def download_tiger_data(shp_path_only=False):
""""""
tiger_url = 'ftp://ftp2.census.gov/geo/tiger/TIGER{yr}'.format(
yr=gv.tiger_year)
gv.shp = dict()
for prod in gv.product:
prod_name = TIGER_PRODUCT[prod].lower()
prod_class = ''.join([c for c in TIGER_PRODUCT[prod] if c.isalpha()])
prod_dir = join(gv.data_dir, prod_class)
if not exists(prod_dir):
os.makedirs(prod_dir)
for st in gv.states:
prod_url = '{base_url}/{class_}/' \
'tl_{yr}_{fips}_{name}.zip'.format(
base_url=tiger_url, class_=prod_class,
yr=gv.tiger_year, fips=gv.state_fips[st],
name=prod_name)
# add names of shapefiles to dictionary mapping to the
# table that they will be inserted into
shp_name = '{}.shp'.format(splitext(basename(prod_url))[0])
shp_path = join(prod_dir, shp_name)
gv.shp[shp_path] = prod
if not shp_path_only:
prod_path = utils.download_with_progress(prod_url, prod_dir)
with ZipFile(prod_path, 'r') as z:
print '\nunzipping...'
z.extractall(prod_dir)
def download_tiger_data(shp_path_only=False):
""""""
tiger_url = 'ftp://ftp2.census.gov/geo/tiger/TIGER{yr}'.format(
yr=gv.tiger_year)
gv.shp = dict()
for prod in gv.product:
prod_name = TIGER_PRODUCT[prod].lower()
prod_class = ''.join([c for c in TIGER_PRODUCT[prod] if c.isalpha()])
prod_dir = join(gv.data_dir, prod_class)
if not exists(prod_dir):
os.makedirs(prod_dir)
for st in gv.states:
prod_url = '{base_url}/{class_}/' \
'tl_{yr}_{fips}_{name}.zip'.format(
base_url=tiger_url, class_=prod_class,
yr=gv.tiger_year, fips=gv.state_fips[st],
name=prod_name)
# add names of shapefiles to dictionary mapping to the
# table that they will be inserted into
shp_name = '{}.shp'.format(splitext(basename(prod_url))[0])
shp_path = join(prod_dir, shp_name)
gv.shp[shp_path] = prod
if not shp_path_only:
prod_path = utils.download_with_progress(prod_url, prod_dir)
with ZipFile(prod_path, 'r') as z:
print '\nunzipping...'
z.extractall(prod_dir)
def download_acs_data():
""""""
# get raw census data in text delimited form, the data has been
# grouped into what the Census Bureau calls 'sequences'
acs_url = 'http://www2.census.gov/programs-surveys/' \
'acs/summary_file/{yr}'.format(yr=gv.acs_year)
for geog in ACS_GEOGRAPHY:
geog_dir = join(gv.data_dir, geog.lower())
if not exists(geog_dir):
os.makedirs(geog_dir)
for st in gv.states:
st_name = gv.state_names[st]
geog_url = '{base_url}/data/{span}_year_by_state/' \
'{state}_{geography}.zip'.format(
base_url=acs_url, span=gv.span,
state=st_name, geography=geog)
geog_path = utils.download_with_progress(geog_url, geog_dir)
with ZipFile(geog_path, 'r') as z:
print '\nunzipping...'
z.extractall(dirname(geog_path))
# the raw csv doesn't have field names for metadata, the templates
# downloaded below provide that (but only the geoheader metadata
# will be used by this process)
schema_url = '{base_url}/data/{yr}_{span}yr_' \
'Summary_FileTemplates.zip'.format(
base_url=acs_url, yr=gv.acs_year, span=gv.span)
schema_path = utils.download_with_progress(schema_url, gv.data_dir)
with ZipFile(schema_path, 'r') as z:
print '\nunzipping...'
z.extractall(dirname(schema_path))
# download the lookup table that contains information as to how to
# extract the ACS tables from the sequences
lookup_url = '{base_url}/documentation/user_tools/' \
'{lookup}'.format(base_url=acs_url, lookup=gv.lookup_file)
utils.download_with_progress(lookup_url, gv.data_dir)
