@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='parcels_in.stories'):
return regroup(stories).median()
@out
def tax_exempt(code='parcels_in.n_tax_code'):
return regroup(code).first().notnull().astype(int)
## Export back to database.
@sim.model()
def export_cnc(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans
df.res_type[df.res_type.isnull()] = ''
df_to_db(df, 'attributes_cnc', schema=staging)
sim.run(['export_cnc'])
import pandas as pd, numpy as np
import models
import urbansim.sim.simulation as sim
np.random.seed(1)
# Simulation run
##Single-year
# sim.run(["build_networks", "neighborhood_vars", "rsh_simulate", "nrh_simulate", "nrh_simulate2",
# "price_vars", "feasibility", "residential_developer", "non_residential_developer"])
##Multi-year
sim.run(["build_networks"]) #initialize network accessibility engine
sim.run(["neighborhood_vars", #"scheduled_development_events", #scheduled events and accessibility variables
"rsh_simulate", "nrh_simulate", "nrh_simulate2", #price models
"jobs_transition", "elcm_simulate", "households_transition", "hlcm_luz_simulate", #demand/location models
"price_vars", "feasibility", "residential_developer", "non_residential_developer", #supply/proforma models
], years=[2013, 2014, 2015,])
# Summarize results at MSA level
b = sim.get_table('buildings').to_frame(columns = ['msa_id', 'mgra_id', 'residential_units', 'non_residential_sqft', 'note'])
new_du_by_msa = b[b.note == 'simulated'].groupby('msa_id').residential_units.sum()
new_nrsf_by_msa = b[b.note == 'simulated'].groupby('msa_id').non_residential_sqft.sum()
proportion_du_by_msa = new_du_by_msa / new_du_by_msa.sum()
proportion_nrsf_by_msa = new_nrsf_by_msa / new_nrsf_by_msa.sum()
# Write out indicators to calibration directory
proportion_du_by_msa.to_csv('.\\data\\calibration\\msa_du_simulated.csv', header = True)
proportion_nrsf_by_msa.to_csv('.\\data\\calibration\\msa_nrsf_simulated.csv', header = True)
residential_units)
@out
def stories(stories='ie673.Stories'):
# 1 story = 10 Alameda County stories.
return 0.1 * stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_ala(parcels_out):
df = parcels_out.to_frame()
# Cast "land_use_type_id" to string for compatibility with other counties.
df['land_use_type_id'] = df.land_use_type_id.astype(str)
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_ala', schema=staging)
sim.run(['export_ala'])
@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='parcels_in.floor'):
# Take greatest number of stories when deduplicating.
return stories.groupby(level=0).max().astype(float)
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_nap(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_nap', schema=staging)
sim.run(['export_nap'])
@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories():
return np.nan
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_son(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_son', schema=staging)
sim.run(['export_son'])
import urbansim.sim.simulation as sim from urbansim_defaults import models import datasources import models import variables sim.run(["build_networks", "neighborhood_vars"]) sim.run(["rsh_estimate"])
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories():
return np.nan
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_smt(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df.res_type[df.res_type.isnull()] = ''
df_to_db(df, 'attributes_smt', schema=staging)
sim.run(['export_smt'])
@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories():
return np.nan
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_sol(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_sol', schema=staging)
sim.run(['export_sol'])
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='scvta.NUMBER_OF1'):
