def getNeighborhoodsByAreas(conn, areaids, user):
print 'getting votes'
(blocks, allVotes) = vote_utils.getVotes(conn, areaids, user)
print 'got votes'
blocks_by_hoodid = defaultdict(list)
id_to_label = {}
for block in blocks:
votes = allVotes[block['geoid10']]
#print block['geoid10']
maxVotes = vote_utils.pickBestVotes(votes)
for maxVote in maxVotes:
blocks_by_hoodid[maxVote['id']].append(block)
id_to_label[maxVote['id']] = maxVote['label']
hoods = {}
print 'doing unions'
for (id, blocks) in blocks_by_hoodid.iteritems():
geoms = [asShape(eval(block['geojson_geom'])) for block in blocks]
blockids = [block['geoid10'] for block in blocks]
pop10 = sum([block['pop10'] for block in blocks])
housing10 = sum([block['housing10'] for block in blocks])
geom = cascaded_union(geoms)
hoods[id] = NeighborhoodArea(geom, blockids, pop10, housing10)
return (hoods, id_to_label)
def getNeighborhoodsByAreas(conn, areaids, user):
print 'getting votes'
(blocks, allVotes) = vote_utils.getVotes(conn, areaids, user)
print 'got votes'
blocks_by_hoodid = defaultdict(list)
id_to_label = {}
for block in blocks:
votes = allVotes[block['geoid10']]
#print block['geoid10']
maxVotes = vote_utils.pickBestVotes(votes)
for maxVote in maxVotes:
blocks_by_hoodid[maxVote['id']].append(block)
id_to_label[maxVote['id']] = maxVote['label']
hoods = {}
print 'doing unions'
for (id, blocks) in blocks_by_hoodid.iteritems():
geoms = [asShape(eval(block['geojson_geom'])) for block in blocks]
blockids = [block['geoid10'] for block in blocks]
pop10 = sum([block['pop10'] for block in blocks])
housing10 = sum([block['housing10'] for block in blocks])
geom = cascaded_union(geoms)
hoods[id] = NeighborhoodArea(geom, blockids, pop10, housing10)
return (hoods, id_to_label)
def citydata():
conn = psycopg2.connect("dbname='gis' user='******' host='localhost' password='******'")
areaid = request.args.get('areaid', False)
apikey = request.args.get('key', '')
user = findUserByApiKey(apikey)
(rows, votes) = vote_utils.getVotes(conn, areaid, user)
response = {
"type": "FeatureCollection",
"features": makeFeatures(rows, votes, user)
}
return jsonify(response)
def citydata():
conn = getPostgresConnection()
areaid = request.args.get('areaid', False)
apikey = request.args.get('key', '')
user = findUserByApiKey(conn, apikey)
(rows, votes) = vote_utils.getVotes(conn, areaid, user)
response = {
"type": "FeatureCollection",
"features": makeFeatures(rows, votes, user)
}
return jsonify(response)
def citydata():
conn = getPostgresConnection()
areaid = request.args.get('areaid', False)
apikey = request.args.get('key', '')
user = findUserByApiKey(conn, apikey)
(rows, votes) = vote_utils.getVotes(conn, areaid, user)
response = {
"type": "FeatureCollection",
"features": makeFeatures(rows, votes, user)
}
return jsonify(response)
def citydata():
conn = psycopg2.connect(
"dbname='gis' user='******' host='localhost' password='******'")
areaid = request.args.get('areaid', False)
apikey = request.args.get('key', '')
user = findUserByApiKey(apikey)
(rows, votes) = vote_utils.getVotes(conn, areaid, user)
response = {
"type": "FeatureCollection",
"features": makeFeatures(rows, votes, user)
}
return jsonify(response)
def getNeighborhoodsByArea(conn, areaid, user):
(blocks, allVotes) = vote_utils.getVotes(conn, areaid, user)
blocks_by_hoodid = defaultdict(list)
blockids_by_hoodid = defaultdict(list)
