def test__write_metadata(self):
try:
from opus_core.indicator_framework.image_types.table import Table
except:
pass
else:
table = Table(
source_data=self.cross_scenario_source_data,
attribute='opus_core.test.attribute',
dataset_name='test',
output_type='tab',
years=[
1980, 1981
] # Indicators are not actually being computed, so the years don't matter here.
)
table.create(False)
table.date_computed = None
output = self.data_manager._export_indicator_to_file(
indicator=table,
source_data=self.cross_scenario_source_data,
year=None)
expected = [
'<version>1.0</version>', '<Table>',
'\t<attributes>[\'opus_core.test.attribute\']</attributes>',
'\t<dataset_name>test</dataset_name>',
'\t<years>[1980, 1981]</years>',
'\t<date_computed>None</date_computed>',
'\t<name>attribute</name>', '\t<operation>None</operation>',
'\t<storage_location>%s</storage_location>' %
os.path.join(self.temp_cache_path, 'indicators'),
'\t<output_type>tab</output_type>', '\t<source_data>',
'\t\t<cache_directory>%s</cache_directory>' %
self.temp_cache_path,
'\t\t<comparison_cache_directory>%s</comparison_cache_directory>'
% self.temp_cache_path2,
'\t\t<run_description>%s</run_description>' %
self.cross_scenario_source_data.get_run_description(),
'\t\t<years>[1980]</years>',
'\t\t<package_order>[\'opus_core\']</package_order>',
'\t</source_data>', '</Table>'
]
for i in range(len(output)):
if expected[i] != output[i]:
print expected[i]
print output[i]
self.assertEqual(output, expected)
def test__write_metadata(self):
try:
from opus_core.indicator_framework.image_types.table import Table
except: pass
else:
table = Table(
source_data = self.cross_scenario_source_data,
attribute = 'opus_core.test.attribute',
dataset_name = 'test',
output_type = 'tab',
years = [1980,1981] # Indicators are not actually being computed, so the years don't matter here.
)
table.create(False)
table.date_computed = None
output = self.data_manager._export_indicator_to_file(
indicator = table,
source_data = self.cross_scenario_source_data,
year = None)
expected = [
'<version>1.0</version>',
'<Table>',
'\t<attributes>[\'opus_core.test.attribute\']</attributes>',
'\t<dataset_name>test</dataset_name>',
'\t<years>[1980, 1981]</years>',
'\t<date_computed>None</date_computed>',
'\t<name>attribute</name>',
'\t<operation>None</operation>',
'\t<storage_location>%s</storage_location>'%os.path.join(self.temp_cache_path, 'indicators'),
'\t<output_type>tab</output_type>',
'\t<source_data>',
'\t\t<cache_directory>%s</cache_directory>'%self.temp_cache_path,
'\t\t<comparison_cache_directory>%s</comparison_cache_directory>'%self.temp_cache_path2,
'\t\t<run_description>%s</run_description>'%self.cross_scenario_source_data.get_run_description(),
'\t\t<years>[1980]</years>',
'\t\t<package_order>[\'opus_core\']</package_order>',
'\t</source_data>',
'</Table>'
]
for i in range(len(output)):
if expected[i] != output[i]:
print expected[i]
print output[i]
self.assertEqual(output,expected)
def test__read_write_metadata(self):
try:
from opus_core.indicator_framework.image_types.table import Table
except:
raise
else:
table = Table(
source_data=self.source_data,
attribute='opus_core.test.attribute',
dataset_name='test',
output_type='tab',
years=[
1980, 1981
] # Indicators are not actually being computed, so the years don't matter here.
)
table.create(False)
self.data_manager._export_indicator_to_file(
indicator=table, source_data=self.source_data, year=None)
metadata_file = table.get_file_name(extension='meta')
metadata_path = os.path.join(table.get_storage_location(),
metadata_file)
self.assertEqual(os.path.exists(metadata_path), True)
expected_path = 'test__tab__attribute.meta'
self.assertEqual(metadata_file, expected_path)
new_table = self.data_manager._import_indicator_from_file(
metadata_path)
for attr in [
'attributes', 'dataset_name', 'output_type',
'date_computed', 'years'
]:
old_val = table.__getattribute__(attr)
new_val = new_table.__getattribute__(attr)
self.assertEqual(old_val, new_val)
self.assertEqual(table.source_data.cache_directory,
new_table.source_data.cache_directory)
self.assertEqual(
table.source_data.dataset_pool_configuration.package_order,
new_table.source_data.dataset_pool_configuration.package_order)
def test_one_indicator(self):
source_data = SourceData(
cache_directory = self.temp_dir,
run_description = 'test',
years = [self.year],
dataset_pool_configuration = DatasetPoolConfiguration(
package_order=['psrc','urbansim','opus_core'],
),
)
indicator_defs = [
Table(
attribute = 'psrc.large_area.average_land_value_for_plan_type_group_residential',
dataset_name = 'large_area',
source_data = source_data,
),
]
IndicatorFactory().create_indicators(
indicators = indicator_defs,
display_error_box = False,
show_results = False)
def test__create_indicators(self):
try:
from opus_core.indicator_framework.image_types.table import Table
except:
pass
else:
indicator_path = os.path.join(self.temp_cache_path, 'indicators')
self.assert_(not os.path.exists(indicator_path))
indicators = [
Table(
source_data = self.source_data,
dataset_name = 'test',
attribute = 'opus_core.test.attribute',
output_type = 'tab'
)
]
factory = IndicatorFactory()
factory.create_indicators(indicators = indicators)
self.assert_(os.path.exists(indicator_path))
self.assert_(os.path.exists(os.path.join(indicator_path, 'test__tab__attribute.tab')))
def test__read_write_metadata(self):
try:
from opus_core.indicator_framework.image_types.table import Table
except:
raise
else:
table = Table(
source_data = self.source_data,
attribute = 'opus_core.test.attribute',
dataset_name = 'test',
output_type = 'tab',
years = [1980,1981] # Indicators are not actually being computed, so the years don't matter here.
