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variables.py
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import pandas as pd, numpy as np
from urbansim_defaults import variables, utils
from urbansim_wfrc.utils import misc
import orca_wfrc.orca as sim
import utils as wfrc_utils
@sim.column('jobs', 'building_type_id')
def building_type_id(jobs, buildings):
df = misc.reindex(buildings.building_type_id, jobs.building_id)
return df.fillna(-1).astype('int')
##################
# PARCELS VARIABLES
##################
@sim.column('parcels', 'ave_sqft_per_unit')
def ave_sqft_per_unit(parcels, nodes, settings, zoning_baseline):
if len(nodes) == 0:
# if nodes isn't generated yet
return pd.Series(index=parcels.index)
s = misc.reindex(nodes.ave_sqft_per_unit, parcels.node_id)
clip = settings.get("ave_sqft_per_unit_clip", None)
if clip is not None:
s = pd.concat([s, zoning_baseline.type2], axis=1)
s.right[s.type2==1] = s.right[s.type2==1].clip(lower=clip['lower'], upper=clip['mf_upper'])
s.right[s.type2==0] = s.right[s.type2==0].clip(lower=clip['lower'], upper=clip['sf_upper'])
return s.right.fillna(clip['lower'])
@sim.column("parcels", "parcel_acres")
def parcel_acres(parcels):
return parcels.local.parcel_acres
@sim.column('parcels', 'node_id')
def node_id(parcels, net):
return net.get_node_ids(parcels.x, parcels.y)
@sim.column('parcels', 'lot_size_per_unit')
def log_size_per_unit(parcels):
ls = parcels.parcel_size / parcels.total_residential_units.replace(0, 1)
return ls.fillna(0)
@sim.column('parcels', 'max_far', cache=True)
def max_far(parcels, scenario, scenario_inputs):
far = utils.conditional_upzone(scenario, scenario_inputs, "max_far", "far_up").reindex(parcels.index)
#downtownTAZ = pd.read_csv(r"DOWNTOWNTAZ\DOWNTOWNTAZ.csv")
#far[(-parcels.zone_id.isin(list(downtownTAZ.TAZID))) & (far > 0.5)] = 0.5
#far[parcels.county_id == 4] = far[parcels.county_id == 4] * 0.8
far[parcels.county_id == 35] = far[parcels.county_id == 35] * 1.05
return far
#far[parcels.parcel_size > 3000000] = far[parcels.parcel_size > 3000000]* 0.5
@sim.column('parcels', 'max_dua', cache=True)
def max_dua(parcels, year, scenario, scenario_inputs):
dua = utils.conditional_upzone(scenario, scenario_inputs, "max_dua", "dua_up").reindex(parcels.index)
#Salt Lake County 10% increase for base year
dua[parcels.county_id == 35] = dua[parcels.county_id == 35]*1.1
dua[parcels.county_id == 35] = dua[parcels.county_id == 35]*1.05
#Davis County 2030 20% increase of capacity. 2040 40% increase of capacity
if year >= 2025 and year < 2035:
dua[parcels.county_id == 11] = dua[parcels.county_id == 11]*1.15
elif year >=2035:
dua[parcels.county_id == 11] = dua[parcels.county_id == 11]*1.3
return dua
@sim.column('parcels', 'max_height', cache=True)
def max_height(parcels, zoning_baseline):
return zoning_baseline.max_height.reindex(parcels.index).fillna(0)
@sim.column('parcels', 'residential_purchase_price_sqft')
def residential_purchase_price_sqft(parcels):
return parcels.building_purchase_price_sqft
@sim.column('parcels', 'residential_sales_price_sqft')
def residential_sales_price_sqft(parcel_sales_price_sqft_func):
return parcel_sales_price_sqft_func("residential")
# for debugging reasons this is split out into its own function
@sim.column('parcels', 'building_purchase_price_sqft')
def building_purchase_price_sqft():
return parcel_average_price("residential") * 1
@sim.column('parcels', 'building_purchase_price')
def building_purchase_price(parcels):
return (parcels.total_sqft * parcels.building_purchase_price_sqft).\
reindex(parcels.index).fillna(0)
"""@sim.column('parcels', 'row')
def row(parcels):
r = pd.read_csv("./data/row_parcels.csv")
p = parcels.index.isin(r.parcel_id)
return p.astype('int')"""
@sim.column('parcels', 'redev_friction')
def redev_friction(parcels,year):
s = pd.read_csv("data/redev_friction.csv", index_col="parcel_id")
sredev = s.redev_friction.reindex(parcels.index).fillna(5)
sredev[parcels.county_id == 49] = 10
if year >=2035:
sredev[parcels.county_id == 11] = sredev[parcels.county_id == 11]/2
return sredev
@sim.column('parcels', 'parcel_volume')
def parcel_volume(parcels, year, settings):
if year < 2023:
file = settings['tdm']['main_dir'] + '2019/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
elif year in range(2023,2028):
file = settings['tdm']['main_dir'] + '2023/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
elif year in range(2028,2032):
file = settings['tdm']['main_dir'] + '2028/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
