EconPapers    
Economics at your fingertips  
 

Regression-based measure of urban sprawl for Italian municipalities using DMSP-OLS night-time light images and economic data

Angela Bergantino, Giuseppe Di Liddo and Francesco Porcelli

Applied Economics, 2020, vol. 52, issue 38, 4213-4222

Abstract: Night-time light can be used in order to evaluate the degree of urbanization and urban sprawl in a specific territory. In fact, at the local level, the lower the urban density, the higher the per-capita length of collector roads and the area covered by buildings and infrastructures. It follows that the lower the urban density, the higher the municipal luminosity. Urban sprawl is determinant in defining the mobility condition in a specific territory and the service and infrastructure needs. This paper uses regression analyses in order to estimate a ‘relative’ measure of urban sprawl that takes into account also demographic and economic characteristics. We apply this technique to a panel of Italian municipalities over the period 2004–2012 and compare the resulting measure to the ‘absolute’ measures provided by the Italian Institute for Environmental Protection and Research in order to evaluate the contribution of our measure to the knowledge of the sprawl phenomenon.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2020.1733475 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:52:y:2020:i:38:p:4213-4222

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2020.1733475

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-22
Handle: RePEc:taf:applec:v:52:y:2020:i:38:p:4213-4222