def handle(self, *args, **options):
CENSUS_BLOCK_2010 = pickle.load(open(BOUNDARY_DATA + "census.p", 'rb'))
NEIGHBORHOOD = pickle.load(open(BOUNDARY_DATA + "community.p", 'rb'))
NEIGHBORHOOD_INDEX = create_spatial_index(NEIGHBORHOOD)
aggregate_results = {}
for i, (aggregate_id, aggregate_shape) in \
enumerate(CENSUS_BLOCK_2010.iteritems(), 1):
# print out a nice message to know how it's going
if i % 1000 == 0:
print >> sys.stderr, 'Aggregating %i of %i' % \
(i, len(CENSUS_BLOCK_2010))
if not CensusBlocks.objects.filter(
census_id=aggregate_id, building_subtype="All").exists():
print >> sys.stderr, "Updating census_id: %s" % (aggregate_id)
neighborhood_count = 0
for j, item_2010 in enumerate(
NEIGHBORHOOD_INDEX.intersection(aggregate_shape.bounds,
objects=True)):
# get the element id
raw_id = item_2010.object
# get the shape from the shape dictionary
raw_shape = NEIGHBORHOOD[raw_id]
# calculate the intersection of the polygons
intersection = aggregate_shape.intersection(raw_shape)
# calculate the fraction of the area of the aggregate shape that
# is in the raw shape
frac_raw = float(intersection.area) / aggregate_shape.area
# fix rawid in those two cases for mckliney and ohare
raw_id = raw_id.title()
if raw_id == 'Mckinley Park':
raw_id = 'McKinley Park'
elif raw_id == 'Ohare':
raw_id = "O'hare"
# if there is any area above tolerance, then add it up
if neighborhood_count == 0:
neighborhood, created = Neighborhoods.objects.get_or_create(
name=raw_id)
census_block, created = CensusBlocks.objects.get_or_create(
census_id=aggregate_id,
building_type="Residential",
building_subtype="All",
neighborhood=neighborhood,
shape=wkt.dumps(aggregate_shape))
neighborhood_count += 1
else:
continue
def handle(self, *args, **options):
CENSUS_BLOCK_2010 = pickle.load(open(BOUNDARY_DATA + "census.p", 'rb'))
NEIGHBORHOOD = pickle.load(open(BOUNDARY_DATA + "community.p",'rb'))
NEIGHBORHOOD_INDEX = create_spatial_index(NEIGHBORHOOD)
aggregate_results = {}
for i, (aggregate_id, aggregate_shape) in \
enumerate(CENSUS_BLOCK_2010.iteritems(), 1):
# print out a nice message to know how it's going
if i % 1000 == 0:
print >> sys.stderr, 'Aggregating %i of %i' % \
(i, len(CENSUS_BLOCK_2010))
if not CensusBlocks.objects.filter(census_id=aggregate_id,
building_subtype="All").exists():
print >> sys.stderr, "Updating census_id: %s" %(aggregate_id)
neighborhood_count = 0
for j,item_2010 in enumerate(NEIGHBORHOOD_INDEX.intersection(aggregate_shape.bounds,
objects=True)):
# get the element id
raw_id = item_2010.object
# get the shape from the shape dictionary
raw_shape = NEIGHBORHOOD[raw_id]
# calculate the intersection of the polygons
intersection = aggregate_shape.intersection(raw_shape)
# calculate the fraction of the area of the aggregate shape that
# is in the raw shape
frac_raw = float(intersection.area) / aggregate_shape.area
# fix rawid in those two cases for mckliney and ohare
raw_id = raw_id.title()
if raw_id == 'Mckinley Park':
raw_id = 'McKinley Park'
elif raw_id == 'Ohare':
raw_id = "O'hare"
# if there is any area above tolerance, then add it up
if neighborhood_count == 0:
neighborhood, created = Neighborhoods.objects.get_or_create(name = raw_id)
census_block, created = CensusBlocks.objects.get_or_create(census_id=aggregate_id,
building_type="Residential",
building_subtype="All",
neighborhood = neighborhood,
shape = wkt.dumps(aggregate_shape))
neighborhood_count += 1
else:
continue
def handle(self, *args, **options):
CENSUS_BLOCK_2010 = pickle.load(open(BOUNDARY_DATA + "census.p", 'rb'))
NEIGHBORHOOD = pickle.load(open(BOUNDARY_DATA + "neighborhood.p",'rb'))
NEIGHBORHOOD_INDEX = create_spatial_index(NEIGHBORHOOD)
aggregate_results = {}
for i, (aggregate_id, aggregate_shape) in \
enumerate(CENSUS_BLOCK_2010.iteritems(), 1):
# print out a nice message to know how it's going
# print >> sys.stderr, 'Aggregating %i of %i' % \
# (i, len(CENSUS_BLOCK_2010))
# try:
# census_block = CensusBlocks.objects.get(census_id=aggregate_id,
# building_type='Residential')
# except CensusBlocks.DoesNotExist:
# print aggregate_id
# continue
neighborhood_count = 0
