postcode))))
def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
non_existent_uris = set()
rows = list(reader)
row_length = len(rows)
for index, row in enumerate(rows):
# this takes a while so lets provide some context
print "{}/{}".format(index, row_length)
pa = PLANNING[utils.idify(row["REFERENCE"])]
graph.add((pa, RDF.type, PLANNING_ONT["PlanningApplication"]))
graph.add((pa, utils.RDFS['label'], Literal("Planning application " + row["REFERENCE"])))
# planning application specific stuff
if row["APP TYPE DECODE"]:
graph.add((pa, PLANNING_ONT['applicationType'], PLANNING_APPLICATION_STATUS_ONT[utils.idify(row["APP TYPE DECODE"])]))
if row["APP TYPE"]:
graph.add((pa, PLANNING_ONT['applicationTypeCode'], Literal(row["APP TYPE"])))
if row["DEVELOPMENT TYPE DECODE"]:
graph.add((pa, PLANNING_ONT['developmentType'], PLANNING_APPLICATION_TYPE_ONT[utils.idify(row["DEVELOPMENT TYPE DECODE"])]))
if row["PROPOSAL"]:
graph.add((pa, PLANNING_ONT['proposal'], Literal(clean_string(row["PROPOSAL"]))))
if row["VALIDATION DATE"]:
validation_date = datetime.datetime.strptime(
row["VALIDATION DATE"].split(" ")[0],
"%d/%m/%Y",
)
try:
date_string = validation_date.strftime("%Y-%m-%d")
graph.add((pa, PLANNING_ONT['validatedDate'], utils.DATE[date_string]))
except ValueError:
# This means we were unable to parse a valid date
# so just don't ' this node to the graph
pass
if row["RECOMMENDATION DECODE"]:
graph.add((pa, PLANNING_ONT['decision'], PLANNING_APPLICATION_STATUS_ONT[utils.idify(row["RECOMMENDATION DECODE"])]))
if row["DATEDECISS"]:
decision_date = datetime.datetime.strptime(
row["DATEDECISS"].split(" ")[0],
"%d/%m/%Y",
)
try:
date_string = decision_date.strftime("%Y-%m-%d")
graph.add((pa, PLANNING_ONT['decisionDate'], utils.DATE[date_string]))
except ValueError:
# This means we were unable to parse a valid date
# so just don't ' this node to the graph
pass
# planning application site
pa_site = PLANNING["site/" + utils.idify(row["REFERENCE"])]
graph.add((pa, utils.VCARD['hasAddress'], pa_site))
graph.add((pa_site, RDF.type, PLANNING_ONT['PlanningApplicationSite']))
graph.add((pa_site, utils.RDFS['label'], Literal("Planning application site for planning application " + row["REFERENCE"])))
# postcode helper used here to remove the postcode if we find it
street_address, address_postcode = utils.postcode_helper(clean_string(row["LOCATION"]))
graph.add((pa_site, utils.VCARD['street-address'], Literal(street_address)))
graph.add((pa_site, utils.VCARD['postal-code'], Literal(row["Postcode"])))
os_postcodeuri = utils.convertpostcodeto_osuri(row["Postcode"])
graph.add((pa_site, utils.POST['postcode'], URIRef(os_postcodeuri)))
# so now we are going to generate lat/long information based on the postcode centroids
try:
os_postcodedata = json.load(urllib2.urlopen(os_postcodeuri + ".json"))
graph.add((pa_site, utils.GEO["lat"], Literal(float(os_postcodedata[os_postcodeuri][str(utils.GEO["lat"])][0]["value"]))))
graph.add((pa_site, utils.GEO["long"], Literal(float(os_postcodedata[os_postcodeuri][str(utils.GEO["long"])][0]["value"]))))
except urllib2.HTTPError:
# print "Unable to load data from: ", os_postcodeuri
non_existent_uris.add(os_postcodeuri)
pass
print "Unable to locate the following uri's:", non_existent_uris