elif year in range(2032,2037):
file = settings['tdm']['main_dir'] + '2032/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
#elif year in range(2031,2035):
# file = settings['tdm']['main_dir'] + '2030/' + settings['tdm']['output_distance']
# p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2037,2042):
file = settings['tdm']['main_dir'] + '2037/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
elif year in range(2042,2046):
file = settings['tdm']['main_dir'] + '2042/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
elif year in range(2046,2050):
file = settings['tdm']['main_dir'] + '2046/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
elif year in range(2050,2099):
file = settings['tdm']['main_dir'] + '2050/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
else:
file = settings['tdm']['main_dir'] + '2019/6_REMM/volumepoint.dbf'
s = wfrc_utils.dbf2df(file).set_index('parcel_id')
svolume = s.RASTERVALU.reindex(parcels.index).fillna(0)
return svolume
@sim.column('parcels', 'gridID')
def gridID(parcels):
s = pd.read_csv("data/parcelgridID.csv", index_col="parcel_id")
return s.GRIDID.reindex(parcels.index).fillna(0)
@sim.column('parcels', 'agriculture')
def agriculture(parcels):
s = pd.read_csv("data/agriculture.csv", index_col="parcel_id")
return s.Agriculture.reindex(parcels.index).fillna(0)
@sim.column('parcels', 'land_cost')
def land_cost(parcels):
result = parcels.building_purchase_price + parcels.land_value
result[parcels.building_purchase_price > 0] = result[parcels.building_purchase_price > 0] * parcels.redev_friction
return result
@sim.column('parcels', 'distlrg_res_shift')
def distlrg_res_shift(parcels):
s = pd.read_csv("data/calibration/distlrg_shifters.csv", index_col="distlrg_id")
return misc.reindex(s.res_price_shifter, parcels.distlrg_id)
@sim.column('parcels', 'distlrg_nonres_shift')
def distlrg_nonres_shift(parcels):
s = pd.read_csv("data/calibration/distlrg_shifters.csv", index_col="distlrg_id")
return misc.reindex(s.nonres_price_shifter, parcels.distlrg_id)
@sim.column('parcels', 'avg_building_age')
def avg_building_age(parcels, buildings):
return buildings.building_age.groupby(buildings.parcel_id).mean().\
reindex(parcels.index).fillna(-99)
@sim.column('parcels', 'bus_stop_dist', cache=True)
def bus_stop_dist(parcels, year, settings):
if year < 2023:
file = settings['tdm']['main_dir'] + '2019/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2023,2028):
file = settings['tdm']['main_dir'] + '2023/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2028,2032):
file = settings['tdm']['main_dir'] + '2028/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2032,2037):
file = settings['tdm']['main_dir'] + '2032/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2037,2042):
file = settings['tdm']['main_dir'] + '2037/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2042,2046):
file = settings['tdm']['main_dir'] + '2042/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
#elif year in range(2031,2035):
# file = settings['tdm']['main_dir'] + '2030/' + settings['tdm']['output_distance']
# p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2046,2050):
file = settings['tdm']['main_dir'] + '2046/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2050,2099):
file = settings['tdm']['main_dir'] + '2050/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
else:
file = settings['tdm']['main_dir'] + '2019/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
return misc.reindex(p.dist2busst, parcels.parent_parcel)
#return p.dist2busst
@sim.column('parcels', 'fwy_exit_dist_tdm_output', cache=True)
def fwy_exit_dist_tdm_output(parcels, year, settings):
if year < 2023:
file = settings['tdm']['main_dir'] + '2019/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
elif year in range(2023,2028):
file = settings['tdm']['main_dir'] + '2023/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
elif year in range(2028,2032):
file = settings['tdm']['main_dir'] + '2028/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
elif year in range(2032,2037):
file = settings['tdm']['main_dir'] + '2032/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
elif year in range(2037,2042):
file = settings['tdm']['main_dir'] + '2037/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
elif year in range(2042,2046):
file = settings['tdm']['main_dir'] + '2042/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
#elif year in range(2031,2035):