)
table.create(False)
self.data_manager._export_indicator_to_file(indicator = table,
source_data = self.source_data,
year = None)
metadata_file = table.get_file_name(extension = 'meta')
metadata_path = os.path.join(table.get_storage_location(),
metadata_file)
self.assertEqual(os.path.exists(metadata_path), True)
expected_path = 'test__tab__attribute.meta'
self.assertEqual(metadata_file,expected_path)
new_table = self.data_manager._import_indicator_from_file(metadata_path)
for attr in ['attributes','dataset_name',
'output_type','date_computed',
'years']:
old_val = table.__getattribute__(attr)
new_val = new_table.__getattribute__(attr)
self.assertEqual(old_val,new_val)
self.assertEqual(table.source_data.cache_directory,
new_table.source_data.cache_directory)
self.assertEqual(table.source_data.dataset_pool_configuration.package_order,
new_table.source_data.dataset_pool_configuration.package_order)
package_order=['sanfrancisco','urbansim','opus_core'],
),
)
multi_year_requests = [
#Chart(
#attribute = 'bus_ = alldata.aggregate_all(business.sector_id == 1)',
#dataset_name = 'alldata',
#source_data = source_data,
#years=arange(2001,2026),
#),
Table(
source_data = source_data,
dataset_name = 'alldata',
name = 'Business Counts 1',
output_type='csv',
attribute = 'bus_1 = alldata.aggregate_all(business.sector_id == 1)',
years = [2001, 2002],
),
Table(
source_data = source_data,
dataset_name = 'alldata',
name = 'Business Counts 2',
output_type='csv',
attribute = 'bus_2 = alldata.aggregate_all(business.sector_id == 2)',
years = [2001, 2002],
),
Table(
source_data = source_data,
scale = [0, 0.2],
dataset_name = 'large_area',
source_data = source_data,
),
Map(
scale = [-5000, 250000],
name = 'urbansim_population_change',
attribute = 'psrc.large_area.population',
source_data = source_data,
operation = 'change',
dataset_name = 'large_area',
),
Table(
source_data = source_data,
dataset_name = 'large_area',
name = 'test_overwrite',
#operation = 'change',
attribute = 'large_area.aggregate(urbansim.zone.number_of_jobs, intermediates=[faz])',
years = [2000, 2005]
),
Table(
source_data = source_data,
dataset_name = 'large_area',
name = 'test_overwrite2',
#operation = 'change',
attribute = 'large_area.aggregate(urbansim.zone.number_of_jobs, intermediates=[faz])',
years = [2000, 2005]
),
Table(
source_data = source_data,
dataset_name = 'zone',
#An example script:
source_data = SourceData(
#cache_directory = r'D:\urbansim_cache\run_1090.2006_11_14_12_12',
cache_directory=
'/Users/hana/urbansim_cache/washtenaw/runs/run_3328.2007_08_01_19_36',
#comparison_cache_directory = r'D:\urbansim_cache\run_1091.2006_11_14_12_12',
years=[2006],
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['urbansim', 'opus_core'], ),
)
indicators = [
Table(source_data=source_data,
dataset_name='gridcell',
attribute='urbansim.gridcell.number_of_households',
output_type='tab'),
Table(
source_data=source_data,
dataset_name='city',
name='average_income',
attribute=
'city.aggregate(urbansim.gridcell.sum_income) / city.aggregate(urbansim.gridcell.number_of_households)',
output_type='tab'),
Table(
source_data=source_data,
dataset_name='city',
name='nonresidential_land_value_higher_than_10000',
attribute=
'city.aggregate(urbansim.gridcell.nonresidential_land_value > 10000)',
output_type='tab'),
)
indicators = [
# Table(
# attribute = 'population=large_area.aggregate(urbansim_parcel.building.population, intermediates=[parcel, zone, faz])',
# dataset_name = 'large_area',
# source_data = source_data,
# ),
# Table(
# attribute = 'employment=large_area.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel, zone, faz])',
# dataset_name = 'large_area',
# source_data = source_data,
# ),
Table(
attribute=
'at_home_workers=district.aggregate(person.work_at_home==1, intermediates=[household, building, parcel, zone])',
dataset_name='district',
source_data=source_data,
),
DatasetTable(
attributes=[
'origin_district_id',
'destination_district_id',
'psrc_parcel.district_commute.commute_trips',
],
dataset_name='district_commute',
source_data=source_data,
name='commutes_by_district',
),
# Map(
# attribute = 'population=zone.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
# dataset_name = 'zone',
#cache_directory = r'D:\urbansim_cache\run_1154.2006_11_17_20_06'
#run_description = '(run 1154 - no ugb + double highway capacity 11/28/2006)'
#cache_directory = r'D:\urbansim_cache\run_1155.2006_11_17_20_07'
#run_description = '(run 1155 - no ugb 11/28/2006)'
source_data = SourceData(
cache_directory=cache_directory,
run_description=run_description,
years=[1980, 1981, 1982],
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['eugene', 'urbansim', 'opus_core'], ),
)
single_year_requests = [
Table(
attribute='urbansim.zone.population',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute='urbansim.zone.number_of_jobs',
dataset_name='zone',
source_data=source_data,
),
Map(
attribute='urbansim.zone.population',
scale=[1, 60000],
dataset_name='zone',
source_data=source_data,
),
Map(
attribute='urbansim.zone.number_of_jobs',
def get_indicators(cache_directory, run_description, years = [2015], base_year=2014):
source_data = SourceData(
cache_directory = cache_directory,
run_description = run_description,
years = years,
base_year = base_year,
dataset_pool_configuration = DatasetPoolConfiguration(
package_order=['psrc_parcel','urbansim_parcel','psrc', 'urbansim','opus_core'],
package_order_exceptions={},
),
)
indicators=[
# ## City indicators
# ==================
Table(
attribute = 'max_dev_residential_capacity=city.aggregate(psrc_parcel.parcel.max_developable_residential_capacity)',
dataset_name = 'city',
source_data = source_data,
),
Table(
attribute = 'max_dev_nonresidential_capacity=city.aggregate(psrc_parcel.parcel.max_developable_nonresidential_capacity)',
dataset_name = 'city',
source_data = source_data,
),
Table(
attribute = 'max_dev_capacity=city.aggregate(psrc_parcel.parcel.max_developable_capacity)',
dataset_name = 'city',
source_data = source_data,
),
#Table(
#attribute = 'max_dev_residential_capacity=city.aggregate(parcel.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.residential_units), function=maximum))',
#dataset_name = 'city',
#source_data = source_data,
#),
#Table(
#attribute = 'max_dev_nonresidential_capacity=city.aggregate(parcel.aggregate(urbansim_parcel.development_project_proposal.building_sqft_non_residential, function=maximum))',
#dataset_name = 'city',
#source_data = source_data,
#),
#Table(
#attribute = 'max_dev_capacity=city.aggregate(parcel.aggregate(urbansim_parcel.development_project_proposal.building_sqft, function=maximum))',
#dataset_name = 'city',
#source_data = source_data,
#),
# ## FAZ indicators
# ==================
Table(
attribute = 'max_dev_residential_capacity=faz.aggregate(psrc_parcel.parcel.max_developable_residential_capacity, intermediates=[zone])',
dataset_name = 'faz',
source_data = source_data,
),
Table(
attribute = 'max_dev_nonresidential_capacity=faz.aggregate(psrc_parcel.parcel.max_developable_nonresidential_capacity, intermediates=[zone])',
dataset_name = 'faz',
source_data = source_data,
),
Table(
attribute = 'max_dev_capacity=faz.aggregate(psrc_parcel.parcel.max_developable_capacity, intermediates=[zone])',
dataset_name = 'faz',
source_data = source_data,
),
#Table(
#attribute = 'max_dev_residential_capacity=faz.aggregate(parcel.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.residential_units), function=maximum), intermediates=[zone])',
#dataset_name = 'faz',
#source_data = source_data,
#),
#Table(
#attribute = 'max_dev_nonresidential_capacity=faz.aggregate(parcel.aggregate(urbansim_parcel.development_project_proposal.building_sqft_non_residential, function=maximum), intermediates=[zone])',
