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data_processing_neighborhood.py
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data_processing_neighborhood.py
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import pandas as pd
import numpy as np
import math
import glob
import argparse
import psycopg2
def weighted_avg_and_std(values, weights):
"""
Return the weighted average and standard deviation.
values, weights -- Numpy ndarrays with the same shape.
"""
average = np.average(values, weights=weights)
variance = np.average((values - average) ** 2, weights=weights) # Fast and numerically precise
return average, math.sqrt(variance)
# calculate total size
def total_size(vector):
return np.sum(vector)
def get_weights(vector):
""" Calculate the vector weights
:param vector: Positive vector
:return: Vector weights
:raise: TypeError if negative total size
"""
ts = total_size(vector)
if ts <= 0:
raise TypeError('Vector must be positive')
else:
return np.true_divide(vector, ts)
def is_heavy_industry(ateco_code):
return (5 <= ateco_code <= 9) | (19 <= ateco_code <= 30) | (36 <= ateco_code <= 39) | (42 <= ateco_code <= 43)
def is_shop_activity(ateco_code):
return (45 <= ateco_code <= 47) | (94 <= ateco_code <= 96) | (ateco_code == 56)
def is_creative_industry(ateco_code):
return (58 <= ateco_code <= 63) | (73 <= ateco_code <= 74) | (90 <= ateco_code <= 91) | (ateco_code == 71)
def make_argument_parser():
"""
Creates an ArgumentParser to read the options for this script from
sys.argv
:return:
"""
parser = argparse.ArgumentParser(
description="Housing parameters"
)
return parser
def main():
parser = make_argument_parser()
args = parser.parse_args()
print("PARAMETERS", args)
con = psycopg2.connect(database="dsaa", user="nadai", host="localhost")
df = pd.read_sql_query('select geoid, shape_area as census_area FROM census_areas_onfocus', con=con, index_col='geoid')
comp_df = pd.read_csv('data/companies.csv', dtype={'geoid', str})
comp_df = comp_df.set_index('geoid')
assert comp_df.index.is_unique
# Land value
land_df = pd.read_csv('data/land_value.csv', dtype={'geoid', str})
land_df = land_df.set_index('geoid')
assert land_df.index.is_unique
path = r'data/census/industry'
allFiles = glob.glob(path + "/*.txt")
list_ = []
for file_ in allFiles:
temp_df = pd.read_csv(file_, index_col=None, header=0, encoding="ISO-8859-1", delimiter=';')
list_.append(temp_df)
frame = pd.concat(list_)
frame['sez2011'] = frame['PROCOM'].astype(str) + frame['NSEZ'].astype(str).str.zfill(7)
frame['sez2011'] = frame.sez2011.astype(str).str.zfill(13)
frame = frame.set_index('sez2011')
frame = frame.rename(columns={'NUM_UNITA': 'num_companies', 'ADDETTI': 'employees'})
frame['ATECO3'] = frame['ATECO3'].astype(str).str.zfill(3).str[:2].astype(np.int32)
frame['heavy_ind'] = frame['ATECO3'].apply(is_heavy_industry).astype(np.int32) * frame['num_companies']
frame['shop_ind'] = frame['ATECO3'].apply(is_shop_activity).astype(np.int32) * frame['num_companies']
frame['creative_ind'] = frame['ATECO3'].apply(is_creative_industry).astype(np.int32) * frame['num_companies']
industry_df = frame.groupby('sez2011').agg(
{'num_companies': 'sum', 'heavy_ind': 'sum', 'shop_ind': 'sum', 'creative_ind': 'sum', 'employees': 'sum'})
assert industry_df.index.is_unique
path = r'data/census' # use your path
allFiles = glob.glob(path + "/*.csv")
list_ = []
for file_ in allFiles:
temp_df = pd.read_csv(file_, index_col=None, header=0, encoding="ISO-8859-1", delimiter=';')
list_.append(temp_df)
frame = pd.concat(list_)
frame = frame.rename(columns={'SEZ2011': 'sez2011'})
frame['sez2011'] = frame.sez2011.astype(str).str.zfill(13)
frame = frame.set_index('sez2011')
frame = frame[['P1', 'A3', 'E1', 'E4', 'E3', 'P137', 'P139', 'A44',
'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16']]
frame = frame.rename(columns={
'P1': 'population',
'A3': 'vacant_buildings',
'E1': '#_buildings',
'E3': '#_res_buildings',
'E4': '#_comm_buildings',
'A44': 'm2_residential'
})
assert frame.index.is_unique
land_use_df = pd.read_csv('data/generated_files/urban_atlas_census.csv', dtype={'sez2011': str})
land_use_df = land_use_df.set_index('sez2011')
assert land_use_df.index.is_unique
placepulse_df = pd.read_csv('data/generated_files/placepulse_census.csv', dtype={'sez2011': str})
placepulse_df = placepulse_df.set_index('sez2011')
assert placepulse_df.index.is_unique
walkability_df = pd.read_parquet('data/generated_files/osm_walkability_census.parquet')
assert walkability_df.index.is_unique
# MERGE
df = pd.merge(df, frame, left_index=True, right_index=True, how='left')
df = pd.merge(df, industry_df, left_index=True, right_index=True, how='left')
df = pd.merge(df, comp_df, left_index=True, right_index=True, how='left')
df = pd.merge(df, land_df, left_index=True, right_index=True, how='left')
df = pd.merge(df, land_use_df, left_index=True, right_index=True, how='left')
df = pd.merge(df, placepulse_df, left_index=True, right_index=True, how='left')
df = pd.merge(df, walkability_df, left_index=True, right_index=True, how='left')
assert df.index.is_unique
print(df.columns)
print("FILTERING DATASET")
df = df.astype(np.float32)
df = df.sort_index()
print("SPATIAL MATRIX")
spatial_matrix = pd.read_sql_query('select geoid1, geoid2 FROM spatial_matrix', con=con, index_col='geoid1')
spatial_matrix = pd.merge(pd.DataFrame(index=df.index), spatial_matrix, left_index=True, right_on='geoid2')
spatial_matrix = pd.merge(pd.DataFrame(index=df.index), spatial_matrix, left_index=True, right_index=True)
spatial_matrix['dist'] = 1
spatial_matrix['dist'] = spatial_matrix['dist'].astype(np.float32)
print("WRITING MATRIX")
spatial_matrix.to_parquet('data/generated_files/spatial_matrix_dsaa.parquet')
df.to_parquet('data/generated_files/dataset_dsaa.parquet')
if __name__ == '__main__':
main()