for j,item_2010 in enumerate(NEIGHBORHOOD_INDEX.intersection(aggregate_shape.bounds,
objects=True)):
# get the element id
raw_id = item_2010.object
# get the shape from the shape dictionary
raw_shape = NEIGHBORHOOD[raw_id]
# calculate the intersection of the polygons
intersection = aggregate_shape.intersection(raw_shape)
# calculate the fraction of the area of the aggregate shape that
# is in the raw shape
frac_raw = float(intersection.area) / aggregate_shape.area
# if there is any area above tolerance, then add it up
if frac_raw >= .05:
neighborhood_count += 1
if neighborhood_count > 2:
print aggregate_id
def handle(self, *args, **options):
CENSUS_BLOCK_2010 = pickle.load(open(BOUNDARY_DATA + "census.p", 'rb'))
NEIGHBORHOOD = pickle.load(open(BOUNDARY_DATA + "neighborhood.p",
'rb'))
NEIGHBORHOOD_INDEX = create_spatial_index(NEIGHBORHOOD)
total = {}
for i, (aggregate_id, aggregate_shape) in \
enumerate(CENSUS_BLOCK_2010.iteritems(), 1):
# print out a nice message to know how it's going
print >> sys.stderr, 'Aggregating %i of %i' % \
(i, len(CENSUS_BLOCK_2010))
# find all intersecting 2000 census blocks, along with the
# fraction of the 2010 census block that will be assigned to
# the 2000 census block
# print dir(raw_spatial_index.intersection(aggregate_shape.bounds,
# objects=True)).__sizeof__()
try:
matched_neighborhood = list(
NEIGHBORHOOD_INDEX.intersection(aggregate_shape.bounds,
objects=True))[0]
except IndexError:
continue
neighborhood = Neighborhoods.objects.get(
name=matched_neighborhood.object)
try:
census_blocks = CensusBlocks.objects.filter(
census_id=aggregate_id)
except CensusBlocks.DoesNotExist:
print aggregate_id
continue
for census_block in census_blocks:
census_block.neighborhood = neighborhood
census_block.save()
def handle(self, *args, **options):
CENSUS_BLOCK_2010 = pickle.load(open(BOUNDARY_DATA + "census.p", 'rb'))
NEIGHBORHOOD = pickle.load(open(BOUNDARY_DATA + "neighborhood.p",'rb'))
NEIGHBORHOOD_INDEX = create_spatial_index(NEIGHBORHOOD)
total = {}
for i, (aggregate_id, aggregate_shape) in \
enumerate(CENSUS_BLOCK_2010.iteritems(), 1):
# print out a nice message to know how it's going
print >> sys.stderr, 'Aggregating %i of %i' % \
(i, len(CENSUS_BLOCK_2010))
# find all intersecting 2000 census blocks, along with the
# fraction of the 2010 census block that will be assigned to
# the 2000 census block
# print dir(raw_spatial_index.intersection(aggregate_shape.bounds,
# objects=True)).__sizeof__()
try:
matched_neighborhood = list(NEIGHBORHOOD_INDEX.intersection(aggregate_shape.bounds,
objects=True))[0]
except IndexError:
continue
neighborhood = Neighborhoods.objects.get(name = matched_neighborhood.object)
try:
census_blocks = CensusBlocks.objects.filter(census_id=aggregate_id)
except CensusBlocks.DoesNotExist:
print aggregate_id
continue
for census_block in census_blocks:
census_block.neighborhood = neighborhood
census_block.save()
# --------------------------------------------- read in all of the boundary data
# CENSUS_BLOCK_2000 = read_shapefile(
# os.path.join(BOUNDARY_DATA, 'Census Blocks.shp'),
# id_fieldname = "CENSUS_B_1",
# simplification = SIMPLIFICATION,
# )
# CENSUS_BLOCK_2000_INDEX = create_spatial_index(CENSUS_BLOCK_2000)
print "getting 2010 census"
CENSUS_BLOCK_2010 = read_shapefile(
os.path.join(BOUNDARY_DATA, 'CensusBlockTIGER2010.shp'),
id_fieldname = "GEOID10",
simplification = SIMPLIFICATION,
)
pickle.dump(CENSUS_BLOCK_2010, open("../web/apps/main/management/commands/census.p", 'wb'))
CENSUS_BLOCK_2010_INDEX = create_spatial_index(CENSUS_BLOCK_2010)
# print CENSUS_BLOCK_2010
# SCHOOL = read_shapefile(
# os.path.join(BOUNDARY_DATA, 'BoundaryGrades10.shp'),
# id_fieldname = "SCHOOLID",
# simplification = SIMPLIFICATION,
# )
# CENSUS_TRACT_2000 = read_shapefile(
# os.path.join(BOUNDARY_DATA, 'Census_Tracts.shp'),
# id_fieldname = "CENSUS_T_1",
# simplification = SIMPLIFICATION,
# )
def handle(self, *args, **options):