# file = settings['tdm']['main_dir'] + '2030/' + settings['tdm']['output_fwy']
# ff = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2046,2050):
file = settings['tdm']['main_dir'] + '2046/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
elif year in range(2050,2099):
file = settings['tdm']['main_dir'] + '2050/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
else:
file = settings['tdm']['main_dir'] + '2019/' + settings['tdm']['output_fwy']
ff = wfrc_utils.dbf2df(file)
p = sim.get_table('parcels')
p = p.to_frame(['x', 'y'])
fe = ff[(ff.FT == 42) | (ff.FT == 29)]
nc = pd.read_csv("data/TDMNODE.csv")
feloc = pd.merge(fe, nc, how='left', left_on="B", right_on="N", suffixes=('','_x'))
pdist = np.sqrt((p.x-feloc.loc[0].URBANSIMX)**2 + (p.y-feloc.loc[0].URBANSIMY)**2)
for i in range(1,len(feloc)):
pdistx = np.sqrt((p.x-feloc.loc[i].URBANSIMX)**2 + (p.y-feloc.loc[i].URBANSIMY)**2)
pdist[pdistx < pdist] = pdistx[pdistx < pdist]
return pdist*0.00018939
@sim.column('parcels', 'rail_stn_dist', cache=True)
def rail_stn_dist(parcels, year, settings):
if year < 2023:
file = settings['tdm']['main_dir'] + '2019/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2023,2028):
file = settings['tdm']['main_dir'] + '2023/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2028,2032):
file = settings['tdm']['main_dir'] + '2028/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2032,2037):
file = settings['tdm']['main_dir'] + '2032/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2037,2042):
file = settings['tdm']['main_dir'] + '2037/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2042,2046):
file = settings['tdm']['main_dir'] + '2042/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
#elif year in range(2031,2035):
# file = settings['tdm']['main_dir'] + '2030/' + settings['tdm']['output_distance']
# p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2046,2050):
file = settings['tdm']['main_dir'] + '2046/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
elif year in range(2050,2099):
file = settings['tdm']['main_dir'] + '2050/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
else:
file = settings['tdm']['main_dir'] + '2019/' + settings['tdm']['output_distance']
p = wfrc_utils.dbf2df(file).set_index('parcel_id')
return misc.reindex(p.dist2rails, parcels.parent_parcel)
#return p.dist2rails
# these are actually functions that take parameters, but are parcel-related
# so are defined here
@sim.injectable('parcel_median_price', autocall=False)
def parcel_median_price(use):
btypes = sim.settings['form_to_btype'][use]
rpm = sim.settings['res_price_multiplier']
nrpm = sim.settings['nonres_price_multiplier']
b = sim.get_table('buildings')
p = sim.get_table('parcels')
if use == "residential":
distmed = []
zones = []
bframe = b.to_frame(['zone_id','building_type_id','res_price_per_sqft','unit_price_non_residential','distmed_id','residential_units'])
zonesum = bframe.groupby('zone_id').agg({'residential_units':'sum'})
tazmax = p.zone_id.max()
zonesum = zonesum.reindex(index = range(1,tazmax+1), fill_value = 0)
for zone, units in zonesum.iterrows():
if int(units) > 20:
zones.append(zone)
else:
distmed.append(zone)
prz = misc.reindex(b.res_price_per_sqft[(b.zone_id.isin(zones))&(b.building_type_id.isin(btypes))&(b.res_price_per_sqft>0)&(b.res_price_per_sqft < 400)].groupby(b.zone_id).quantile(0.75), p.zone_id)
prd = misc.reindex(b.res_price_per_sqft[(b.zone_id.isin(distmed))&(b.building_type_id.isin(btypes))&(b.res_price_per_sqft>0)&(b.res_price_per_sqft < 400)].groupby(b.distmed_id).quantile(0.75), p.distmed_id[p.zone_id.isin(distmed)])
pr = pd.concat([prz[~np.isnan(prz)], prd[~np.isnan(prd)]])
pr = pr.reindex(index = range(1,p.index.max()+1),fill_value = 50)
pr = np.clip(pr, 15, 400)*rpm
return pr * 0.1646 * 2
elif use == "industrial":
pr = misc.reindex(b.unit_price_non_residential[(b.building_type_id.isin(btypes))&(b.unit_price_non_residential>0)&(b.unit_price_non_residential < 100)].groupby(b.zone_id).median() * nrpm, p.zone_id).fillna(6)*2
pr = pr.reindex(index = range(1,p.index.max()+1),fill_value = 12)
pr = np.clip(pr, 12, 100)
return pr * 0.6488 * 2
elif use == "retail":
pr = misc.reindex(b.unit_price_non_residential[(b.building_type_id.isin(btypes))&(b.unit_price_non_residential>0)&(b.unit_price_non_residential < 500)].groupby(b.zone_id).median() * nrpm, p.zone_id).fillna(120)
pr = pr.reindex(index = range(1,p.index.max()+1),fill_value = 120)
pr = np.clip(pr, 120, 500)
return pr * 0.1111 * 2
else:
