def add_employment(df, year):
hhs_by_inc = df[["hhincq1", "hhincq2", "hhincq3", "hhincq4"]]
hh_shares = hhs_by_inc.divide(hhs_by_inc.sum(axis=1), axis="index")
zfi = zone_forecast_inputs()
empshare = 0.46381 * hh_shares.hhincq1 + 0.49361 * hh_shares.hhincq2 +\
0.56938 * hh_shares.hhincq3 + 0.29818 * hh_shares.hhincq4 +\
zfi.zonal_emp_sh_resid10
# I really don't think more than 70% of people should be employed
# in a given zone - this also makes sure that the employed residents
# is less then the total population (after scaling) - if the
# assertion below is triggered you can fix it by reducing this
# .7 even a little bit more
empshare = empshare.fillna(0).clip(.3, .7)
empres = empshare * df.totpop
rc = regional_controls()
target = rc.empres.loc[year]
empres = scale_by_target(empres, target)
df["empres"] = round_series_match_target(empres, target, 0)
# this should really make the assertion below pass, but this now
# only occurs very infrequently
df["empres"] = df[["empres", "totpop"]].min(axis=1)
# make sure employed residents is less than total residents
assert (df.empres <= df.totpop).all()
return df
def add_age_categories(df, year):
zfi = zone_forecast_inputs()
rc = regional_controls()
seed_matrix = zfi[["sh_age0004", "sh_age0519", "sh_age2044",
"sh_age4564", "sh_age65p"]].\
mul(df.totpop, axis='index').as_matrix()
row_marginals = df.totpop.values
agecols = ["age0004", "age0519", "age2044", "age4564", "age65p"]
col_marginals = rc[agecols].loc[year].values
target = df.totpop.sum()
col_marginals = scale_by_target(pd.Series(col_marginals),
target).round().astype('int')
seed_matrix[seed_matrix == 0] = .1
seed_matrix[row_marginals == 0, :] = 0
mat = simple_ipf(seed_matrix, col_marginals, row_marginals)
agedf = pd.DataFrame(mat)
agedf.columns = [col.upper() for col in agecols]
agedf.index = zfi.index
for ind, row in agedf.iterrows():
target = df.totpop.loc[ind]
row = row.round()
agedf.loc[ind] = round_series_match_target(row, target, 0)
for col in agedf.columns:
df[col] = agedf[col]
return df
def add_age_categories(df, year):
zfi = zone_forecast_inputs()
rc = regional_controls()
seed_matrix = zfi[["sh_age0004", "sh_age0519", "sh_age2044",
"sh_age4564", "sh_age65p"]].\
mul(df.totpop, axis='index').as_matrix()
row_marginals = df.totpop.values
agecols = ["age0004", "age0519", "age2044", "age4564", "age65p"]
col_marginals = rc[agecols].loc[year].values
target = df.totpop.sum()
col_marginals = scale_by_target(pd.Series(col_marginals),
target).round().astype('int')
seed_matrix[seed_matrix == 0] = .1
seed_matrix[row_marginals == 0, :] = 0
mat = simple_ipf(seed_matrix, col_marginals, row_marginals)
agedf = pd.DataFrame(mat)
agedf.columns = [col.upper() for col in agecols]
agedf.index = zfi.index
for ind, row in agedf.iterrows():
target = df.totpop.loc[ind]
row = row.round()
agedf.loc[ind] = round_series_match_target(row, target, 0)
for col in agedf.columns:
df[col] = agedf[col]
return df
def add_employment(df, year):
hhs_by_inc = df[["hhincq1", "hhincq2", "hhincq3", "hhincq4"]]
hh_shares = hhs_by_inc.divide(hhs_by_inc.sum(axis=1), axis="index")
zfi = zone_forecast_inputs()
empshare = 0.46381 * hh_shares.hhincq1 + 0.49361 * hh_shares.hhincq2 +\
0.56938 * hh_shares.hhincq3 + 0.29818 * hh_shares.hhincq4 +\
zfi.zonal_emp_sh_resid10
# I really don't think more than 70% of people should be employed
# in a given zone - this also makes sure that the employed residents
# is less then the total population (after scaling) - if the
# assertion below is triggered you can fix it by reducing this
# .7 even a little bit more
empshare = empshare.fillna(0).clip(.3, .7)
empres = empshare * df.totpop
rc = regional_controls()
target = rc.empres.loc[year]
empres = scale_by_target(empres, target)
df["empres"] = round_series_match_target(empres, target, 0)
# this should really make the assertion below pass, but this now
# only occurs very infrequently
df["empres"] = df[["empres", "totpop"]].min(axis=1)
# make sure employed residents is less than total residents
assert (df.empres <= df.totpop).all()
return df
def add_population(df, year):
rc = regional_controls()
target = rc.totpop.loc[year] - df.gqpop.sum()
zfi = zone_forecast_inputs()
s = df.tothh * zfi.meanhhsize
s = scale_by_target(s, target, .15)
df["hhpop"] = round_series_match_target(s, target, 0)
df["hhpop"] = df.hhpop.fillna(0)
return df
def add_population(df, year):
rc = regional_controls()
target = rc.totpop.loc[year] - df.gqpop.sum()
zfi = zone_forecast_inputs()
s = df.tothh * zfi.meanhhsize
s = scale_by_target(s, target, .15)
df["hhpop"] = round_series_match_target(s, target, 0)
df["hhpop"] = df.hhpop.fillna(0)
return df
def add_age_categories(df, year):
zfi = zone_forecast_inputs()
rc = regional_controls()
# Added [df.index.isin(zfi.index)] to prevent errors on non-matching data
seed_matrix = zfi[["sh_age0004", "sh_age0519", "sh_age2044",
"sh_age4564", "sh_age65p"]].\
mul(df[df.index.isin(zfi.index)].totpop, axis='index').as_matrix()
row_marginals = df.totpop.values
agecols = ["age0004", "age0519", "age2044", "age4564", "age65p"]
col_marginals = rc[agecols].loc[year].values
target = df.totpop.sum()
col_marginals = scale_by_target(pd.Series(col_marginals),
target).round().astype('int')
seed_matrix[seed_matrix == 0] = .1
seed_matrix[row_marginals == 0, :] = 0
# Added by Derek to make sure the seed_matrix is a matrix, rather than a
# ndarray, which caused problems in simple_ipf
seed_matrix = pd.DataFrame(seed_matrix)
mat = simple_ipf(seed_matrix, col_marginals, row_marginals)
agedf = pd.DataFrame(mat)
agedf.columns = [col.upper() for col in agecols]
agedf.index = zfi.index
for ind, row in agedf.iterrows():
target = df.totpop.loc[ind]
row = row.round()
agedf.loc[ind] = round_series_match_target(row, target, 0)
for col in agedf.columns:
df[col] = agedf[col]
return df
