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Estimating small area demand for online package delivery

Using publicly available microdata sets, we show how estimates for online delivery purchases can be generated for small geographic areas defined in our study as micro analysis zones (MAZ) and how these estimates vary across the MAZs that featured in our study. With a focus on Miami-Dade County, we u...

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Autores principales: Fabusuyi, Tayo, Twumasi-Boakye, Richard, Broaddus, Andrea, Fishelson, James, Hampshire, Robert Cornelius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505800/
https://www.ncbi.nlm.nih.gov/pubmed/32982088
http://dx.doi.org/10.1016/j.jtrangeo.2020.102864
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author Fabusuyi, Tayo
Twumasi-Boakye, Richard
Broaddus, Andrea
Fishelson, James
Hampshire, Robert Cornelius
author_facet Fabusuyi, Tayo
Twumasi-Boakye, Richard
Broaddus, Andrea
Fishelson, James
Hampshire, Robert Cornelius
author_sort Fabusuyi, Tayo
collection PubMed
description Using publicly available microdata sets, we show how estimates for online delivery purchases can be generated for small geographic areas defined in our study as micro analysis zones (MAZ) and how these estimates vary across the MAZs that featured in our study. With a focus on Miami-Dade County, we use both the national household travel survey (NHTS) data and synthetic data obtained from Southeast Florida Regional Planning Model (SERPM) to generate demand estimates of online delivery purchases for more than 5300 distinct geographic units in Miami-Dade County. We assess the quality of the estimates using measures of predictive accuracy and by comparing the cumulative values obtained with the population estimates generated from the NHTS survey data for Miami-Dade County. Our approach fills a void in the area of purchases of online delivery items where rich observable data are typically unavailable and it also provides the added potential benefit of being easily replicated nationwide given the emphasis on the use of publicly available data.
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spelling pubmed-75058002020-09-23 Estimating small area demand for online package delivery Fabusuyi, Tayo Twumasi-Boakye, Richard Broaddus, Andrea Fishelson, James Hampshire, Robert Cornelius J Transp Geogr Article Using publicly available microdata sets, we show how estimates for online delivery purchases can be generated for small geographic areas defined in our study as micro analysis zones (MAZ) and how these estimates vary across the MAZs that featured in our study. With a focus on Miami-Dade County, we use both the national household travel survey (NHTS) data and synthetic data obtained from Southeast Florida Regional Planning Model (SERPM) to generate demand estimates of online delivery purchases for more than 5300 distinct geographic units in Miami-Dade County. We assess the quality of the estimates using measures of predictive accuracy and by comparing the cumulative values obtained with the population estimates generated from the NHTS survey data for Miami-Dade County. Our approach fills a void in the area of purchases of online delivery items where rich observable data are typically unavailable and it also provides the added potential benefit of being easily replicated nationwide given the emphasis on the use of publicly available data. Elsevier Ltd. 2020-10 2020-09-22 /pmc/articles/PMC7505800/ /pubmed/32982088 http://dx.doi.org/10.1016/j.jtrangeo.2020.102864 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Fabusuyi, Tayo
Twumasi-Boakye, Richard
Broaddus, Andrea
Fishelson, James
Hampshire, Robert Cornelius
Estimating small area demand for online package delivery
title Estimating small area demand for online package delivery
title_full Estimating small area demand for online package delivery
title_fullStr Estimating small area demand for online package delivery
title_full_unstemmed Estimating small area demand for online package delivery
title_short Estimating small area demand for online package delivery
title_sort estimating small area demand for online package delivery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505800/
https://www.ncbi.nlm.nih.gov/pubmed/32982088
http://dx.doi.org/10.1016/j.jtrangeo.2020.102864
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