Identifying Employment Subcenters: The Method of Exponentially Declining Cutoffs

Ban, Arnott, Macdonald 2017

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.

Publication
Land 6 (1): pg. 17

Open source supplementary (R) code for identifying employment subcentres available with the publication.

Jacob L. Macdonald
Jacob L. Macdonald
Geographic Data Science Research Associate

My research interests include urban-environmental data science and impact evaluation.