pr = misc.reindex(b.unit_price_non_residential[(b.building_type_id.isin(btypes))&(b.unit_price_non_residential>0)&(b.unit_price_non_residential < 500)].groupby(b.zone_id).median() * nrpm, p.zone_id).fillna(120)
pr = pr.reindex(index = range(1,p.index.max()+1),fill_value = 120)
pr = np.clip(pr, 120, 500)
return pr * 0.1138 * 2
@sim.injectable('parcel_average_price', autocall=False)
def parcel_average_price(use):
btypes = sim.settings['form_to_btype'][use]
b = sim.get_table('buildings')
if use == "residential":
return misc.reindex(b.res_price_per_sqft[b.building_type_id.isin(btypes)].groupby(b.zone_id).mean(), sim.get_table('parcels').zone_id).fillna(b.res_price_per_sqft[b.building_type_id.isin(btypes)].mean())
else:
return misc.reindex(b.unit_price_non_residential[b.building_type_id.isin(btypes)].groupby(b.zone_id).mean(), sim.get_table('parcels').zone_id).fillna(b.unit_price_non_residential[b.building_type_id.isin(btypes)].mean())
@sim.injectable('parcel_sales_price_sqft_func', autocall=False)
def parcel_sales_price_sqft(use):
return parcel_median_price(use)
@sim.injectable('parcel_is_allowed_func', autocall=False)
def parcel_is_allowed(form):
settings = sim.get_injectable("settings")
form_to_btype = settings["form_to_btype"]
# we have zoning by building type but want
# to know if specific forms are allowed
allowed = [sim.get_table('zoning_baseline')
['type%d' % typ] == 1 for typ in form_to_btype[form]]
return pd.concat(allowed, axis=1).max(axis=1).\
reindex(sim.get_table('parcels').index).fillna(False)
####################
#BUILDINGS VARIABLES
####################
@sim.column('buildings', 'job_spaces', cache=True)
def job_spaces(buildings):
return buildings.local.job_spaces
@sim.column('buildings', 'zone_id', cache=True)
def zone_id(buildings, parcels):
df = misc.reindex(parcels.zone_id, buildings.parcel_id)
return df.fillna(0).astype('int')
@sim.column('buildings', 'node_id', cache=True)
def node_id(buildings, parcels):
df = misc.reindex(parcels.node_id, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'county_id')
def county_id(buildings, parcels):
df = misc.reindex(parcels.county_id, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'distlrg_id')
def distlrg_id(buildings, parcels):
df = misc.reindex(parcels.distlrg_id, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'distmed_id')
def distmed_id(buildings, parcels):
df = misc.reindex(parcels.distmed_id, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'distsml_id')
def distsml_id(buildings, parcels):
df = misc.reindex(parcels.distsml_id, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'distmed_1')
def distmed_1(buildings):
return (buildings.distmed_id==1).astype('int32')
@sim.column('buildings', 'distmed_2')
def distmed_2(buildings):
return (buildings.distmed_id==2).astype('int32')
@sim.column('buildings', 'distmed_3')
def distmed_3(buildings):
return (buildings.distmed_id==3).astype('int32')
@sim.column('buildings', 'distmed_4')
def distmed_4(buildings):
return (buildings.distmed_id==4).astype('int32')
@sim.column('buildings', 'distmed_5')
def distmed_5(buildings):
return (buildings.distmed_id==5).astype('int32')
@sim.column('buildings', 'distmed_6')
def distmed_6(buildings):
return (buildings.distmed_id==6).astype('int32')
@sim.column('buildings', 'distmed_7')
def distmed_7(buildings):
return (buildings.distmed_id==7).astype('int32')
@sim.column('buildings', 'distmed_8')
def distmed_8(buildings):
return (buildings.distmed_id==8).astype('int32')
@sim.column('buildings', 'distmed_9')
def distmed_9(buildings):
return (buildings.distmed_id==9).astype('int32')
@sim.column('buildings', 'distmed_10')
def distmed_10(buildings):
return (buildings.distmed_id==10).astype('int32')
@sim.column('buildings', 'distmed_11')
def distmed_11(buildings):
return (buildings.distmed_id==11).astype('int32')
@sim.column('buildings', 'distmed_12')
def distmed_12(buildings):
return (buildings.distmed_id==12).astype('int32')
@sim.column('buildings', 'distmed_13')
def distmed_13(buildings):
return (buildings.distmed_id==13).astype('int32')
@sim.column('buildings', 'distmed_14')
def distmed_14(buildings):
return (buildings.distmed_id==14).astype('int32')
@sim.column('buildings', 'distmed_15')
def distmed_15(buildings):
return (buildings.distmed_id==15).astype('int32')
@sim.column('buildings', 'distmed_16')
def distmed_16(buildings):
return (buildings.distmed_id==16).astype('int32')
@sim.column('buildings', 'distmed_17')
def distmed_17(buildings):
return (buildings.distmed_id==17).astype('int32')
@sim.column('buildings', 'distmed_18')
