Open AccessChoice Set Imputation in Atomistic Spatial Choice Models
Author(s) -
Alireza Zolfaghari,
John Polak,
Aruna Sivakumar
Publication year - 2016
Publication title -
transportation research record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.624
H-Index - 119
eISSN - 2169-4052
pISSN - 0361-1981
DOI - 10.3141/2564-15
Subject(s) - categorical variable , imputation (statistics) , computer science , data mining , econometrics , monte carlo method , set (abstract data type) , data set , missing data , matching (statistics) , coding (social sciences) , context (archaeology) , statistics , machine learning , mathematics , artificial intelligence , geography , archaeology , programming language
Constructing the universal choice set in spatial choice models developed at the level of elemental alternatives (atomistic models) is challenging because disaggregate data on the attributes of nonchosen alternatives are often not available. Even when the disaggregate data on nonchosen alternatives are available, matching two data sources will inevitably be error prone given that they might be collected at different times and they might have different coding for categorical variables. An important practical question in the estimation of such atomistic models, therefore, is how to construct the universal choice set in the absence of disaggregate data on the attributes of the nonchosen alternatives. This paper presents a novel approach for spatial imputation of attributes of nonchosen alternatives for estimation and application of atomistic spatial choice models in the absence of disaggregate data. The proposed approach uses the iterative proportional fitting algorithm to impute the attributes of nonchosen alternatives from aggregated data on elemental alternatives. The proposed method is validated with a Monte Carlo experiment and applied to real data in the London residential location choice context