id_to_label = {}
for block in blocks:
geom = asShape(eval(block['geojson_geom']))
votes = allVotes[block['geoid10']]
#print block['geoid10']
maxVotes = vote_utils.pickBestVotes(votes)
for maxVote in maxVotes:
blocks_by_hoodid[maxVote['id']].append(geom)
blockids_by_hoodid[maxVote['id']].append(block['geoid10'])
id_to_label[maxVote['id']] = maxVote['label']
hoods = {}
for (id, geoms) in blocks_by_hoodid.iteritems():
hoods[id] = NeighborhoodArea(cascaded_union(geoms), blockids_by_hoodid[id])
return (hoods, id_to_label)
def getNeighborhoodsByArea(conn, areaid, user):
(blocks, allVotes) = vote_utils.getVotes(conn, areaid, user)
blocks_by_hoodid = defaultdict(list)
blockids_by_hoodid = defaultdict(list)
id_to_label = {}
for block in blocks:
geom = asShape(eval(block['geojson_geom']))
votes = allVotes[block['geoid10']]
#print block['geoid10']
maxVotes = vote_utils.pickBestVotes(votes)
for maxVote in maxVotes:
blocks_by_hoodid[maxVote['id']].append(geom)
blockids_by_hoodid[maxVote['id']].append(block['geoid10'])
id_to_label[maxVote['id']] = maxVote['label']
hoods = {}
for (id, geoms) in blocks_by_hoodid.iteritems():
hoods[id] = NeighborhoodArea(cascaded_union(geoms),
blockids_by_hoodid[id])
return (hoods, id_to_label)
def processArea(areaid):
hoodDict = {}
(hoods, id_to_label) = geo_utils.getNeighborhoodsByArea(conn, areaid, None)
for (id, shape) in hoods.iteritems():
hoodIndex.add(int(id), shape.bounds)
hoodDict[int(id)] = shape
(rows, voteDict) = vote_utils.getVotes(conn, areaid, None)
bestVoteDict = {}
print 'building vote dict'
for k,v in voteDict.iteritems():
bestVoteDict[str(k).zfill(15)] = vote_utils.pickBestVote(v)
print 'building rtree'
shapeDict = {}
for r in rows:
id = r['geoid10']
shape = asShape(geojson.loads(r['geojson_geom']))
shapeDict[id] = shape
index.add(int(id), shape.bounds)
print 'smoothin'
for r in rows:
id = str(r['geoid10']).zfill(15)
bestId = None
if id in bestVoteDict:
bestId = bestVoteDict[id]['id']
shape = shapeDict[id]
# find all the blocks it maybe touches
candidate_ids = set([n for n in index.intersection(shape.bounds)])
touches = []
for cid in candidate_ids:
if cid != id:
cshape = shapeDict[str(cid).zfill(15)]
if shape.touches(cshape):
touches.append(cid)
print '%s touches %s' % (id, touches)
smearDict = defaultdict(lambda: 0)
totalWithVotes = 0
for t in touches:
tid = str(t).zfill(15)
if tid in bestVoteDict:
cbestId = bestVoteDict[tid]['id']
smearDict[cbestId] += 1
totalWithVotes += 1
candidate_hood_ids = set([n for n in hoodIndex.intersection(shape.bounds)])
for hood_id in candidate_hood_ids:
hoodShape = hoodDict[hood_id]
if hoodShape.contains(shape):
print 'block %s was contained by neighborhood %s' % (id, hood_id)
smearDict[hood_id] += 1000000
print smearDict
if len(smearDict) > 0:
maxVotes = {x for x in itertools.groupby(smearDict.iteritems(), operator.itemgetter(1))}
maxCount = max(maxVotes.keys())
isAmbiguous = len(maxVotes[maxCount]) > 1
maxVote = maxVotes[maxCount][0]
threshold = totalWithVotes * (5.0/8.0)
if maxVote[1] >= threshold and (maxVote[0] != bestId or isAmbiguous):
print 'very likely that block %s should be in %s, is in %s' % (id, maxVote[0], bestId)
cur.execute("""DELETE FROM votes WHERE source=%s AND id=%s""", ('smear', id))
cur.execute("""INSERT INTO votes (id, label, count, source) values (%s, %s, %s, 'smear')""", (
id, maxVote[0], 1))
conn.commit()