#dataset_name = 'faz',
#source_data = source_data,
#),
#Table(
#attribute = 'max_dev_capacity=faz.aggregate(parcel.aggregate(urbansim_parcel.development_project_proposal.building_sqft, function=maximum), intermediates=[zone])',
#dataset_name = 'faz',
#source_data = source_data,
#),
# ## TAZ indicators
# ==================
#Table(
#attribute = 'max_dev_residential_capacity=zone.aggregate(parcel.aggregate(development_project_proposal.aggregate(urbansim_parcel.development_project_proposal_component.residential_units), function=maximum))',
#dataset_name = 'zone',
#source_data = source_data,
#),
#Table(
#attribute = 'max_dev_nonresidential_capacity=zone.aggregate(parcel.aggregate(urbansim_parcel.development_project_proposal.building_sqft_non_residential, function=maximum))',
#dataset_name = 'zone',
#source_data = source_data,
#),
#Table(
#attribute = 'max_dev_capacity=zone.aggregate(parcel.aggregate(urbansim_parcel.development_project_proposal.building_sqft, function=maximum))',
#dataset_name = 'zone',
#source_data = source_data,
#),
Table(
attribute = 'max_dev_residential_capacity=zone.aggregate(psrc_parcel.parcel.max_developable_residential_capacity)',
dataset_name = 'zone',
source_data = source_data,
),
Table(
attribute = 'max_dev_nonresidential_capacity=zone.aggregate(psrc_parcel.parcel.max_developable_nonresidential_capacity)',
dataset_name = 'zone',
source_data = source_data,
),
Table(
attribute = 'max_dev_capacity=zone.aggregate(psrc_parcel.parcel.max_developable_capacity)',
dataset_name = 'zone',
source_data = source_data,
),
# ## Growth centers indicators
# ============================
Table(
attribute = 'max_dev_residential_capacity=growth_center.aggregate(psrc_parcel.parcel.max_developable_residential_capacity)',
dataset_name = 'growth_center',
source_data = source_data,
),
Table(
attribute = 'max_dev_nonresidential_capacity=growth_center.aggregate(psrc_parcel.parcel.max_developable_nonresidential_capacity)',
dataset_name = 'growth_center',
source_data = source_data,
),
Table(
attribute = 'max_dev_capacity=growth_center.aggregate(psrc_parcel.parcel.max_developable_capacity)',
dataset_name = 'growth_center',
source_data = source_data,
),
]
return indicators
def get_indicators(
cache_directory,
run_description,
years=[2014, 2015, 2017, 2020, 2025, 2030, 2035, 2040, 2045, 2050],
base_year=2014):
#def get_indicators(cache_directory, run_description, years = [2014,2015,2017,2020,2021,2022,2023,2024,2025,2030,2035,2040,2045,2046,2047,2048,2049,2050], base_year=2014):
#def get_indicators(cache_directory, run_description, years = [2050], base_year=2014):
#def get_indicators(cache_directory, run_description, years = [2014,2017,2050], base_year=2014):
source_data = SourceData(
cache_directory=cache_directory,
run_description=run_description,
years=years,
base_year=base_year,
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=[
'psrc_parcel', 'urbansim_parcel', 'psrc', 'urbansim',
'opus_core'
],
package_order_exceptions={},
),
)
indicators = [
DatasetTable(
source_data=source_data,
dataset_name='faz',
name='DU_and_HH_by_bld_type_by_faz_by_year',
attributes=[
'DU_SF_19=faz.aggregate(urbansim_parcel.building.residential_units * (building.building_type_id==19), intermediates=[parcel])',
'DU_MF_12=faz.aggregate(urbansim_parcel.building.residential_units * (building.building_type_id==12), intermediates=[parcel])',
'DU_CO_4=faz.aggregate(urbansim_parcel.building.residential_units * (building.building_type_id==4), intermediates=[parcel])',
'DU_MH_11=faz.aggregate(urbansim_parcel.building.residential_units * (building.building_type_id==11), intermediates=[parcel])',
'DU_Total=faz.aggregate(urbansim_parcel.building.residential_units, intermediates=[parcel])',
'HH_SF_19=faz.aggregate(urbansim_parcel.building.number_of_households * (building.building_type_id==19), intermediates=[parcel])',
'HH_MF_12=faz.aggregate(urbansim_parcel.building.number_of_households * (building.building_type_id==12), intermediates=[parcel])',
'HH_CO_4=faz.aggregate(urbansim_parcel.building.number_of_households * (building.building_type_id==4), intermediates=[parcel])',
'HH_MH_11=faz.aggregate(urbansim_parcel.building.number_of_households * (building.building_type_id==11), intermediates=[parcel])',
'HH_Total=faz.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
],
),
## County Level Control indicators - added 4.17.2018
Table(
attribute=
'population = county.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel])',
dataset_name='county',
source_data=source_data,
),
Table(
attribute=
'households = county.aggregate(urbansim_parcel.parcel.number_of_households, intermediates=[parcel])',
dataset_name='county',
source_data=source_data,
),
Table(
attribute=
'employment = county.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='county',
source_data=source_data,
),
Table(
attribute=
'activity_units = county.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel]) + county.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='county',
source_data=source_data,
),
## County Regional Geography indicators - added 1.23.2018
Table(
attribute=
'population = fips_rgs_proposed.aggregate(urbansim_parcel.parcel.population, intermediates=[city])',
dataset_name='fips_rgs_proposed',
source_data=source_data,
),
Table(
attribute=
'households = fips_rgs_proposed.aggregate(urbansim_parcel.parcel.number_of_households, intermediates=[city])',
dataset_name='fips_rgs_proposed',
source_data=source_data,
),
Table(
attribute=
'employment = fips_rgs_proposed.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[city])',
dataset_name='fips_rgs_proposed',
source_data=source_data,
),
Table(
attribute=
'activity_units = fips_rgs_proposed.aggregate(urbansim_parcel.parcel.population, intermediates=[city]) + fips_rgs_proposed.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[city])',
dataset_name='fips_rgs_proposed',
source_data=source_data,
),
## County MHS vacancy indicators - added 11.1.2017
DatasetTable(
source_data=source_data,
dataset_name='county',
name='eoy_vacancy_by_building_type',
output_type='csv',
attributes=[
'res_4_VR=numpy.safe_array_divide(county.aggregate(urbansim_parcel.building.vacant_residential_units*(building.building_type_id==4)),county.aggregate(urbansim_parcel.building.residential_units*(building.building_type_id==4)))',
'res_12_VR=numpy.safe_array_divide(county.aggregate(urbansim_parcel.building.vacant_residential_units*(building.building_type_id==12)),county.aggregate(urbansim_parcel.building.residential_units*(building.building_type_id==12)))',
'res_19_VR=numpy.safe_array_divide(county.aggregate(urbansim_parcel.building.vacant_residential_units*(building.building_type_id==19)),county.aggregate(urbansim_parcel.building.residential_units*(building.building_type_id==19)))',
'nonres_3_VR=numpy.safe_array_divide(county.aggregate(psrc_parcel.building.vacant_non_home_based_job_space*(psrc_parcel.building.building_type_id==3)),county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==3)))',
'nonres_8_VR=numpy.safe_array_divide(county.aggregate(psrc_parcel.building.vacant_non_home_based_job_space*(psrc_parcel.building.building_type_id==8)),county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==8)))',
'nonres_13_VR=numpy.safe_array_divide(county.aggregate(psrc_parcel.building.vacant_non_home_based_job_space*(psrc_parcel.building.building_type_id==13)),county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==13)))',
'nonres_20_VR=numpy.safe_array_divide(county.aggregate(psrc_parcel.building.vacant_non_home_based_job_space*(psrc_parcel.building.building_type_id==20)),county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==20)))',