# Field name confirmed by inspecting the Sobrato Office Tower
# (APN 26428171), at 488 Almaden Blvd, San Jose, which is a
# single parcel with a 17-story building.
return stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_scl(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_scl', schema=staging)
sim.run(['export_scl'])
# Alternate inputs:
# - "ressqft_ex"
per_unit = 1. * ressqft / residential_units
per_unit.replace(np.inf, np.nan, inplace=True)
return per_unit
@out
def stories(stories='parcels_in.stories'):
# Alternate inputs:
# - "lidarstori": always zero
return stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_sfr(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_sfr', schema=staging)
sim.run(['export_sfr'])
import pandas as pd, numpy as np
import models
import urbansim.sim.simulation as sim
np.random.seed(1)
# Simulation run
##Single-year
sim.run([
"build_networks", "neighborhood_vars", "rsh_simulate", "nrh_simulate",
"nrh_simulate2", "price_vars", "feasibility", "residential_developer",
"non_residential_developer"
],
years=[
2013,
])
##Multi-year
# sim.run(["build_networks"]) #initialize network accessibility engine
# sim.run(["neighborhood_vars", #"scheduled_development_events", #scheduled events and accessibility variables
# "rsh_simulate", "nrh_simulate", "nrh_simulate2", #price models
# "jobs_transition", "elcm_simulate", "households_transition", "hlcm_luz_simulate", #demand/location models
# "price_vars", "feasibility", "residential_developer", "non_residential_developer", #supply/proforma models
# ], years=[2013, 2014, 2015,])
# Summarize results at target LUZ level
target_luz = [69, 70, 72]
b = sim.get_table('buildings').to_frame(columns=[
'luz_id', 'msa_id', 'mgra_id', 'residential_units', 'non_residential_sqft',
'note'
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='parcels_in.storiesnum'):
return stories.astype(float)
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
# "exemption" column is value of exemptions, which includes welfare.
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_mar(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df.res_type[df.res_type.isnull()] = ''
df_to_db(df, 'attributes_mar', schema=staging)
sim.run(['export_mar'])
import pandas as pd, numpy as np
import models
import urbansim.sim.simulation as sim
np.random.seed(1)
# Simulation run
##Single-year
sim.run(["build_networks", "neighborhood_vars", "rsh_simulate", "nrh_simulate", "nrh_simulate2",
"price_vars", "feasibility", "residential_developer", "non_residential_developer"], years = [2013,])
##Multi-year
# sim.run(["build_networks"]) #initialize network accessibility engine
# sim.run(["neighborhood_vars", #"scheduled_development_events", #scheduled events and accessibility variables
# "rsh_simulate", "nrh_simulate", "nrh_simulate2", #price models
# "jobs_transition", "elcm_simulate", "households_transition", "hlcm_luz_simulate", #demand/location models
# "price_vars", "feasibility", "residential_developer", "non_residential_developer", #supply/proforma models
# ], years=[2013, 2014, 2015,])
# Summarize results at target LUZ level
target_luz = [69, 70, 72]
b = sim.get_table('buildings').to_frame(columns = ['luz_id', 'msa_id', 'mgra_id', 'residential_units', 'non_residential_sqft', 'note'])
b_target = b[b.luz_id.isin(target_luz)]
new_du_region = b[b.note == 'simulated'].residential_units.sum()
new_du_luz = b_target[b_target.note == 'simulated'].residential_units.sum()
proportion_in_luz = new_du_luz*1.0/new_du_region
# Write out indicators to calibration directory
pd.DataFrame([proportion_in_luz]).to_csv('.\\data\\calibration\\luz_du_simulated.csv', index = False)
@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='FIXME'):
return stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_FIXME(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_FIXME', schema=staging)
sim.run(['export_FIXME'])
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='scvta.NUMBER_OF1'):
# Field name confirmed by inspecting the Sobrato Office Tower
# (APN 26428171), at 488 Almaden Blvd, San Jose, which is a
# single parcel with a 17-story building.
return stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_scl(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_scl', schema=staging)
sim.run(['export_scl'])
@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories():
return np.nan
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_smt(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df.res_type[df.res_type.isnull()] = ''
df_to_db(df, 'attributes_smt', schema=staging)
sim.run(['export_smt'])
import pandas as pd, numpy as np
import models
import urbansim.sim.simulation as sim
np.random.seed(1)
# Simulation run
##Single-year
# sim.run(["build_networks", "neighborhood_vars", "rsh_simulate", "nrh_simulate", "nrh_simulate2",
# "price_vars", "feasibility", "residential_developer", "non_residential_developer"])
##Multi-year
sim.run(["build_networks"]) #initialize network accessibility engine
sim.run(
[
"neighborhood_vars", #"scheduled_development_events", #scheduled events and accessibility variables
"rsh_simulate",
"nrh_simulate",
"nrh_simulate2", #price models
"jobs_transition",
"elcm_simulate",
"households_transition",
"hlcm_luz_simulate", #demand/location models
"price_vars",
"feasibility",
"residential_developer",
"non_residential_developer", #supply/proforma models
],
years=[
2013,
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='parcels_in.storiesnum'):
return stories.astype(float)