def distmed_18(buildings):
return (buildings.distmed_id==18).astype('int32')
@sim.column('buildings', 'distmed_19')
def distmed_19(buildings):
return (buildings.distmed_id==19).astype('int32')
@sim.column('buildings', 'distmed_20')
def distmed_20(buildings):
return (buildings.distmed_id==20).astype('int32')
@sim.column('buildings', 'distmed_21')
def distmed_21(buildings):
return (buildings.distmed_id==21).astype('int32')
@sim.column('buildings', 'distmed_22')
def distmed_22(buildings):
return (buildings.distmed_id==22).astype('int32')
@sim.column('buildings', 'distmed_23')
def distmed_23(buildings):
return (buildings.distmed_id==23).astype('int32')
@sim.column('buildings', 'distmed_24')
def distmed_24(buildings):
return (buildings.distmed_id==24).astype('int32')
@sim.column('buildings', 'distmed_25')
def distmed_25(buildings):
return (buildings.distmed_id==25).astype('int32')
@sim.column('buildings', 'distmed_26')
def distmed_26(buildings):
return (buildings.distmed_id==26).astype('int32')
@sim.column('buildings', 'distmed_27')
def distmed_27(buildings):
return (buildings.distmed_id==27).astype('int32')
@sim.column('buildings', 'distmed_28')
def distmed_28(buildings):
return (buildings.distmed_id==28).astype('int32')
@sim.column('buildings', 'distmed_29')
def distmed_29(buildings):
return (buildings.distmed_id==29).astype('int32')
@sim.column('buildings', 'distmed_30')
def distmed_30(buildings):
return (buildings.distmed_id==30).astype('int32')
@sim.column('buildings', 'distmed_31')
def distmed_31(buildings):
return (buildings.distmed_id==31).astype('int32')
@sim.column('buildings', 'distmed_32')
def distmed_32(buildings):
return (buildings.distmed_id==32).astype('int32')
@sim.column('buildings', 'distmed_33')
def distmed_33(buildings):
return (buildings.distmed_id==33).astype('int32')
@sim.column('buildings', 'distmed_34')
def distmed_34(buildings):
return (buildings.distmed_id==34).astype('int32')
@sim.column('buildings', 'distmed_35')
def distmed_35(buildings):
return (buildings.distmed_id==35).astype('int32')
@sim.column('buildings', 'distmed_36')
def distmed_36(buildings):
return (buildings.distmed_id==36).astype('int32')
@sim.column('buildings', 'distmed_37')
def distmed_37(buildings):
return (buildings.distmed_id==37).astype('int32')
@sim.column('buildings', 'distmed_38')
def distmed_38(buildings):
return (buildings.distmed_id==38).astype('int32')
@sim.column('buildings', 'distmed_39')
def distmed_39(buildings):
return (buildings.distmed_id==39).astype('int32')
@sim.column('buildings', 'distmed_40')
def distmed_40(buildings):
return (buildings.distmed_id==40).astype('int32')
@sim.column('buildings', 'distmed_41')
def distmed_41(buildings):
return (buildings.distmed_id==41).astype('int32')
@sim.column('buildings', 'distmed_42')
def distmed_42(buildings):
return (buildings.distmed_id == 42).astype('int32')
@sim.column('buildings', 'distmed_43')
def distmed_43(buildings):
return (buildings.distmed_id == 43).astype('int32')
@sim.column('buildings', 'distmed_44')
def distmed_44(buildings):
return (buildings.distmed_id == 44).astype('int32')
@sim.column('buildings', 'distmed_45')
def distmed_45(buildings):
return (buildings.distmed_id == 45).astype('int32')
@sim.column('buildings', 'distmed_46')
def distmed_46(buildings):
return (buildings.distmed_id == 46).astype('int32')
@sim.column('buildings', 'distmed_47')
def distmed_47(buildings):
return (buildings.distmed_id == 47).astype('int32')
@sim.column('buildings', 'distmed_48')
def distmed_48(buildings):
return (buildings.distmed_id == 48).astype('int32')
@sim.column('buildings', 'distmed_49')
def distmed_49(buildings):
return (buildings.distmed_id == 49).astype('int32')
@sim.column('buildings', 'distmed_50')
def distmed_50(buildings):
return (buildings.distmed_id == 50).astype('int32')
@sim.column('buildings', 'distmed_51')
def distmed_51(buildings):
return (buildings.distmed_id == 51).astype('int32')
@sim.column('buildings', 'distmed_52')
def distmed_52(buildings):
return (buildings.distmed_id == 52).astype('int32')
@sim.column('buildings', 'distmed_53')
def distmed_53(buildings):
return (buildings.distmed_id == 53).astype('int32')
@sim.column('buildings', 'distmed_54')
def distmed_54(buildings):
return (buildings.distmed_id == 54).astype('int32')
@sim.column('buildings', 'distmed_55')
def distmed_55(buildings):
return (buildings.distmed_id == 55).astype('int32')
@sim.column('buildings', 'distmed_56')
def distmed_56(buildings):
return (buildings.distmed_id == 56).astype('int32')