'nonres_21_VR=numpy.safe_array_divide(county.aggregate(psrc_parcel.building.vacant_non_home_based_job_space*(psrc_parcel.building.building_type_id==21)),county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==21)))',
],
),
DatasetTable(
source_data=source_data,
dataset_name='county',
name='units_and_nonres_sqft_by_building_type',
output_type='csv',
attributes=[
'res_4_units=county.aggregate(urbansim_parcel.building.residential_units*(building.building_type_id==4))',
'res_12_units=county.aggregate(urbansim_parcel.building.residential_units*(building.building_type_id==12))',
'res_19_units=county.aggregate(urbansim_parcel.building.residential_units*(building.building_type_id==19))',
'nonres_3_spaces=county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==3))',
'nonres_8_spaces=county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==8))',
'nonres_13_spaces=county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==13))',
'nonres_20_spaces=county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==20))',
'nonres_21_spaces=county.aggregate(psrc_parcel.building.total_non_home_based_job_space*(psrc_parcel.building.building_type_id==21))',
'nonres_3_sqft=county.aggregate(psrc_parcel.building.non_residential_sqft*(psrc_parcel.building.building_type_id==3))',
'nonres_8_sqft=county.aggregate(psrc_parcel.building.non_residential_sqft*(psrc_parcel.building.building_type_id==8))',
'nonres_13_sqft=county.aggregate(psrc_parcel.building.non_residential_sqft*(psrc_parcel.building.building_type_id==13))',
'nonres_20_sqft=county.aggregate(psrc_parcel.building.non_residential_sqft*(psrc_parcel.building.building_type_id==20))',
'nonres_21_sqft=county.aggregate(psrc_parcel.building.non_residential_sqft*(psrc_parcel.building.building_type_id==21))',
],
),
## FAZ indicators
## =====================
Table(
attribute=
'households=faz.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'population=faz.aggregate(urbansim_parcel.building.population, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'employment=faz.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'nonres_sqft=faz.aggregate(urbansim_parcel.building.non_residential_sqft, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'residential_units=faz.aggregate(urbansim_parcel.building.residential_units, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'building_sqft=faz.aggregate(urbansim_parcel.parcel.building_sqft, intermediates=[zone])',
dataset_name='faz',
source_data=source_data,
),
## TAZ indicators
Table(
attribute=
'residential_units=zone.aggregate(urbansim_parcel.building.residential_units, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'households=zone.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'population=zone.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'employment=zone.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'nonres_sqft=zone.aggregate(urbansim_parcel.building.non_residential_sqft, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'building_sqft=zone.aggregate(urbansim_parcel.parcel.building_sqft)',
dataset_name='zone',
source_data=source_data,
),
DatasetTable(source_data=source_data,
dataset_name='zone',
name='employment_by_aggr_sector',
attributes=jobs_by_sector("zone", "urbansim_parcel"),
output_type='tab'),
## City indicators
## ==================
Table(
attribute=
'households=city.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'population=city.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'employment=city.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'residential_units=city.aggregate(urbansim_parcel.building.residential_units, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'nonres_sqft=city.aggregate(urbansim_parcel.building.non_residential_sqft, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'building_sqft=city.aggregate(urbansim_parcel.parcel.building_sqft)',
dataset_name='city',
source_data=source_data,
),
Table(
attribute='acres=city.aggregate(parcel.parcel_sqft/43560.)',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'activity_units = city.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel]) + city.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
DatasetTable(source_data=source_data,
dataset_name='city',
name='employment_by_aggr_sector',
attributes=jobs_by_sector("city"),
output_type='tab'),
# # ## Tract-City indicators
# # ==================
# # # Table(
# # # attribute = 'households=tractcity.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
# # # dataset_name = 'tractcity',
# # # source_data = source_data,
# # # ),
# # # Table(
# # # attribute = 'population=tractcity.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
# # # dataset_name = 'tractcity',
# # # source_data = source_data,
# # # ),
# # # Table(
# # # attribute = 'employment=tractcity.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
# # # dataset_name = 'tractcity',
# # # source_data = source_data,
# # # ),
# # # Table(
# # # attribute = 'residential_units=tractcity.aggregate(urbansim_parcel.building.residential_units, intermediates=[parcel])',
# # # dataset_name = 'tractcity',
# # # source_data = source_data,
# # # ),
# # # Table(
# # # attribute = 'nonres_sqft=tractcity.aggregate(urbansim_parcel.building.non_residential_sqft, intermediates=[parcel])',
# # # dataset_name = 'tractcity',
# # # source_data = source_data,
# # # ),
# # # Table(
# # # attribute = 'building_sqft=tractcity.aggregate(urbansim_parcel.parcel.building_sqft)',
# # # dataset_name = 'tractcity',
# # # source_data = source_data,
# # # ),
## Growth Centers Indicators
## ============================
Table(
attribute=
'residential_units=growth_center.aggregate(urbansim_parcel.building.residential_units, intermediates=[parcel])',
dataset_name='growth_center',
source_data=source_data,
),
Table(
attribute=
'households=growth_center.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
dataset_name='growth_center',
source_data=source_data,
),
Table(
attribute=
'population=growth_center.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
dataset_name='growth_center',
source_data=source_data,
),
Table(
attribute=
'employment=growth_center.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
dataset_name='growth_center',
source_data=source_data,
),
Table(
attribute=
'nonres_sqft=growth_center.aggregate(urbansim_parcel.building.non_residential_sqft, intermediates=[parcel])',
dataset_name='growth_center',
source_data=source_data,
),
Table(
attribute=
'building_sqft=growth_center.aggregate(urbansim_parcel.parcel.building_sqft)',
dataset_name='growth_center',
source_data=source_data,
),
Table(
attribute=
'acres=growth_center.aggregate(parcel.parcel_sqft/43560.)',
dataset_name='growth_center',
source_data=source_data,
),
## Large Area Indicators
## ============================
DatasetTable(source_data=source_data,
dataset_name='large_area',
name='employment_by_aggr_sector',
attributes=['large_area.county_id'] +
jobs_by_sector("large_area"),
output_type='tab'),
## Tract indicators
##============================
Table(
attribute=
'households=census_tract.aggregate(urbansim_parcel.parcel.number_of_households)',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'population=census_tract.aggregate(urbansim_parcel.parcel.population)',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'employment=census_tract.aggregate(urbansim_parcel.parcel.number_of_jobs)',
dataset_name='census_tract',
source_data=source_data,
),
# Table(
# attribute = 'nonres_sqft=fcensus_tract.aggregate(urbansim_parcel.parcel.non_residential_sqft, intermediates=[census_block_group])',
# dataset_name = 'census_tract',
# source_data = source_data,
# )
Table(
attribute=
