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economic_indicator.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 31 17:48:36 2020
@author: doorleyr
"""
import pandas as pd
import json
from toolbox import Handler, Indicator
from indicator_tools import EconomicIndicatorBase
# def load_output_per_employee():
# industry_ouput=pd.read_csv('./tables/innovation_data/USA_industry_ouput.csv', skiprows=1)
# industry_ouput=industry_ouput.set_index('2017 NAICS code')
# output_per_employee_by_naics={}
# for ind_row, row in industry_ouput.iterrows():
# output_per_emp=row['Sales, value of shipments, or revenue ($1,000)']/row['Number of employees']
# if '-' in ind_row:
# from_code, to_code=ind_row.split('-')
# if '(' in to_code:
# to_code=to_code.split('(')[0]
# for code in range(int(from_code), int(to_code)+1):
# output_per_employee_by_naics[str(code)]=output_per_emp
# else:
# output_per_employee_by_naics[ind_row]=output_per_emp
# # if '(' in ind_row:
# # ind_row=ind_row.split('(')[0]
# # output_per_employee_by_naics[ind_row]=output_per_emp
# return output_per_employee_by_naics
# def get_baseline_employees_by_naics(table_name, table_geoids):
# employees_by_naics={}
# wac=pd.read_csv('./tables/{}/mi_wac_S000_JT00_2017.csv.gz'.format(table_name))
# wac['block_group']=wac.apply(lambda row: str(row['w_geocode'])[:12], axis=1)
# wac=wac.loc[wac['block_group'].isin(table_geoids)]
# wac_data_full_table=wac.sum(axis=0)
# for col in wac:
# if 'CNS' in col:
# naics=wac_cns_to_naics[col]
# if '-' in naics:
# naics=naics.split('-')[0]
# employees_by_naics[naics]=wac_data_full_table[col]
# return employees_by_naics
# wac_cns_to_naics={
# 'CNS01' : '11',
# 'CNS02' : '21',
# 'CNS03' : '22',
# 'CNS04' : '23',
# 'CNS05' : '31-33',
# 'CNS06' : '42',
# 'CNS07' : '44-45',
# 'CNS08' : '48-49',
# 'CNS09' : '51',
# 'CNS10' : '52',
# 'CNS11' : '53',
# 'CNS12' : '54',
# 'CNS13' : '55',
# 'CNS14' : '56' ,
# 'CNS15' : '61',
# 'CNS16' : '62',
# 'CNS17' : '71',
# 'CNS18' : '72',
# 'CNS19' : '81',
# 'CNS20' : '92' }
class EconomicIndicator(EconomicIndicatorBase):
def setup(self,*args,**kwargs):
self.table_name= kwargs['table_name']
# self.grid_to_industries=kwargs['grid_to_industries']
# self.industries_to_occupations=kwargs['industries_to_occupations']
self.name=kwargs['name']
# sim_zones=json.load(open('./tables/{}/sim_zones.json'.format(self.table_name)))
# table_geoids=[z.split('US')[1] for z in sim_zones]
# # get the baseline num workers in district by industry NAICS code
# self.base_industry_composition=self.get_baseline_employees_by_naics(self.table_name, table_geoids,return_data=True)
# self.base_worker_composition=self.industries_to_occupations(self.base_industry_composition)
#
# # self.output_per_employee_by_naics=self.load_output_per_employee(return_data=True)
self.load_output_per_employee() #This function should load the df without the need of returning it
salary_data=pd.read_excel('./tables/innovation_data/national_M2019_dl.xlsx')
# salary_data=salary_data.set_index('occ_code')
self.code_to_salary={salary_data.iloc[i]['occ_code']: salary_data.iloc[i]['a_mean']
for i in range(len(salary_data))}
def return_indicator(self, geogrid_data):
# add new workers to baseline workers
# new_industry_composition = self.grid_to_industries(geogrid_data)
# new_worker_composition = self.industries_to_occupations(new_industry_composition)
# all_worker_composition={k: v for k,v in self.base_worker_composition.items()}
# for code in new_worker_composition:
# if code in all_worker_composition:
# all_worker_composition[code]+=new_worker_composition[code]
# else:
# all_worker_composition[code]=new_worker_composition[code]
industry_composition=self.grid_to_industries(geogrid_data)
worker_composition = self.industries_to_occupations(industry_composition)
num_workers=sum([worker_composition[code] for code in worker_composition])
num_workers_per_km_sq=num_workers/4
avg_salary=self.get_avg_salary(worker_composition)
# base_ouput=self.get_total_output(self.base_industry_composition)
output=self.get_total_output(industry_composition)
max_output=5e9
max_workers_per_km_sq=7500
print(output)
# total_output=base_ouput+new_ouput
self.value_indicators=[{'value': min(1, avg_salary/80000), 'raw_value': avg_salary, 'name': 'Average Salary',
'viz_type': self.viz_type, 'units': 'USD'},
{'value': min(1, output/(max_output)), 'name': 'Productivity',
'viz_type': self.viz_type, 'raw_value': output, 'units': 'USD'},
{'value': min(1, num_workers_per_km_sq/max_workers_per_km_sq), 'raw_value': num_workers_per_km_sq,'name': 'Employment Density',
'viz_type': self.viz_type, 'units': 'employees/sq_km'}]
return self.value_indicators
# def return_baseline(self):
# base_ouput=self.get_total_output(self.base_industry_composition)
# base_avg_salary=self.get_avg_salary(self.base_worker_composition)
# return [{'value': min(1, base_avg_salary/100000), 'name': 'Average Earnings',
# 'viz_type': self.viz_type},
# {'value': base_ouput, 'name': 'Industry Output',
# 'viz_type': self.viz_type}]
def get_avg_salary(self, worker_composition):
total_salary=0
denom=0
for occ_code in worker_composition:
padded_occ_code=occ_code.ljust(7, '0')
if padded_occ_code in self.code_to_salary:
salary=self.code_to_salary[padded_occ_code]
else:
padded_occ_code=occ_code[:-1].ljust(7, '0')
salary=self.code_to_salary[padded_occ_code]
weight=worker_composition[occ_code]
total_salary+=salary*weight
denom+=weight
# print('{} : {}'.format(padded_occ_code,self.code_to_salary[padded_occ_code]))
avg_salary=total_salary/denom
return avg_salary
def get_total_output(self, industry_composition):
total_ouput=0
for naics in industry_composition:
naics_2=naics[:2]
if not naics_2 in ['11', '92']: # ignore agriculture and public order/safety
total_ouput+=industry_composition[naics]*self.output_per_employee_by_naics[naics_2]
return 1000*total_ouput
def main():
E = EconomicIndicator(table_name='corktown',
name='Economic')
H = Handler('corktown', quietly=False)
H.add_indicator(E)
print(E.return_baseline())
print(E.return_indicator(H.get_geogrid_data()))
if __name__ == '__main__':
main()