@sim.column('buildings', 'distmed_57')
def distmed_57(buildings):
return (buildings.distmed_id == 57).astype('int32')
@sim.column('buildings', 'distmed_58')
def distmed_58(buildings):
return (buildings.distmed_id == 58).astype('int32')
@sim.column('buildings', 'distmed_59')
def distmed_59(buildings):
return (buildings.distmed_id == 59).astype('int32')
@sim.column('buildings', 'distmed_60')
def distmed_60(buildings):
return (buildings.distmed_id == 60).astype('int32')
@sim.column('buildings', 'distmed_61')
def distmed_61(buildings):
return (buildings.distmed_id == 61).astype('int32')
@sim.column('buildings', 'distmed_62')
def distmed_62(buildings):
return (buildings.distmed_id == 62).astype('int32')
@sim.column('buildings', 'distmed_63')
def distmed_63(buildings):
return (buildings.distmed_id == 63).astype('int32')
@sim.column('buildings', 'distmed_64')
def distmed_64(buildings):
return (buildings.distmed_id == 64).astype('int32')
@sim.column('buildings', 'zone_1212')
def zone_1212(buildings):
return (buildings.zone_id==1212).astype('int32')
@sim.column('buildings', 'building_age', cache=True, cache_scope='iteration')
def building_age(buildings, year):
if year > 2010:
df = (year - buildings.year_built).clip(lower=0)
df[df > 200] = 30
return df
else:
df = (2010 - buildings.year_built).clip(lower=0)
df[df > 200] = 30
return df
"""@sim.column("buildings", "res_price_per_sqft", cache=False)
def res_price_per_sqft(buildings, year):
return buildings.unit_price_residential / buildings.sqft_per_unit.replace(0, 1)"""
@sim.column("buildings", "building_sqft", cache=True)
def building_sqft(buildings):
return buildings.local.building_sqft.clip(upper=5000000)
@sim.column("buildings", "real_far", cache=True)
def real_far(buildings):
df = buildings.building_sqft.fillna(0)/(buildings.parcel_acres.fillna(0.001)*43560)
df = df.replace(np.inf, 0)
df = df.clip(upper=50)
return df.fillna(0)
@sim.column("buildings", "land_value", cache=True)
def land_value(buildings, parcels):
df = misc.reindex(parcels.land_value, buildings.parcel_id)
return df.fillna(0)
# @sim.column("buildings", "utmxi", cache=True)
# def utmxi(buildings, parcels):
# df = misc.reindex(parcels.utmxi, buildings.parcel_id)
# return df.fillna(0)
#
# @sim.column("buildings", "utmyi", cache=True)
# def utmyi(buildings, parcels):
# df = misc.reindex(parcels.utmyi, buildings.parcel_id)
# return df.fillna(0)
@sim.column('buildings', 'avg_building_age')
def avg_building_age(parcels, buildings):
df = misc.reindex(parcels.avg_building_age, buildings.parcel_id)
return df.fillna(0)
@sim.column("buildings", "parcel_acres", cache=True)
def parcel_acres(buildings, parcels):
df = misc.reindex(parcels.parcel_acres, buildings.parcel_id)
return df.fillna(0)
@sim.column("buildings", "parcel_volume", cache=True)
def parcel_volume(buildings, parcels):
df = misc.reindex(parcels.parcel_volume, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'elevation', cache=True)
def elevation(buildings, parcels):
return misc.reindex(parcels.elevation, buildings.parcel_id).fillna(parcels.elevation.median())
@sim.column('buildings', 'volume_two_way', cache=True)
def volume_two_way(buildings, parcels):
df = misc.reindex(parcels.volume_two_way, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'volume_two_way_nofwy', cache=True)
def volume_two_way_nofwy(buildings, parcels):
df = misc.reindex(parcels.volume_two_way_nofwy, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'max_far', cache=True)
def max_far(buildings, parcels):
df = misc.reindex(parcels.max_far, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'residential_sqft', cache=True)
def residential_sqft(buildings):
df = buildings.residential_units * buildings.sqft_per_unit
return df
@sim.column('buildings', 'is_sf', cache=True)
def is_sf(buildings):
return (buildings.building_type_id==1).astype('int32')
@sim.column('buildings', 'is_mf', cache=True)
def is_mf(buildings):
return (buildings.building_type_id==2).astype('int32')
@sim.column('buildings', 'is_industrial', cache=True)
def is_industrial(buildings):
return (buildings.building_type_id==3).astype('int32')
@sim.column('buildings', 'is_retail', cache=True)
def is_retail(buildings):
return (buildings.building_type_id==4).astype('int32')
@sim.column('buildings', 'is_office', cache=True)
def is_office(buildings):
return (buildings.building_type_id==5).astype('int32')
@sim.column('buildings', 'is_govt', cache=True)
def is_govt(buildings):
return (buildings.building_type_id==6).astype('int32')