'residential_units=census_tract.aggregate(urbansim_parcel.parcel.residential_units)',
dataset_name='census_tract',
source_data=source_data,
),
#DatasetTable(
# source_data = source_data,
# dataset_name = 'census_tract',
# name = 'employment_by_aggr_sector',
# attributes = jobs_by_sector("census_tract"),
# output_type = 'csv'
# ),
##------Liming's Unplaced Households and Jobs in the Region-----------
Table(
attribute=
'num_unplaced_hhs=alldata.aggregate_all(household.building_id<=0)',
dataset_name='alldata',
source_data=source_data,
),
Table(
attribute=
'num_unplaced_jobs=alldata.aggregate_all(job.building_id<=0)',
dataset_name='alldata',
source_data=source_data,
),
## Regional Total Tables
Table(
attribute=
'residential_units=alldata.aggregate_all(urbansim_parcel.building.residential_units)',
dataset_name='alldata',
source_data=source_data,
),
Table(
attribute=
'non_residential_sqft=alldata.aggregate_all(urbansim_parcel.building.non_residential_sqft)',
dataset_name='alldata',
source_data=source_data,
),
Table(
attribute=
'households=alldata.aggregate_all(urbansim_parcel.building.number_of_households)',
dataset_name='alldata',
source_data=source_data,
),
Table(
attribute=
'employment=alldata.aggregate_all(urbansim_parcel.building.number_of_jobs)',
dataset_name='alldata',
source_data=source_data,
),
Table(
attribute=
'population=alldata.aggregate_all(urbansim_parcel.building.population)',
dataset_name='alldata',
source_data=source_data,
),
## Demographic indicators
## ======================
# DatasetTable(
# source_data = source_data,
# dataset_name = 'alldata',
# name = 'pptyp',
# output_type = 'csv',
# attributes = [
# 'full_time_worker = alldata.aggregate_all(person.employment_status==1)',
# 'part_time_worker = alldata.aggregate_all(numpy.logical_and(person.employment_status==2,person.student==0))',
# 'non_working_adult_age_65_plus = alldata.aggregate_all(numpy.logical_and(person.employment_status<1,numpy.logical_and(person.student==0,person.age>64)))',
# 'non_working_adult_age_16_64 = alldata.aggregate_all(numpy.logical_and(person.employment_status<1,numpy.logical_and(person.student==0,numpy.logical_and(person.age<65,person.age>15))))',
# 'university_student = alldata.aggregate_all(numpy.logical_and(person.employment_status<>1,numpy.logical_and(person.student==1,person.age>18)))',
# 'hs_student_age_15_up = alldata.aggregate_all(numpy.logical_and(person.employment_status<>1,numpy.logical_and(person.student==1,numpy.logical_and(person.age>15,person.age<19))))',
# 'child_age_5_15 = alldata.aggregate_all(numpy.logical_and(person.age>4,person.age<16))',
# 'child_age_0_4 = alldata.aggregate_all(person.age<5)',
# ## 'age_6_to_10 = alldata.aggregate_all(numpy.logical_and(person.age<11,person.age>=6))',
# ## 'age_11_to_15 = alldata.aggregate_all(numpy.logical_and(person.age<16,person.age>=11))',
# ## 'age_16_to_20 = alldata.aggregate_all(numpy.logical_and(person.age<21,person.age>=16))',
# ## 'age_21_to_25 = alldata.aggregate_all(numpy.logical_and(person.age<26,person.age>=21))',
# ## 'income = households.income',
# ],
# ),
# DatasetTable(
# source_data = source_data,
# dataset_name = 'alldata',
# name = 'persons_by_age_groups_of_interest',
# output_type = 'csv',
# attributes = [
# 'Under5 = alldata.aggregate_all(person.age<5)',
# 'Five_18 = alldata.aggregate_all(numpy.logical_and(person.age<19,person.age>=5))',
# 'Nineteen_24 = alldata.aggregate_all(numpy.logical_and(person.age<25,person.age>=19))',
# 'Twentyfive_60 = alldata.aggregate_all(numpy.logical_and(person.age<61,person.age>=25))',
# 'Over_60 = alldata.aggregate_all(person.age>=61)',
# ],
# ),
# DatasetTable(
# source_data = source_data,
# dataset_name = 'alldata',
# name = 'persons_by_5year_age_groups',
# output_type = 'csv',
# attributes = [
# 'age_0_to_5 = alldata.aggregate_all(person.age<6)',
# 'age_6_to_10 = alldata.aggregate_all(numpy.logical_and(person.age<11,person.age>=6))',
# 'age_11_to_15 = alldata.aggregate_all(numpy.logical_and(person.age<16,person.age>=11))',
# 'age_16_to_20 = alldata.aggregate_all(numpy.logical_and(person.age<21,person.age>=16))',
# 'age_21_to_25 = alldata.aggregate_all(numpy.logical_and(person.age<26,person.age>=21))',
# 'age_26_to_30 = alldata.aggregate_all(numpy.logical_and(person.age<31,person.age>=26))',
# 'age_31_to_35 = alldata.aggregate_all(numpy.logical_and(person.age<36,person.age>=31))',
# 'age_36_to_40 = alldata.aggregate_all(numpy.logical_and(person.age<41,person.age>=36))',
# 'age_41_to_45 = alldata.aggregate_all(numpy.logical_and(person.age<46,person.age>=41))',
# 'age_46_to_50 = alldata.aggregate_all(numpy.logical_and(person.age<51,person.age>=46))',
# 'age_51_to_55 = alldata.aggregate_all(numpy.logical_and(person.age<56,person.age>=51))',
# 'age_56_to_60 = alldata.aggregate_all(numpy.logical_and(person.age<61,person.age>=56))',
# 'age_61_to_65 = alldata.aggregate_all(numpy.logical_and(person.age<66,person.age>=61))',
# 'age_66_to_70 = alldata.aggregate_all(numpy.logical_and(person.age<71,person.age>=66))',
# 'age_71_to_75 = alldata.aggregate_all(numpy.logical_and(person.age<76,person.age>=71))',
# 'age_76_to_80 = alldata.aggregate_all(numpy.logical_and(person.age<81,person.age>=76))',
# 'age_81_to_85 = alldata.aggregate_all(numpy.logical_and(person.age<86,person.age>=81))',
# 'age_86_to_90 = alldata.aggregate_all(numpy.logical_and(person.age<91,person.age>=86))',
# 'age_91_to_95 = alldata.aggregate_all(numpy.logical_and(person.age<96,person.age>=91))',
# 'age_96_and_up = alldata.aggregate_all(person.age>=96)',
# ],
# ),
# DatasetTable(
# source_data = source_data,
# dataset_name = 'alldata',
# name = 'regional_total_hhs_by_new_14incomegroups',
# output_type = 'csv',
# #output_type = 'sql',
# #storage_location = database,
# attributes = [
# 'Group1_Under50K = alldata.aggregate_all(household.income<50000)',
# 'Group2_50_75K = alldata.aggregate_all(numpy.logical_and(household.income<75001,household.income>=50000))',
# 'Group3_75_100K = alldata.aggregate_all(numpy.logical_and(household.income<100001,household.income>=75000))',
# 'Group4_Over100K = alldata.aggregate_all(household.income>=100001)',
# ],
# ),
# DatasetTable(
# source_data = source_data,
# dataset_name = 'alldata',
# name = 'regional_total_hhs_by_30_60_90_in_14dollars_groups',
# output_type = 'csv',
# #output_type = 'sql',
# #storage_location = database,
# attributes = [
# 'Group1_Under36870K = alldata.aggregate_all(household.income<36870)',
# 'Group2_UpTo73700 = alldata.aggregate_all(numpy.logical_and(household.income<73700,household.income>=36870))',
# 'Group3_UpTo110600 = alldata.aggregate_all(numpy.logical_and(household.income<110600,household.income>=73700))',
# 'Group4_Over110600 = alldata.aggregate_all(household.income>=110600)',
# ],
# ),
# DatasetTable(
# source_data = source_data,
# dataset_name = 'alldata',
# name = 'pwtyp',
# output_type = 'csv',
# attributes = [
# 'full_time = alldata.aggregate_all((person.employment_status==1)*(urbansim_parcel.person.job_id > 0))',
# 'part_time = alldata.aggregate_all((person.employment_status==2)*(urbansim_parcel.person.job_id > 0))',
# 'workers_no_job = alldata.aggregate_all((person.employment_status >0)*(urbansim_parcel.person.job_id < 0))',
# 'non_workers_no_job = alldata.aggregate_all((person.employment_status <1)*(urbansim_parcel.person.job_id < 0))',
# ],
# ),
]
return indicators
def make_multiyear_workbook(cache_directory, yearstart=2010, yearend=2035):
""" This spits out the indicators for a multiyear workbook and then combines them
into a single file, cache_directory/alldata.csv
You can then copy this a copy of the MultiyearLU_template.xls and see some basic
performance/troubleshooting graphs for the various submodels.