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
# "exemption" column is value of exemptions, which includes welfare.
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_mar(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df.res_type[df.res_type.isnull()] = ''
df_to_db(df, 'attributes_mar', schema=staging)
sim.run(['export_mar'])
# - "ressqft_ex"
per_unit = 1. * ressqft / residential_units
per_unit.replace(np.inf, np.nan, inplace=True)
return per_unit
@out
def stories(stories='parcels_in.stories'):
# Alternate inputs:
# - "lidarstori": always zero
return stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_sfr(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_sfr', schema=staging)
sim.run(['export_sfr'])
@out
def sqft_per_unit(building_sqft='parcels_out.building_sqft',
non_residential_sqft='parcels_out.non_residential_sqft',
residential_units='parcels_out.residential_units'):
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories():
return np.nan
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_son(parcels_out):
df = parcels_out.to_frame()
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_son', schema=staging)
sim.run(['export_son'])
sim.run(
[
"clear_cache", # recompute variables every year
"run_arcpy",
"neighborhood_vars", # neighborhood variables
"households_transition", # households transition
#"households_relocation", # households relocation model
"jobs_transition", # jobs transition
#"jobs_relocation", # jobs relocation model
"nrh_ind_simulate", # industrial price model
"nrh_ofc_simulate", # office price model
"nrh_ret_simulate", # retail price model
"nrh_mu_oth_simulate", # non-residential mixed-use, other model
"rsh_sf_simulate", # single-family residential price model
"rsh_mf_simulate", # multi-family residential price model
"hlcm_simulate_slc", # households location choice Salt Lake County
"hlcm_simulate_utah", # households location choice Utah County
"hlcm_simulate_davis", # households location choice Davis County
"hlcm_simulate_weber", # households location choice Weber County
"elcm_simulate_slc", # employment location choice Salt Lake County
"elcm_simulate_utah", # employment location choice Utah County
"elcm_simulate_davis", # employment location choice Davis County
"elcm_simulate_weber", # employment location choice Weber County
#"clear_cache",
"indicator_export", # export county and zone level indicators to csv
"travel_model_export_no_construction",
#"trend_calibration",
#"network_prices",
"feasibility", # compute development feasibility
"garbage_collect",
"residential_developer_slc", # build actual buildings Salt Lake County
"residential_developer_utah", # build actual buildings Utah County
"residential_developer_davis", # build actual buildings Davis County
"residential_developer_weber", # build actual buildings Weber County
"office_developer_slc",
"office_developer_utah",
"office_developer_davis",
"office_developer_weber",
"retail_developer_slc",
"retail_developer_utah",
"retail_developer_davis",
"retail_developer_weber",
"industrial_developer_slc",
"industrial_developer_utah",
"industrial_developer_davis",
"industrial_developer_weber",
"travel_model_export_add_construction", # export travel model inputs at TAZ level in specified years
#"travel_time_import",
"run_cube", # call Cube and import travel times in specified years
],
years=range(2015,
2061)) #, data_out=utils.get_run_filename(), out_interval=10)
return utils.get_sqft_per_unit(building_sqft, non_residential_sqft,
residential_units)
@out
def stories(stories='ie673.Stories'):
# 1 story = 10 Alameda County stories.
return 0.1 * stories
@out
def tax_exempt(land_use_type_id='parcels_out.land_use_type_id'):
return utils.get_tax_exempt(land_use_type_id, exempt_codes)
## Export back to database.
@sim.model()
def export_ala(parcels_out):
df = parcels_out.to_frame()
# Cast "land_use_type_id" to string for compatibility with other counties.
df['land_use_type_id'] = df.land_use_type_id.astype(str)
assert df.index.is_unique
assert not df.index.hasnans()
df_to_db(df, 'attributes_ala', schema=staging)
sim.run(['export_ala'])