@sim.column('buildings', 'is_mixeduse', cache=True)
def is_mixeduse(buildings):
return (buildings.building_type_id==7).astype('int32')
@sim.column('buildings', 'is_other', cache=True)
def is_other(buildings):
return (buildings.building_type_id==8).astype('int32')
@sim.column('buildings', 'airport', cache=True)
def airport(buildings, parcels):
df = misc.reindex(parcels.airport, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'airport_distance', cache=True)
def airport_distance(buildings, parcels):
df = misc.reindex(parcels.airport_distance, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'fwy_exit_dist', cache=True)
def fwy_exit_dist(buildings, parcels):
df = misc.reindex(parcels.fwy_exit_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'fwy_exit_dist_tdm_output', cache=True)
def fwy_exit_dist_tdm_output(buildings, parcels):
df = misc.reindex(parcels.fwy_exit_dist_tdm_output, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'raildepot_dist', cache=True)
def raildepot_dist(buildings, parcels):
df = misc.reindex(parcels.raildepot_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'university_dist', cache=True)
def university_dist(buildings, parcels):
df = misc.reindex(parcels.university_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'trail_dist', cache=True)
def trail_dist(buildings, parcels):
df = misc.reindex(parcels.trail_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'stream_dist', cache=True)
def stream_dist(buildings, parcels):
df = misc.reindex(parcels.stream_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'rail_stn_dist', cache=True)
def rail_stn_dist(buildings, parcels):
df = misc.reindex(parcels.rail_stn_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'bus_rte_dist', cache=True)
def bus_rte_dist(buildings, parcels):
df = misc.reindex(parcels.bus_rte_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'bus_stop_dist', cache=True)
def bus_stop_dist(buildings, parcels):
df = misc.reindex(parcels.bus_stop_dist, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'residential_sales_price_sqft', cache=True)
def residential_sales_price_sqft(buildings, parcels):
df = misc.reindex(parcels.residential_sales_price_sqft, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'total_job_spaces', cache=True)
def total_job_spaces(buildings, parcels):
df = misc.reindex(parcels.total_job_spaces, buildings.parcel_id)
return df.fillna(0)
@sim.column('buildings', 'rail_depot', cache=True)
def rail_depot(buildings, parcels):
df = misc.reindex(parcels.rail_depot, buildings.parcel_id)
return df.fillna(0)
@sim.column("buildings", "improvement_value", cache=True)
def improvement_value(buildings):
return (buildings.res_price_per_sqft * buildings.building_sqft).fillna(0)
@sim.column("buildings", "unit_price_residential", cache=True)
def unit_price_residential(buildings):
df = (buildings.improvement_value / buildings.residential_units)
df = df.replace([np.inf, -np.inf], np.nan)
return df.fillna(0)
@sim.column('buildings', 'distlrg_med_inc')
def distlrg_med_inc(buildings, distlrg):
df = misc.reindex(distlrg.distlrg_median_income, buildings.distlrg_id)
return df.fillna(0)
@sim.column('buildings', 'distmed_med_inc')
def distmed_med_inc(buildings, distmed):
df = misc.reindex(distmed.distmed_median_income, buildings.distmed_id)
return df.fillna(0)
@sim.column('buildings', 'distsml_med_inc')
def distsml_med_inc(buildings, distsml):
df = misc.reindex(distsml.distsml_median_income, buildings.distsml_id)
return df.fillna(0)
####################################
# BUILDINGS FOR ESTIMATION VARIABLES
####################################
@sim.column('buildings_for_estimation', 'node_id', cache=True)
def node_id(buildings_for_estimation, parcels):
return misc.reindex(parcels.node_id, buildings_for_estimation.parcel_id)
@sim.column('buildings_for_estimation', 'zone_id', cache=True)
def zone_id(buildings_for_estimation, parcels):
return misc.reindex(parcels.zone_id, buildings_for_estimation.parcel_id)
@sim.column('buildings_for_estimation', 'county_id', cache=True)
def zone_id(buildings_for_estimation, parcels):
return misc.reindex(parcels.county_id, buildings_for_estimation.parcel_id)
@sim.column('buildings_for_estimation', 'general_type', cache=True)
def general_type(buildings_for_estimation, building_type_map):
return buildings_for_estimation.building_type_id.map(building_type_map)
@sim.column('buildings_for_estimation', 'sqft_per_unit', cache=True)
def unit_sqft(buildings_for_estimation):