"""
multiyear_workbook_source_data = SourceData(
cache_directory=cache_directory,
run_description="Run description is used for what?",
years=range(yearstart, yearend + 1),
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['sanfrancisco', 'urbansim', 'opus_core'], ),
)
multiyear_workbook_alldata_attributes = \
[
# commercial
'bldg_count_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,1,0))',
'bldg_occsqft_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_comm=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_comm=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==1,sanfrancisco.building.residential_units,0))',
# institutional
'bldg_count_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,1,0))',
'bldg_occsqft_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_inst=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_inst=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==2,sanfrancisco.building.residential_units,0))',
# office
'bldg_count_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,1,0))',
'bldg_occsqft_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_offc=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_offc=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==3,sanfrancisco.building.residential_units,0))',
# residential
'bldg_count_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,1,0))',
'bldg_occunit_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.number_of_households,0))',
'bldg_count_totunit_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.residential_units,0))',
'bldg_vacrate_res=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.residential_units,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.number_of_households,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.residential_units,0))',
'bldg_count_totsqft_res=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==4,sanfrancisco.building.non_residential_sqft,0))',
# res in non-res group buildings
'bldg_count_reso=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,1,0))',
'bldg_occunit_reso=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.number_of_households,0))',
'bldg_count_totunit_reso=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.residential_units,0))',
'bldg_vacrate_reso=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.residential_units,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.number_of_households,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.residential_units,0))',
'bldg_count_totsqft_ores=alldata.aggregate_all(where(sanfrancisco.building.building_group_id!=4,sanfrancisco.building.non_residential_sqft,0))',
# visitor
'bldg_count_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,1,0))',
'bldg_occsqft_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_vis=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_vis=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==5,sanfrancisco.building.residential_units,0))',
# mixed
'bldg_count_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,1,0))',
'bldg_occsqft_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.occupied_sqft,0))',
'bldg_count_totsqft_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.non_residential_sqft,0))',
'bldg_vacrate_mix=(alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.non_residential_sqft,0))-' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.occupied_sqft,0)))/' +
'alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.non_residential_sqft,0))',
'bldg_count_totunit_mix=alldata.aggregate_all(where(sanfrancisco.building.building_group_id==6,sanfrancisco.building.residential_units,0))',
# unplaced buildings
'bldg_count_unplaced=alldata.aggregate_all(where(sanfrancisco.building.parcel_id<0,1,0))',
'bldg_count_unplaced_BLCMspec=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.parcel_id<0,sanfrancisco.building.is_placed_type>0),1,0))',
'bldg_count_unplaced_noBLCMspec=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.parcel_id<0,sanfrancisco.building.is_placed_type==0),1,0))',
# household counts
'hhld_count_sz1=alldata.aggregate_all(where(household.household_size==1,1,0))',
'hhld_count_sz2=alldata.aggregate_all(where(household.household_size==2,1,0))',
'hhld_count_sz34=alldata.aggregate_all(where(numpy.logical_or(household.household_size==3,household.household_size==4),1,0))',
'hhld_count_sz56=alldata.aggregate_all(where(numpy.logical_or(household.household_size==5,household.household_size==6),1,0))',
'hhld_count_sz7=alldata.aggregate_all(where(household.household_size>=7,1,0))',
# business counts
'jobs_count_sect1=alldata.aggregate_all(where(business.sector_id==1,business.employment,0))',
'jobs_count_sect2=alldata.aggregate_all(where(business.sector_id==2,business.employment,0))',
'jobs_count_sect3=alldata.aggregate_all(where(business.sector_id==3,business.employment,0))',
'jobs_count_sect4=alldata.aggregate_all(where(business.sector_id==4,business.employment,0))',
'jobs_count_sect5=alldata.aggregate_all(where(business.sector_id==5,business.employment,0))',
'jobs_count_sect6=alldata.aggregate_all(where(business.sector_id==6,business.employment,0))',
'jobs_count_sect7=alldata.aggregate_all(where(business.sector_id==7,business.employment,0))',
'jobs_count_sect8=alldata.aggregate_all(where(business.sector_id==8,business.employment,0))',
'jobs_count_sect9=alldata.aggregate_all(where(business.sector_id==9,business.employment,0))',
'jobs_count_sect10=alldata.aggregate_all(where(business.sector_id==10,business.employment,0))',
'jobs_count_sect11=alldata.aggregate_all(where(business.sector_id==11,business.employment,0))',
# unplaced businesses, how full are buildings? overall nonres sqft totals
'business_count_unplaced=alldata.aggregate_all(where(business.building_id<1,1,0))',
'bldg_count_overfullbiz=alldata.aggregate_all(where(sanfrancisco.building.occupied_sqft>building.non_residential_sqft,1,0))',
'bldg_count_partialfullbiz=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.occupied_sqft<building.non_residential_sqft,'+
'sanfrancisco.building.occupied_sqft>0),1,0))',
'bldg_count_vacantbiz=alldata.aggregate_all(where(numpy.logical_and(building.non_residential_sqft>0,'+
'sanfrancisco.building.occupied_sqft==0),1,0))',
# these are covered above for building_groups
'bldg_nonres_sqft_total=alldata.aggregate_all(building.non_residential_sqft)',
'bldg_nonres_sqft_occ=alldata.aggregate_all(sanfrancisco.building.occupied_sqft)',
'bldg_nonres_sqft_vacant=alldata.aggregate_all(building.non_residential_sqft)-alldata.aggregate_all(sanfrancisco.building.occupied_sqft)',
# unplaced households, how full are buildings? overall hhunit totals
'hhld_count_unplaced=alldata.aggregate_all(where(household.building_id<1,1,0))',
'hhld_count_overfullhh=alldata.aggregate_all(where(sanfrancisco.building.number_of_households>building.residential_units,1,0))',
'hhld_count_partialfullhh=alldata.aggregate_all(where(numpy.logical_and(sanfrancisco.building.number_of_households<building.residential_units,'+
'sanfrancisco.building.number_of_households>0),1,0))',
'hhld_count_vacanthh=alldata.aggregate_all(where(numpy.logical_and(building.residential_units>0,'+
'sanfrancisco.building.number_of_households==0),1,0))',
# these are covered above for building_groups
'hhld_res_unit_total=alldata.aggregate_all(building.residential_units)',
'hhld_res_unit_occ=alldata.aggregate_all(sanfrancisco.building.number_of_households)',
'hhld_res_unit_vacant=alldata.aggregate_all(building.residential_units)-alldata.aggregate_all(sanfrancisco.building.number_of_households)',
]
class SFIndicatorDialect(csv.excel):
lineterminator = '\n'
csv.register_dialect("SFIndicatorDialect", SFIndicatorDialect)
onetableWriter = csv.writer(open(
os.path.join(cache_directory, "alldata.csv"), 'w'),
dialect='SFIndicatorDialect')
headeryears = []
for year in multiyear_workbook_source_data.years:
headeryears.append("y" + str(year))
onetableWriter.writerow(['variable'] + headeryears)
for attr in multiyear_workbook_alldata_attributes:
attr_name = attr.partition('=')[0]
request = [
Table(source_data=multiyear_workbook_source_data,
dataset_name='alldata',
name=attr_name,
attribute=attr)
]
IndicatorFactory().create_indicators(indicators=request,
display_error_box=False,
show_results=False)
# open this file
filename = request[0].get_file_path()
indicatorReader = csv.reader(open(filename, 'r'))
# title row
fields = indicatorReader.next()
assert (len(fields) == len(multiyear_workbook_source_data.years) + 1
) # quick check
# data row
fields = indicatorReader.next()
fields[0] = attr_name
onetableWriter.writerow(fields)
# for *_count_* rows, add *_new_* row
if attr_name.find("_count_") >= 0:
attr_new_name = attr_name.replace("_count_", "_new_")
new_fields = [attr_new_name, 0]
for ind in range(1, len(multiyear_workbook_source_data.years)):
new_fields.append(float(fields[ind + 1]) - float(fields[ind]))
onetableWriter.writerow(new_fields)
def get_indicators(cache_directory,
run_description,
years=[2014, 2017, 2050],
base_year=2014):
source_data = SourceData(
cache_directory=cache_directory,
run_description=run_description,
years=years,
base_year=base_year,
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=[
'psrc_parcel', 'urbansim_parcel', 'psrc', 'urbansim',
'opus_core'
],
package_order_exceptions={},
),
)
indicators = [
# # Minority - population, households, employment, and activity units
Table(
attribute=
'population = minority.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel])',
dataset_name='minority',
source_data=source_data,
),
Table(
attribute=
'households = minority.aggregate(urbansim_parcel.parcel.number_of_households, intermediates=[parcel])',
dataset_name='minority',
source_data=source_data,
),
Table(
attribute=
'employment = minority.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='minority',
source_data=source_data,
),
Table(
attribute=
'activity_units = minority.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel]) + minority.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='minority',
source_data=source_data,
),
# ## Hex Level Indicators (minority_id = 2) -- for mapping
Table(
attribute=
'minority_population = hex.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 2))',
