def subsidized_office_developer(feasibility, coffer, acct_settings, year,
add_extra_columns_func, buildings, summary):
# get the total subsidy for subsidizing office
total_subsidy = coffer["vmt_com_acct"].\
total_transactions_by_subacct("regional")
# get the office feasibility frame and sort by profit per sqft
feasibility = feasibility.to_frame().loc[:, "office"]
if "max_profit" not in feasibility.columns:
feasibility["max_profit"] = 0
feasibility = feasibility.dropna(subset=["max_profit"])
if "pda" not in feasibility.columns:
feasibility["pda"] = np.nan
feasibility["pda_id"] = feasibility.pda
# filter to receiving zone
feasibility = feasibility.\
query(acct_settings["vmt_settings"]["receiving_buildings_filter"])
if "non_residential_sqft" not in feasibility.columns:
feasibility["non_residential_sqft"] = 0
feasibility["non_residential_sqft"] = \
feasibility.non_residential_sqft.astype("int")
feasibility["max_profit_per_sqft"] = feasibility.max_profit / \
feasibility.non_residential_sqft
# sorting by our choice metric as we're going to pick our devs
# in order off the top
feasibility = feasibility.sort_values(['max_profit_per_sqft'])
# make parcel_id available
feasibility = feasibility.reset_index()
print "%.0f subsidy with %d developments to choose from" % (
total_subsidy, len(feasibility))
devs = []
for dev_id, d in feasibility.iterrows():
# the logic here is that we allow each dev to have a "normal"
# profit per square foot and once it does we guarantee that it
# gets build - we assume the planning commission for each city
# enables this to happen. If a project gets enough profit already
# we just allow it to compete on the open market - e.g. in the
# non-subsidized office developer
NORMAL_PROFIT_PER_SQFT = 70 # assume price is around $700/sqft
if d.max_profit_per_sqft >= NORMAL_PROFIT_PER_SQFT:
# competes in open market
continue
else:
amt = (NORMAL_PROFIT_PER_SQFT - d.max_profit_per_sqft) * \
d.non_residential_sqft
if amt > total_subsidy:
# we don't have enough money to buy yet
continue
metadata = {
"description": "Developing subsidized office building",
"year": year,
"non_residential_sqft": d["non_residential_sqft"],
"index": dev_id
}
coffer["vmt_com_acct"].add_transaction(-1 * amt,
subaccount="regional",
metadata=metadata)
total_subsidy -= amt
devs.append(d)
if len(devs) == 0:
return
# record keeping - add extra columns to match building dataframe
# add the buidings and demolish old buildings, and add to debug output
devs = pd.DataFrame(devs, columns=feasibility.columns)
print "Building {:,} subsidized office sqft in {:,} projects".format(
devs.non_residential_sqft.sum(), len(devs))
devs["form"] = "office"
devs = add_extra_columns_func(devs)
add_buildings(buildings, devs)
summary.add_parcel_output(devs)
def retail_developer(jobs, buildings, parcels, nodes, feasibility,
settings, summary, add_extra_columns_func, net):
dev_settings = settings['non_residential_developer']
all_units = dev.compute_units_to_build(
len(jobs),
buildings.job_spaces.sum(),
dev_settings['kwargs']['target_vacancy'])
target = all_units * float(dev_settings['type_splits']["Retail"])
# target here is in sqft
target *= settings["building_sqft_per_job"]["HS"]
feasibility = feasibility.to_frame().loc[:, "retail"]
feasibility = feasibility.dropna(subset=["max_profit"])
feasibility["non_residential_sqft"] = \
feasibility.non_residential_sqft.astype("int")
feasibility["retail_ratio"] = parcels.retail_ratio
feasibility = feasibility.reset_index()
# create features
f1 = feasibility.retail_ratio / feasibility.retail_ratio.max()
f2 = feasibility.max_profit / feasibility.max_profit.max()
# combine features in probability function - it's like combining expense
# of building the building with the market in the neighborhood
p = f1 * 1.5 + f2
p = p.clip(lower=1.0 / len(p) / 10)
print "Attempting to build {:,} retail sqft".format(target)
# order by weighted random sample
feasibility = feasibility.sample(frac=1.0, weights=p)
foreign_columns = ["general_type"]
bldgs = buildings.to_frame(buildings.local_columns + foreign_columns)
devs = []
for dev_id, d in feasibility.iterrows():
if target <= 0:
break
# any special logic to filter these devs?
# remove new dev sqft from target
target -= d.non_residential_sqft
# add redeveloped sqft to target
filt = "general_type == 'Retail' and parcel_id == %d" % \
d["parcel_id"]
target += bldgs.query(filt).non_residential_sqft.sum()
devs.append(d)
if len(devs) == 0:
return
# record keeping - add extra columns to match building dataframe
# add the buidings and demolish old buildings, and add to debug output
devs = pd.DataFrame(devs, columns=buildings.local_columns)
print "Building {:,} retail sqft in {:,} projects".format(
devs.non_residential_sqft.sum(), len(devs))
if target > 0:
print " WARNING: retail target not met"
devs["form"] = "retail"
devs = add_extra_columns_func(devs)
add_buildings(buildings, devs)
summary.add_parcel_output(devs)
def retail_developer(jobs, buildings, parcels, nodes, feasibility,
settings, summary, add_extra_columns_func, net):
dev_settings = settings['non_residential_developer']
all_units = dev.compute_units_to_build(
len(jobs),
buildings.job_spaces.sum(),
dev_settings['kwargs']['target_vacancy'])
target = all_units * float(dev_settings['type_splits']["Retail"])
# target here is in sqft
target *= settings["building_sqft_per_job"]["HS"]
feasibility = feasibility.to_frame().loc[:, "retail"]
feasibility = feasibility.dropna(subset=["max_profit"])
feasibility["non_residential_sqft"] = \
feasibility.non_residential_sqft.astype("int")
feasibility["retail_ratio"] = parcels.retail_ratio
feasibility = feasibility.reset_index()
# create features
f1 = feasibility.retail_ratio / feasibility.retail_ratio.max()
f2 = feasibility.max_profit / feasibility.max_profit.max()
# combine features in probability function - it's like combining expense
# of building the building with the market in the neighborhood
p = f1 * 1.5 + f2
p = p.clip(lower=1.0/len(p)/10)
print "Attempting to build {:,} retail sqft".format(target)
# order by weighted random sample
feasibility = feasibility.sample(frac=1.0, weights=p)
bldgs = buildings.to_frame(buildings.local_columns + ["general_type"])
devs = []
for dev_id, d in feasibility.iterrows():
if target <= 0:
break
# any special logic to filter these devs?
# remove new dev sqft from target
target -= d.non_residential_sqft
# add redeveloped sqft to target
filt = "general_type == 'Retail' and parcel_id == %d" % \
d["parcel_id"]
target += bldgs.query(filt).non_residential_sqft.sum()
devs.append(d)
if len(devs) == 0:
return
# record keeping - add extra columns to match building dataframe
# add the buidings and demolish old buildings, and add to debug output
devs = pd.DataFrame(devs, columns=feasibility.columns)
print "Building {:,} retail sqft in {:,} projects".format(
devs.non_residential_sqft.sum(), len(devs))
if target > 0:
print " WARNING: retail target not met"
devs["form"] = "retail"
devs = add_extra_columns_func(devs)
add_buildings(buildings, devs)
summary.add_parcel_output(devs)
def subsidized_office_developer(feasibility, coffer, acct_settings, year,
add_extra_columns_func, buildings, summary):
# get the total subsidy for subsidizing office
total_subsidy = coffer["vmt_com_acct"].\
total_transactions_by_subacct("regional")
# get the office feasibility frame and sort by profit per sqft
feasibility = feasibility.to_frame().loc[:, "office"]
feasibility = feasibility.dropna(subset=["max_profit"])
feasibility["pda_id"] = feasibility.pda
# filter to receiving zone
feasibility = feasibility.\
query(acct_settings["vmt_settings"]["receiving_buildings_filter"])
feasibility["non_residential_sqft"] = \
feasibility.non_residential_sqft.astype("int")
feasibility["max_profit_per_sqft"] = feasibility.max_profit / \
feasibility.non_residential_sqft
# sorting by our choice metric as we're going to pick our devs
# in order off the top
feasibility = feasibility.sort_values(['max_profit_per_sqft'])
# make parcel_id available
feasibility = feasibility.reset_index()
print "%.0f subsidy with %d developments to choose from" % (
total_subsidy, len(feasibility))
devs = []
for dev_id, d in feasibility.iterrows():
# the logic here is that we allow each dev to have a "normal"
# profit per square foot and once it does we guarantee that it
# gets build - we assume the planning commission for each city
# enables this to happen. If a project gets enough profit already
# we just allow it to compete on the open market - e.g. in the
# non-subsidized office developer
NORMAL_PROFIT_PER_SQFT = 70 # assume price is around $700/sqft
if d.max_profit_per_sqft >= NORMAL_PROFIT_PER_SQFT:
# competes in open market
continue
else:
amt = (NORMAL_PROFIT_PER_SQFT - d.max_profit_per_sqft) * \
d.non_residential_sqft
if amt > total_subsidy:
# we don't have enough money to buy yet
continue
metadata = {
"description": "Developing subsidized office building",
"year": year,
"non_residential_sqft": d["non_residential_sqft"],
"index": dev_id
}
coffer["vmt_com_acct"].add_transaction(-1*amt, subaccount="regional",
metadata=metadata)
total_subsidy -= amt
devs.append(d)
if len(devs) == 0:
return
# record keeping - add extra columns to match building dataframe
# add the buidings and demolish old buildings, and add to debug output
devs = pd.DataFrame(devs, columns=feasibility.columns)
print "Building {:,} subsidized office sqft in {:,} projects".format(
devs.non_residential_sqft.sum(), len(devs))
devs["form"] = "office"
devs = add_extra_columns_func(devs)
add_buildings(buildings, devs)
summary.add_parcel_output(devs)