def convert(graph, input_path):
reader = csv.DictReader(open(input_path, mode='r'))
non_existent_uris = set()
rows = list(reader)
row_length = len(rows)
for index, row in enumerate(rows):
# this takes a while so lets provide some context
print "{}/{}".format(index, row_length)
pa = PLANNING[utils.idify(row["REFERENCE"])]
graph.add((pa, RDF.type, PLANNING_ONT["PlanningApplication"]))
graph.add((pa, utils.RDFS['label'],
Literal("Planning application " + row["REFERENCE"])))
# planning application specific stuff
if row["APP TYPE DECODE"]:
graph.add((pa, PLANNING_ONT['applicationType'],
PLANNING_APPLICATION_STATUS_ONT[utils.idify(
row["APP TYPE DECODE"])]))
if row["APP TYPE"]:
graph.add((pa, PLANNING_ONT['applicationTypeCode'],
Literal(row["APP TYPE"])))
if row["DEVELOPMENT TYPE DECODE"]:
graph.add((pa, PLANNING_ONT['developmentType'],
PLANNING_APPLICATION_TYPE_ONT[utils.idify(
row["DEVELOPMENT TYPE DECODE"])]))
if row["PROPOSAL"]:
graph.add((pa, PLANNING_ONT['proposal'],
Literal(clean_string(row["PROPOSAL"]))))
if row["VALIDATION DATE"]:
validation_date = datetime.datetime.strptime(
row["VALIDATION DATE"].split(" ")[0],
"%d/%m/%Y",
)
try:
date_string = validation_date.strftime("%Y-%m-%d")
graph.add((pa, PLANNING_ONT['validatedDate'],
utils.DATE[date_string]))
except ValueError:
# This means we were unable to parse a valid date
# so just don't ' this node to the graph
pass
if row["RECOMMENDATION DECODE"]:
graph.add((pa, PLANNING_ONT['decision'],
PLANNING_APPLICATION_STATUS_ONT[utils.idify(
row["RECOMMENDATION DECODE"])]))
if row["DATEDECISS"]:
decision_date = datetime.datetime.strptime(
row["DATEDECISS"].split(" ")[0],
"%d/%m/%Y",
)
try:
date_string = decision_date.strftime("%Y-%m-%d")
graph.add((pa, PLANNING_ONT['decisionDate'],
utils.DATE[date_string]))
except ValueError:
# This means we were unable to parse a valid date
# so just don't ' this node to the graph
pass
# planning application site
pa_site = PLANNING["site/" + utils.idify(row["REFERENCE"])]
graph.add((pa, utils.VCARD['hasAddress'], pa_site))
graph.add((pa_site, RDF.type, PLANNING_ONT['PlanningApplicationSite']))
graph.add(
(pa_site, utils.RDFS['label'],
Literal("Planning application site for planning application " +
row["REFERENCE"])))
# postcode helper used here to remove the postcode if we find it
street_address, address_postcode = utils.postcode_helper(
clean_string(row["LOCATION"]))
graph.add(
(pa_site, utils.VCARD['street-address'], Literal(street_address)))
graph.add(
(pa_site, utils.VCARD['postal-code'], Literal(row["Postcode"])))
os_postcodeuri = utils.convertpostcodeto_osuri(row["Postcode"])
graph.add((pa_site, utils.POST['postcode'], URIRef(os_postcodeuri)))
# so now we are going to generate lat/long information based on the postcode centroids
try:
os_postcodedata = json.load(
urllib2.urlopen(os_postcodeuri + ".json"))
graph.add((pa_site, utils.GEO["lat"],
Literal(
float(os_postcodedata[os_postcodeuri][str(
utils.GEO["lat"])][0]["value"]))))
graph.add((pa_site, utils.GEO["long"],
Literal(
float(os_postcodedata[os_postcodeuri][str(
utils.GEO["long"])][0]["value"]))))
except urllib2.HTTPError:
# print "Unable to load data from: ", os_postcodeuri
non_existent_uris.add(os_postcodeuri)
pass
print "Unable to locate the following uri's:", non_existent_uris