return buildings_for_estimation.building_sqft / buildings_for_estimation.residential_units.replace(0, 1)
@sim.column('buildings_for_estimation', 'lot_size_per_unit', cache=True)
def lot_size_per_unit(buildings_for_estimation, parcels):
return misc.reindex(parcels.lot_size_per_unit, buildings_for_estimation.parcel_id)
@sim.column('buildings_for_estimation', 'sqft_per_job', cache=True)
def sqft_per_job(buildings_for_estimation, building_sqft_per_job):
return buildings_for_estimation.building_type_id.fillna(-1).map(building_sqft_per_job)
@sim.column('buildings_for_estimation', 'job_spaces', cache=True)
def job_spaces(buildings_for_estimation):
return (buildings_for_estimation.non_residential_sqft /
buildings_for_estimation.sqft_per_job).fillna(0).astype('int')
@sim.column('buildings_for_estimation', 'vacant_residential_units')
def vacant_residential_units(buildings_for_estimation, households):
return buildings_for_estimation.residential_units.sub(
households.building_id.value_counts(), fill_value=0)
@sim.column('buildings_for_estimation', 'vacant_job_spaces')
def vacant_job_spaces(buildings_for_estimation, jobs):
return buildings_for_estimation.job_spaces.sub(
jobs.building_id.value_counts(), fill_value=0)
@sim.column("buildings_for_estimation", "land_value", cache=True)
def land_value(buildings_for_estimation, parcels):
df = misc.reindex(parcels.land_value, buildings_for_estimation.parcel_id)
return df.fillna(0)
@sim.column("buildings_for_estimation", "parcel_acres", cache=True)
def parcel_acres(buildings_for_estimation, parcels):
df = misc.reindex(parcels.parcel_acres, buildings_for_estimation.parcel_id)
return df.fillna(0)
@sim.column("buildings_for_estimation", "parcel_volume", cache=True)
def parcel_acres(buildings_for_estimation, parcels):
df = misc.reindex(parcels.parcel_volume, buildings_for_estimation.parcel_id)
return df.fillna(0)
@sim.column('buildings_for_estimation', 'elevation', cache=True)
def elevation(buildings_for_estimation, parcels):
return misc.reindex(parcels.elevation, buildings_for_estimation.parcel_id)
@sim.column('buildings_for_estimation', 'volume_two_way', cache=True)
def volume_two_way(buildings_for_estimation, parcels):
return misc.reindex(parcels.volume_two_way, buildings_for_estimation.parcel_id)
############################################
# GROUPED BUILDINGS FOR ESTIMATION VARIABLES
############################################
@sim.column('buildings_for_estimation_grouped', 'node_id', cache=True)
def node_id(buildings_for_estimation_grouped, parcels):
return misc.reindex(parcels.node_id, buildings_for_estimation_grouped.parcel_id)
@sim.column('buildings_for_estimation_grouped', 'zone_id', cache=True)
def zone_id(buildings_for_estimation_grouped, parcels):
return misc.reindex(parcels.zone_id, buildings_for_estimation_grouped.parcel_id)
@sim.column('buildings_for_estimation_grouped', 'county_id', cache=True)
def zone_id(buildings_for_estimation_grouped, parcels):
return misc.reindex(parcels.county_id, buildings_for_estimation_grouped.parcel_id)
@sim.column('buildings_for_estimation_grouped', 'distmed_id')
def distmed_id(buildings_for_estimation_grouped, parcels):
df = misc.reindex(parcels.distmed_id, buildings_for_estimation_grouped.parcel_id)
return df.fillna(0)
@sim.column('buildings_for_estimation_grouped', 'distsml_id')
def distsml_id(buildings_for_estimation_grouped, parcels):
df = misc.reindex(parcels.distsml_id, buildings_for_estimation_grouped.parcel_id)
return df.fillna(0)
@sim.column('buildings_for_estimation_grouped', 'distmed_1')
def distmed_1(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==1).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_2')
def distmed_2(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==2).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_3')
def distmed_3(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==3).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_4')
def distmed_4(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==4).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_5')
def distmed_5(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==5).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_6')
def distmed_6(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==6).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_7')
def distmed_7(buildings_for_estimation_grouped):
return (buildings_for_estimation_grouped.distmed_id==7).astype('int32')
@sim.column('buildings_for_estimation_grouped', 'distmed_8')
def distmed_8(buildings_for_estimation_grouped):