Quantifying Retail Agglomeration using Diverse Spatial Data

Abstract Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the ret...

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Autores principales:	Duccio Piovani, Vassilis Zachariadis, Michael Batty
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/89be8a46b8754bd18514af4b355525f1
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spelling	oai:doaj.org-article:89be8a46b8754bd18514af4b355525f12021-12-02T15:06:07ZQuantifying Retail Agglomeration using Diverse Spatial Data10.1038/s41598-017-05304-12045-2322https://doaj.org/article/89be8a46b8754bd18514af4b355525f12017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05304-1https://doaj.org/toc/2045-2322Abstract Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks an appropriate theoretical framework. The model we present here helps to address these issues. Based on random utility theory, we have built it around the idea of quantifying the role of floor-space and agglomeration in retail location choice. We test this model on the inner area of Greater London. The results are consistent with a super linear scaling of a retailer’s attractiveness with its floorspace, and with an agglomeration effect approximated as the total retail floorspace within a 300 m radius from each shop. Our model illustrates many of the issues involved in testing and validating urban simulation models involving spatial data and its aggregation to different spatial scales.Duccio PiovaniVassilis ZachariadisMichael BattyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Duccio Piovani Vassilis Zachariadis Michael Batty Quantifying Retail Agglomeration using Diverse Spatial Data
description	Abstract Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks an appropriate theoretical framework. The model we present here helps to address these issues. Based on random utility theory, we have built it around the idea of quantifying the role of floor-space and agglomeration in retail location choice. We test this model on the inner area of Greater London. The results are consistent with a super linear scaling of a retailer’s attractiveness with its floorspace, and with an agglomeration effect approximated as the total retail floorspace within a 300 m radius from each shop. Our model illustrates many of the issues involved in testing and validating urban simulation models involving spatial data and its aggregation to different spatial scales.
format	article
author	Duccio Piovani Vassilis Zachariadis Michael Batty
author_facet	Duccio Piovani Vassilis Zachariadis Michael Batty
author_sort	Duccio Piovani
title	Quantifying Retail Agglomeration using Diverse Spatial Data
title_short	Quantifying Retail Agglomeration using Diverse Spatial Data
title_full	Quantifying Retail Agglomeration using Diverse Spatial Data
title_fullStr	Quantifying Retail Agglomeration using Diverse Spatial Data
title_full_unstemmed	Quantifying Retail Agglomeration using Diverse Spatial Data
title_sort	quantifying retail agglomeration using diverse spatial data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/89be8a46b8754bd18514af4b355525f1
work_keys_str_mv	AT ducciopiovani quantifyingretailagglomerationusingdiversespatialdata AT vassiliszachariadis quantifyingretailagglomerationusingdiversespatialdata AT michaelbatty quantifyingretailagglomerationusingdiversespatialdata
_version_	1718388595643908096

Quantifying Retail Agglomeration using Diverse Spatial Data

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