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AccFromODMatrix_v2.py
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# -*- coding: utf-8 -*-
"""
Create accessibility from OD matrix
version: 2.0
date: Jan 2020
@author: dvale
"""
# Libraries used
import pandas as pd
import numpy
import os
# Option to make sure that we don't get an alert
# that we are trying to create a number out of a string
pd.options.mode.chained_assignment = None
print ('Working directory is: ' + os.getcwd()) # get directory
print ('')
##### SET PARAMETERS ######################################
### A) Files to be read ############################
### 1) OD Matrix (with 3 columns: Origin, Destination, TravelCost (can be time, cost, GTC, etc))
OD_Matrix = 'OD_Matrix.csv'
### 2) Table with data for opportunities (jobs, hospitals, retail, population, etc)
OppData = 'DestinationData.csv'
### 2.1) Identify Name of column with ID for each destination
DestinationIDColumn = 'Destination_Code'
# Identify Name of column with relevant destination data (eg jobs, hospitals, retail, population)
# This should be integer or float, not a string
DataColumn = 'Residents'
### B) Output file #################################
# 2) Define Output file name (CSV)
OutputFile = 'AccResults.csv'
### C) Parameters for impedance functions ##########
# 1) Exponential
# Formula: e ^ (beta * Cij)
# define beta
beta = -0.0384
# 2) Cumulative opportunities
# Formula: 1 if Cij <= MaxValue ; 0 if Cij > MaxValue
# define threshold
MaxCost = 30 # 30 minutes
# 3) Gaussian
# Formula: e ^(-1 *((Cij^2)/v))
# define v
v = 324.60614 # the equivalent to 15 minutes = 0.500
# 4) cumulative-Gaussian
# Formula: 1 if Cij <= Acceptable
# e ^(-1 *((Cij^2)/v)) if Cij > Acceptable
# define a
a = 30 # acceptable = 30 minutes
# define v
v = 324.60614 # the equivalent to (a + 15 minutes) = 0.500
##### END OF PARAMETERS ###################################
###########################################################
###### CALCULATIONS START HERE ############################
##### 1) Read OD Matrix ###################################
print ('Reading OD matrix...')
# Read OD matrix
df = pd.read_csv(OD_Matrix , index_col=False)
# If Matrix has more than 3 columns, only the first 3 will be used
df = df.iloc[:,0:3]
# We assume that the OD matrix is in fomat Origin, Destination, ODCost (time, money, CO2, etc)
df.columns = ['Origin', 'Destination', 'ODCost']
# Force string on destination field
df['Destination']=df['Destination'].astype(str)
# Force float on ODCost
df['ODCost']=df['ODCost'].astype(float)
print ('OD matrix read!')
print ('It has ' + str(len(df.index)) + ' rows' )
print ('')
##### 2) Calculate impedance function #####################
print ('Calculating impedance function...')
###### 2a) Exponential
# calculate exponential function
df['fCij_exp'] = numpy.exp(beta * df['ODCost']).astype(float)
###### 2b) Cumulative opportunities
# calculate rectangular function
df['fCij_cum'] = 0
df['fCij_cum'].loc[df['ODCost'] <= MaxCost] = 1
###### 2c) Gaussian
#calculate function
df['fCij_gau'] = numpy.exp(-1 * ((df['ODCost']**2) / v)).astype(float)
###### 2d) Cumulative-Gaussian
#calculate function
df['fCij_cumgau'] = 0 #create column
df['fCij_cumgau'].loc[df['ODCost'] > a] = numpy.exp(-1 * (((df['ODCost']-a)**2) / v)).astype(float)
df['fCij_cumgau'].loc[df['ODCost'] <= a] = 1
print ('Impedance functions calculated!')
##### 3) Read Destination Data ############################
print ('')
print ('Reading destination data...')
#### Read table with data for destinations (opportunities)
df_opportunities = pd.read_csv(OppData, index_col=False)
# Rename column and force string
df_opportunities['Destination'] = df_opportunities[DestinationIDColumn].astype(str)
# force float on data column
df_opportunities['OppData'] = df_opportunities[DataColumn].astype(float)
print ('Destination data read!')
print ('It has ' + str(len(df_opportunities.index)) + ' rows' )
##### 4) Join dataframes and calculate accessibility ######
print ('')
print ('Merging tables and calculating accessibility (this might take some time)...')
# merge tables (Join based on destination field)
df = df.merge(df_opportunities, on='Destination')
## Calculate Ojf(Cij)
# exponential
df['Opp_exp'] = df['fCij_exp'] * df[DataColumn]
# cumulative
df['Opp_cum'] = df['fCij_cum'] * df[DataColumn]
# cumulative
df['Opp_gau'] = df['fCij_gau'] * df[DataColumn]
# cumulative
df['Opp_cumgau'] = df['fCij_cumgau'] * df[DataColumn]
# Sum jobs by origin
# sum (Ojf(Cij))
df_Results = df.groupby('Origin')[['Opp_exp', 'Opp_cum', 'Opp_gau', 'Opp_cumgau']].sum()
# Rename accessibility fields
df_Results = df_Results.rename(columns={'Opp_exp': 'Acc_exponential', 'Opp_cum': 'Acc_cumulative', 'Opp_gau': 'Acc_Gaussian', 'Opp_cumgau': 'Acc_CumGaussian'})
print ('Accessibility values calculated!')
print ('Final table has ' + str(len(df_Results.index)) + ' rows' )
##### 5) Save results to csv file #########################
print ('')
print ('Saving output to csv')
# Write csv file
df_Results.to_csv(OutputFile)
print ('')
print('Results saved at ' + os.getcwd())
print('Filename: ' + OutputFile)
##################################################