dataset_name='hex',
source_data=source_data,
),
Table(
attribute=
'minority_households = hex.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 2))',
dataset_name='hex',
source_data=source_data,
),
Table(
attribute=
'minority_employment = hex.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 2))',
dataset_name='hex',
source_data=source_data,
),
Table(
attribute=
'minority_activity_units = hex.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel]) + hex.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 2))',
dataset_name='hex',
source_data=source_data,
),
# # # Minority Park/OS Buffer Indicators - #64
Table(
attribute=
'population_park_buffer = minority.aggregate(urbansim_parcel.parcel.population * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
dataset_name='minority',
source_data=source_data,
),
# Table(
# attribute = 'households_park_buffer = minority.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
# dataset_name = 'minority',
# source_data = source_data,
# ),
Table(
attribute=
'employment_park_buffer = minority.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
dataset_name='minority',
source_data=source_data,
),
Table(
attribute=
'activity_units_park_buffer = minority.aggregate((urbansim_parcel.parcel.population + urbansim_parcel.parcel.number_of_jobs) * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
dataset_name='minority',
source_data=source_data,
),
# # # Minority Growth Amenities Buffer Indicators - #31
Table(
attribute=
'population_growth_amenities = minority.aggregate(urbansim_parcel.parcel.population * (parcel.growth_amenities_id == 1))',
dataset_name='minority',
source_data=source_data,
),
# Table(
# attribute = 'households_growth_amenities = minority.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.growth_amenities_id == 1))',
# dataset_name = 'minority',
# source_data = source_data,
# ),
Table(
attribute=
'employment_growth_amenities = minority.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.growth_amenities_id == 1))',
dataset_name='minority',
source_data=source_data,
),
Table(
attribute=
'activity_units_growth_amenities = minority.aggregate((urbansim_parcel.parcel.population + urbansim_parcel.parcel.number_of_jobs) * (parcel.growth_amenities_id == 1))',
dataset_name='minority',
source_data=source_data,
),
# # # Minority Transit Buffer level by Indicators - #28a
Table(
attribute=
'population_transit_buffer = minority.aggregate(urbansim_parcel.parcel.population * (parcel.transit_buffer_id == 1))',
dataset_name='minority',
source_data=source_data,
),
# Table(
# attribute = 'households_transit_buffer = minority.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.transit_buffer_id == 1))',
# dataset_name = 'minority',
# source_data = source_data,
# ),
Table(
attribute=
'employment_transit_buffer = minority.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.transit_buffer_id == 1))',
dataset_name='minority',
source_data=source_data,
),
Table(
attribute=
'activity_units_transit_buffer = minority.aggregate((urbansim_parcel.parcel.population + urbansim_parcel.parcel.number_of_jobs) * (parcel.transit_buffer_id == 1))',
dataset_name='minority',
source_data=source_data,
),
# # # Minority Census Tract Level Indicators - #79
# # Non-minority = 1
Table(
attribute=
'non_minority_population = census_tract.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 1))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_minority_households = census_tract.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 1))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_minority_employment = census_tract.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 1))',
dataset_name='census_tract',
source_data=source_data,
),
# # # Minority = 2
Table(
attribute=
'non_minority_population = census_tract.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 2))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_minority_households = census_tract.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 2))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_minority_employment = census_tract.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 2))',
dataset_name='census_tract',
source_data=source_data,
),
# # # Minority Subarea Level Indicators - #18
# # Non-minority = 1
Table(
attribute=
'non_minority_population = subarea.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 1))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'non_minority_households = subarea.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 1))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'non_minority_employment = subarea.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 1))',
dataset_name='subarea',
source_data=source_data,
),
# # Minority = 2
Table(
attribute=
'minority_population = subarea.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 2))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'minority_households = subarea.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 2))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'minority_employment = subarea.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 2))',
dataset_name='subarea',
source_data=source_data,
),
# # # Minority TOD Level Indicators - #30
# # Non-Minority = 1
Table(
attribute=
'non_minority_population = tod.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 1))',
dataset_name='tod',
source_data=source_data,
),
# Table(
# attribute = 'non_minority_households = tod.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 1))',
# dataset_name = 'tod',
# source_data = source_data,
# ),
Table(
attribute=
'non_minority_employment = tod.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 1))',
dataset_name='tod',
source_data=source_data,
),
# # Minority = 2
Table(
attribute=
'minority_population = tod.aggregate(urbansim_parcel.parcel.population * (parcel.minority_id == 2))',
dataset_name='tod',
source_data=source_data,
),
Table(
attribute=
'minority_households = tod.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.minority_id == 2))',
dataset_name='tod',
source_data=source_data,
),
Table(
attribute=
'minority_employment = tod.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.minority_id == 2))',
dataset_name='tod',
source_data=source_data,
),
Table(
attribute=
'developed_acres = minority.aggregate(numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft,intermediates=[parcel])/43560.0',
dataset_name='minority',
source_data=source_data,
),
DatasetTable(
source_data=source_data,
dataset_name='minority',
name='Acreage_by_built_res_density',
attributes=[
'Nonres_existing = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units == 0) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Nonres_redev = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units == 0) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Nonres_newdev = minority.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units == 0) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Low_existing = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 3630) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Low_redev = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 3630) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Low_newdev = minority.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 3630) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Medium_existing = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 3630) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Medium_redev = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 3630) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Medium_newdev = minority.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 3630) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'High_existing = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'High_redev = minority.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'High_newdev = minority.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
],
),
# # Poverty - population, households, employment, and activity units
Table(
attribute=
'population = poverty.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel])',
dataset_name='poverty',
source_data=source_data,
),
Table(
attribute=
'households = poverty.aggregate(urbansim_parcel.parcel.number_of_households, intermediates=[parcel])',
dataset_name='poverty',
source_data=source_data,
),
Table(
attribute=
'employment = poverty.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='poverty',
source_data=source_data,
),
Table(
attribute=
'activity_units = poverty.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel]) + poverty.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[parcel])',
dataset_name='poverty',
source_data=source_data,
),
## Hex Level Indicators (poverty = 2) -- for mapping
Table(
attribute=
'poverty_population = hex.aggregate(urbansim_parcel.parcel.population, * (parcel.poverty_id == 2))',
dataset_name='hex',
source_data=source_data,
),
Table(
attribute=
'poverty_households = hex.aggregate(urbansim_parcel.parcel.number_of_households, * (parcel.poverty_id == 2))',
dataset_name='hex',
source_data=source_data,
),
Table(
attribute=
'poverty_employment = hex.aggregate(urbansim_parcel.parcel.number_of_jobs, * (parcel.poverty_id == 2))',
dataset_name='hex',
source_data=source_data,
),
Table(
attribute=
'poverty_activity_units = hex.aggregate(urbansim_parcel.parcel.population, intermediates=[parcel]) + hex.aggregate(urbansim_parcel.parcel.number_of_jobs, * (parcel.poverty_id == 2))',
dataset_name='hex',
source_data=source_data,
),
# # # Poverty Park/OS Buffer Indicators - #64
Table(
attribute=
'population_park_buffer = poverty.aggregate(urbansim_parcel.parcel.population * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
dataset_name='poverty',
source_data=source_data,
),
# Table(
# attribute = 'households_park_buffer = poverty.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
# dataset_name = 'poverty',
# source_data = source_data,
# ),
Table(
attribute=
'employment_park_buffer = poverty.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
dataset_name='poverty',
source_data=source_data,
),
Table(
attribute=
