Ban, J., Arnott, R. and Macdonald, J. orcid.org/0000-0002-3940-4876 (2017) Identifying employment subcenters: the method of exponentially declining cutoffs. Land, 6 (1). 17. ISSN 2073-445X
Abstract
The standard method of identifying subcenters is due to Giuliano and Small. While simple, robust and easy to apply, because it uses absolute employment density and employment cutoffs, it identifies “too few” subcenters at the metropolitan periphery. This paper presents a straight forward modification to this method aimed at remedying this weakness. The modification entails using cutoffs that decline exponentially with distance from the metropolitan center, thereby giving consideration to the employment density of a location relative to that of its locality. In urban studies, there is a long history of estimating employment density “gradients”, the exponential rate at which employment density declines with distance from the metropolitan center. These density gradients differ substantially across metropolitan areas and across time for a particular metropolitan area. Applying our method to Los Angeles, Calgary and Paris, we have found that using cutoffs that decline exponentially at one-half the estimated density gradients achieves an appealing balance between subcenters identified close to the metropolitan center and those identified at the metropolitan periphery. Many other methods of subcenter identification have been proposed that use sophisticated econometric procedures. Our method should appeal to practitioners who are looking for a simple method to apply.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | subcenter; employment subcenter; subcenter identification; Giuliano–Small; Los Angeles; Paris; Calgary |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Urban Studies & Planning (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Jan 2022 15:55 |
Last Modified: | 05 Jan 2022 15:55 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/land6010017 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181995 |