def main_County():
# Import air data
airEmissions = read_airEmissions_County()
# Import smoking data
smoking = readSmoking()
# Import cancer data
allCancer = mergeCancer_County()
# Import county level population data
# acsCounty = popData('county')
# Join air emission data with cancer rates data
data_merged = pd.merge(allCancer, airEmissions, left_on = 'countyName', right_on = 'county')
data_merged = data_merged.drop('county', 1)
smokeMerge = pd.merge(data_merged, smoking, left_on ='countyName', right_on = 'County Name')
testOls = pd.merge(data_merged[['observed_Total_Per100k', 'countyName']], airEmissions, left_on = 'countyName', right_on = 'county')
testOls=testOls.drop('county',1)
testOls=pd.merge(testOls, smoking, left_on='countyName', right_on='County Name')
testOls.drop(['County Name', 'Location', '95% CI', 'cCode'], inplace=True, axis=1)
# print testOls.columns.values
# mod = smf.ols(formula='observed_Total_Per100k ~ n_5_1_fugitive_air + n_5_2_stack_air + n_5_2_stack_air_benzene + n_5_1_fugitive_air_benzene', data = testOls).fit()
# mod = smf.ols(formula='observed_Total_Per100k ~ n_5_1_fugitive_air + n_5_2_stack_air + pctSmoking', data = testOls).fit()
# print(mod.summary())
correlation_table = smokeMerge.corr()
correlation_table.to_csv('data/CorrelationTable/county_correlationTable.csv')
def main_CensusTract():
# Import air data
airEmissions = read_airEmissions_CensusTract()
#print airEmissions['n_5_1_fugitive_air_dioxin'].describe()
# Import cancer data
# allCancer = readIndivCancer_CensusTract()
allCancer = mergeCancer_Tract()
#allCancer.to_csv('/Users/Steve/Github/ny_cancerAnalysis/data/NYSDOH_CancerMapping_Data_2005_2009/allCancer.csv')
# Import smoking data
smoking = readSmoking()
# Import census tract level population data
acsTract = popData('tract')
# Import cancer risk data
cancerRisk = read_cancerRisk_CensusTract()
# Join air emission data with cancer rates data
data_merged = pd.merge(allCancer, airEmissions, how='left', left_on = 'geoid11', right_on = 'geoid')
data_merged.fillna(0, inplace=True) # To avoid losing those tracts in model with smoking and demographic data but no chemical releases
data_merged = data_merged.drop('geoid', 1)
data_merged['countyCode'] = data_merged['tractFIPS'].str[:3]
data_merged = pd.merge(data_merged, smoking, left_on = 'countyCode', right_on = 'cCode')
data_merged = pd.merge(data_merged, acsTract, left_on = 'geoid11', right_on = 'Geo_FIPS')
data_merged = pd.merge(data_merged, cancerRisk, left_on = 'Geo_FIPS', right_on = 'GEOID')
# Produce correlation table
correlation_table = data_merged.corr()
correlation_table.to_csv('data/CorrelationTable/censusTract_correlationTable.csv')
cancer_list = ['observed_Bladder_Per100k', 'observed_Bone_Per100k', 'observed_Brain_Per100k',
'observed_Breast_Per100k', 'observed_Colorectal_Per100k','observed_Esophagus_Per100k', 'observed_Kidney_Per100k',
'observed_Larynx_Per100k', 'observed_Leukemia_Per100k', 'observed_Liver_Per100k', 'observed_Lung_Per100k',
'observed_Mesothelioma_Per100k', 'observed_NHL_Per100k', 'observed_Nasal_Per100k', 'observed_Oral_Per100k',
'observed_Other_Per100k', 'observed_Ovary_Per100k', 'observed_Pancreas_Per100k', 'observed_Prostate_Per100k',
'observed_Soft_Tissue_Per100k', 'observed_Stomach_Per100k', 'observed_Testis_Per100k', 'observed_Thyroid_Per100k',
'observed_Uterus_Per100k', 'observed_Total_Per100k']
chemical_list = ['n_5_1_fugitive_air', 'n_5_2_stack_air', 'airTotal', 'n_5_1_fugitive_air_benzene',
'n_5_2_stack_air_benzene', 'benzeneTotal', 'n_5_1_fugitive_air_toluene', 'n_5_2_stack_air_toluene',
'tolueneTotal', 'n_5_1_fugitive_air_ethylbenzene', 'n_5_2_stack_air_ethylbenzene', 'ethylbenzeneTotal',
'n_5_1_fugitive_air_xylene', 'n_5_2_stack_air_xylene', 'xyleneTotal', 'n_5_1_fugitive_air_formaldehyde',
'n_5_2_stack_air_formaldehyde', 'formaldehydeTotal', 'BTEX_fugitive', 'BTEX_stack', 'BTEX_total',
'n_5_1_fugitive_air_dioxin', 'n_5_2_stack_air_dioxin', 'dioxinTotal']
for chemical in chemical_list:
with open('data/Regression/'+chemical+'.csv', 'w') as f:
# columns = ['Cancer', 'Coefficient', 'p-Value', 'Std. Error', 'Adj. R']
# result_df = pd.DataFrame(index=columns)
for cancer in cancer_list:
mod = smf.ols(formula=cancer+' ~ '+chemical+' + \
pctSmoking + pctElderly + income + higherEd + unemploy', data = data_merged).fit(cov_type='HC0')
result_df = pd.DataFrame({
'Cancer': cancer,
'Coefficient': mod.params.apply(lambda x: round(x, 3)),
'p-Value': mod.pvalues.apply(lambda x: round(x, 3)),
'Std. Error': mod.bse.astype(int),
'Adj. R': round(mod.rsquared_adj, 3)})
# Coefficient for air emission feature is mod.params[1]
if mod.params[1] > 1.0:
print 'Cancer and pollutant combination with Coefficient > 1:'
print mod.params[1], mod.pvalues[1], cancer, chemical
result_df.to_csv(f)
# Test model
mod = smf.ols(formula='observed_Esophagus_Per100k ~ n_5_1_fugitive_air_dioxin + \
pctSmoking + pctElderly + income + higherEd + unemploy', data = data_merged).fit(cov_type='HC0')
print mod.summary()
# Correlation Table Heat Map
hm(correlation_table)
return data_merged