'activity_units_park_buffer = poverty.aggregate((urbansim_parcel.parcel.population + urbansim_parcel.parcel.number_of_jobs) * (parcel.park_buffer_id == 1) * (parcel.is_inside_urban_growth_boundary == 1))',
dataset_name='poverty',
source_data=source_data,
),
# # # Poverty Growth Amenities Buffer Indicators - #31
Table(
attribute=
'population_growth_amenities = poverty.aggregate(urbansim_parcel.parcel.population * (parcel.growth_amenities_id == 1))',
dataset_name='poverty',
source_data=source_data,
),
# Table(
# attribute = 'households_growth_amenities = poverty.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.growth_amenities_id == 1))',
# dataset_name = 'poverty',
# source_data = source_data,
# ),
Table(
attribute=
'employment_growth_amenities = poverty.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.growth_amenities_id == 1))',
dataset_name='poverty',
source_data=source_data,
),
Table(
attribute=
'activity_units_growth_amenities = poverty.aggregate((urbansim_parcel.parcel.population + urbansim_parcel.parcel.number_of_jobs) * (parcel.growth_amenities_id == 1))',
dataset_name='poverty',
source_data=source_data,
),
# # # Poverty Transit Buffer level by Indicators - #28a
Table(
attribute=
'population_transit_buffer = poverty.aggregate(urbansim_parcel.parcel.population * (parcel.transit_buffer_id == 1))',
dataset_name='poverty',
source_data=source_data,
),
# Table(
# attribute = 'households_transit_buffer = poverty.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.transit_buffer_id == 1))',
# dataset_name = 'poverty',
# source_data = source_data,
# ),
Table(
attribute=
'employment_transit_buffer = poverty.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.transit_buffer_id == 1))',
dataset_name='poverty',
source_data=source_data,
),
Table(
attribute=
'activity_units_transit_buffer = poverty.aggregate((urbansim_parcel.parcel.population + urbansim_parcel.parcel.number_of_jobs) * (parcel.transit_buffer_id == 1))',
dataset_name='poverty',
source_data=source_data,
),
# # # Poverty Census Tract Level Indicators - #79
# # Non-poverty = 1
Table(
attribute=
'non_poverty_population = census_tract.aggregate(urbansim_parcel.parcel.population * (parcel.poverty_id == 1))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_poverty_households = census_tract.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.poverty_id == 1))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_poverty_employment = census_tract.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.poverty_id == 1))',
dataset_name='census_tract',
source_data=source_data,
),
# # # Poverty = 2
Table(
attribute=
'non_poverty_population = census_tract.aggregate(urbansim_parcel.parcel.population * (parcel.poverty_id == 2))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_poverty_households = census_tract.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.poverty_id == 2))',
dataset_name='census_tract',
source_data=source_data,
),
Table(
attribute=
'non_poverty_employment = census_tract.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.poverty_id == 2))',
dataset_name='census_tract',
source_data=source_data,
),
# # # Poverty Subarea Level Indicators - #18
# # Non-poverty = 1
Table(
attribute=
'non_poverty_population = subarea.aggregate(urbansim_parcel.parcel.population * (parcel.poverty_id == 1))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'non_poverty_households = subarea.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.poverty_id == 1))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'non_poverty_employment = subarea.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.poverty_id == 1))',
dataset_name='subarea',
source_data=source_data,
),
# # Poverty = 2
Table(
attribute=
'poverty_population = subarea.aggregate(urbansim_parcel.parcel.population * (parcel.poverty_id == 2))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'poverty_households = subarea.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.poverty_id == 2))',
dataset_name='subarea',
source_data=source_data,
),
Table(
attribute=
'poverty_employment = subarea.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.poverty_id == 2))',
dataset_name='subarea',
source_data=source_data,
),
# # # Poverty TOD Level Indicators - #30
# # Non-Poverty = 1
Table(
attribute=
'non_poverty_population = tod.aggregate(urbansim_parcel.parcel.population * (parcel.poverty_id == 1))',
dataset_name='tod',
source_data=source_data,
),
# Table(
# attribute = 'non_poverty_households = tod.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.poverty_id == 1))',
# dataset_name = 'tod',
# source_data = source_data,
# ),
Table(
attribute=
'non_poverty_employment = tod.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.poverty_id == 1))',
dataset_name='tod',
source_data=source_data,
),
# # Poverty = 2
Table(
attribute=
'poverty_population = tod.aggregate(urbansim_parcel.parcel.population * (parcel.poverty_id == 2))',
dataset_name='tod',
source_data=source_data,
),
# Table(
# attribute = 'poverty_households = tod.aggregate(urbansim_parcel.parcel.number_of_households * (parcel.poverty_id == 2))',
# dataset_name = 'tod',
# source_data = source_data,
# ),
Table(
attribute=
'poverty_employment = tod.aggregate(urbansim_parcel.parcel.number_of_jobs * (parcel.poverty_id == 2))',
dataset_name='tod',
source_data=source_data,
),
Table(
attribute=
'developed_acres = poverty.aggregate(numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft,intermediates=[parcel])/43560.0',
dataset_name='poverty',
source_data=source_data,
),
DatasetTable(
source_data=source_data,
dataset_name='poverty',
name='Acreage_by_built_res_density',
attributes=[
'Nonres_existing = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units == 0) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Nonres_redev = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units == 0) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Nonres_newdev = poverty.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units == 0) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Low_existing = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 3630) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Low_redev = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 3630) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Low_newdev = poverty.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 3630) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Medium_existing = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 3630) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Medium_redev = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 3630) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'Medium_newdev = poverty.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 3630) * (urbansim_parcel.parcel.parcel_sqft_per_unit > 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'High_existing = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) < 2015) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'High_redev = poverty.aggregate((parcel.baseyear_built > 0) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
'High_newdev = poverty.aggregate((parcel.baseyear_built < 1850) * (parcel.aggregate(building.year_built, function=maximum) > 2014) * (psrc_parcel.parcel.residential_units > 0) * (urbansim_parcel.parcel.parcel_sqft_per_unit <= 871) * numpy.logical_or((psrc_parcel.parcel.residential_units > 0),(parcel.aggregate(psrc_parcel.building.job_capacity_computed_if_necessary) > 0)) * parcel.parcel_sqft, intermediates=[parcel]) / 43560.0',
],
),
]
return indicators
def get_indicators(cache_directory,
run_description,
years=range(2014, 2051),
base_year=2014):
source_data = SourceData(
cache_directory=cache_directory,
run_description=run_description,
years=years,
base_year=base_year,
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=[
'psrc_parcel', 'urbansim_parcel', 'psrc', 'urbansim',
'opus_core'
],
package_order_exceptions={},
),
)
indicators = [
# FAZ indicators
# =====================
Table(
attribute=
'householdsAn=faz.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'populationAn=faz.aggregate(urbansim_parcel.building.population, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
Table(
attribute=
'employmentAn=faz.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel,zone])',
dataset_name='faz',
source_data=source_data,
),
# TAZ indicators
Table(
attribute=
'householdsAn=zone.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'populationAn=zone.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
Table(
attribute=
'employmentAn=zone.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
dataset_name='zone',
source_data=source_data,
),
# ## City indicators
# ==================
Table(
attribute=
'householdsAn=city.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'populationAn=city.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
Table(
attribute=
'employmentAn=city.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
dataset_name='city',
source_data=source_data,
),
# ## Tract-City indicators
# ==================
Table(
attribute=
'householdsAn=tractcity.aggregate(urbansim_parcel.building.number_of_households, intermediates=[parcel])',
dataset_name='tractcity',
source_data=source_data,
),
Table(
attribute=
'populationAn=tractcity.aggregate(urbansim_parcel.building.population, intermediates=[parcel])',
dataset_name='tractcity',
source_data=source_data,
),
Table(
attribute=
'employmentAn=tractcity.aggregate(urbansim_parcel.building.number_of_jobs, intermediates=[parcel])',
dataset_name='tractcity',
source_data=source_data,
),
# # County Regional Geography indicators
Table(
attribute=
'populationAn = fips_rgs.aggregate(urbansim_parcel.parcel.population, intermediates=[city])',
dataset_name='fips_rgs',
source_data=source_data,
),
Table(
attribute=
'householdsAn = fips_rgs.aggregate(urbansim_parcel.parcel.number_of_households, intermediates=[city])',
dataset_name='fips_rgs',
source_data=source_data,
),
Table(
attribute=
'employmentAn = fips_rgs.aggregate(urbansim_parcel.parcel.number_of_jobs, intermediates=[city])',
dataset_name='fips_rgs',
source_data=source_data,
),
]
return indicators