g = geocoders.GeoNames()
CENSUS_BLOCK_2010 = pickle.load(open(BOUNDARY_DATA + "census.p", 'rb'))
NEIGHBORHOOD = pickle.load(open(BOUNDARY_DATA + "community.p",'rb'))
NEIGHBORHOOD_INDEX = create_spatial_index(NEIGHBORHOOD)
aggregate_results = {}
for i, (aggregate_id, aggregate_shape) in \
enumerate(CENSUS_BLOCK_2010.iteritems(), 1):
# print out a nice message to know how it's going
if i % 100 == 0:
print >> sys.stderr, 'Aggregating %i of %i' % \
(i, len(CENSUS_BLOCK_2010))
# try:
# census_blocks = CensusBlocks.objects.filter(census_id=aggregate_id)
# except CensusBlocks.DoesNotExist:
# continue
neighborhood_count = 0
for j,item_2010 in enumerate(NEIGHBORHOOD_INDEX.intersection(aggregate_shape.bounds,
objects=True)):
# get the element id
raw_id = item_2010.object
# get the shape from the shape dictionary
raw_shape = NEIGHBORHOOD[raw_id]
# calculate the intersection of the polygons
intersection = aggregate_shape.intersection(raw_shape)
# calculate the fraction of the area of the aggregate shape that
# is in the raw shape
frac_raw = float(intersection.area) / aggregate_shape.area
# fix rawid in those two cases for mckliney and ohare
raw_id = raw_id.title()
if raw_id == 'Mckinley Park':
raw_id = 'McKinley Park'
elif raw_id == 'Ohare':
raw_id = "O'hare"
# if there is any area above tolerance, then add it up
if frac_raw >= .05 and neighborhood_count == 0:
neighborhood, created = Neighborhoods.objects.get_or_create(name = raw_id)
# Neighborhoods.objects.filter(name = neighborhood.name)\
# .update(shape = wkt.dumps(raw_shape))
#json.dumps(list(raw_shape.exterior.coords)))
# neighborhood.shape = json.dumps(list(raw_shape.exterior.coords))
# neighborhood.save()
# Add the average value to the non-existing rows
if not CensusBlocks.objects.filter(census_id=aggregate_id,
building_type = "Residential",
building_subtype = "Single Family").exists():
CensusBlocks.objects.create(census_id=aggregate_id,
building_type = "Residential",
building_subtype = "Single Family",
kwh_efficiency = neighborhood.kwh_efficiency,
therm_efficiency = neighborhood.therm_efficiency,
neighborhood = neighborhood,
shape = wkt.dumps(aggregate_shape))
if not CensusBlocks.objects.filter(census_id=aggregate_id,
building_type = "Residential",
building_subtype = "Multi 7+").exists():
CensusBlocks.objects.create(census_id=aggregate_id,
building_type = "Residential",
building_subtype = "Multi 7+",
kwh_efficiency = neighborhood.kwh_efficiency,
therm_efficiency = neighborhood.therm_efficiency,
neighborhood = neighborhood,
shape = wkt.dumps(aggregate_shape))
if not CensusBlocks.objects.filter(census_id=aggregate_id,
building_type = "Residential",
building_subtype = "Multi < 7").exists():
CensusBlocks.objects.create(census_id=aggregate_id,
building_type = "Residential",
building_subtype = "Multi < 7",
kwh_efficiency = neighborhood.kwh_efficiency,
therm_efficiency = neighborhood.therm_efficiency,
neighborhood = neighborhood,
shape = wkt.dumps(aggregate_shape))
# try:
# shape = aggregate_shape
# if shape != None:
# lat, lon = list(shape.representative_point().coords)[0]
# try:
# (place, point) = g.reverse((lon,lat))
# except ValueError:
# nice_address = ""
# else:
# # Get the address because we are rounding it out
# address = place.split(",")[0]
# broken = address.split()
# try:
# new_street = str(int(round(int(broken[0]) ,-2)))
# except ValueError:
# print address, place
# nice_address = address
# else:
# broken = ' '.join(broken[1:])
# nice_address = new_street + " " + broken
# else:
# nice_address = ""
# except (AttributeError) as e:
# print "attribute error", aggregate_id
# nice_address = ""
# CensusBlocks.objects.filter(census_id=aggregate_id)\
# .update(neighborhood = neighborhood,
# shape = wkt.dumps(aggregate_shape))
# nice_address = nice_address)
#json.dumps(list(aggregate_shape.exterior.coords)))
neighborhood_count += 1
