We thank the editor, Steven Brakman, and three anonymous referees for providing useful suggestions. We are also grateful to J. Caballé, G. Hewings, S. MacEachern, C. Nicodemo, J. Parr, S. Rosenthal, and the participants at the Third Spatial Econometric World Conference (Barcelona), XXXIV SAEe (Valencia), 58th North American Regional Science Council meeting (Denver) as well as participants at the seminar at Université de Luxembourg and CUNY for comments. Special thanks go to M.A. Garcia-Lopez for professional assistance with the ArcView software. The usual disclaimer applies. R. Nicolini's research was supported by a Ramón y Cajal contract. Financial support from research grants 2009SGR600 and SEJ2008-01850 is acknowledged.
ON THE POPULATION DENSITY DISTRIBUTION ACROSS SPACE: A PROBABILISTIC APPROACH†
Article first published online: 30 APR 2013
© 2013, Wiley Periodicals, Inc.
Journal of Regional Science
Volume 53, Issue 3, pages 481–510, August 2013
How to Cite
Epifani, I. and Nicolini, R. (2013), ON THE POPULATION DENSITY DISTRIBUTION ACROSS SPACE: A PROBABILISTIC APPROACH. Journal of Regional Science, 53: 481–510. doi: 10.1111/jors.12018
- Issue published online: 24 JUL 2013
- Article first published online: 30 APR 2013
- Manuscript Accepted: SEP 2012
- Manuscript Revised: JUL 2012
- Manuscript Received: JUN 2011
- Ramón y Cajal. Grant Numbers: 2009SGR600, SEJ2008-01850
Working within a Bayesian parametric framework, we develop a novel approach to studying the distribution of regional population density across space. By exploiting the Gamma distribution, we are able to introduce heterogeneity across space without incurring an a priori definition of territorial units. Our contribution also permits the inclusion of an approximation of individual preferences as a further driving force in location choices. We perform an empirical application to the case of Massachusetts. Our results demonstrate that a subjective measure of distance performs well in replicating the population distribution across Massachusetts.