Mapping geographic concentrations of quaternary industries (QIs) may help to assess regional performance and formulate informed development policies. However, fine resolution data on QIs concentrations are sparsely reported. Thus, for the year 2010, only 45% of all NUTS3 regions (i.e. regions of the third and most detailed level of the Nomenclature of Units for Territorial Statistics of the EU) provide relevant information. In this study, we investigate a possibility that artificial light-at-night (ALAN), captured by satellite sensors, can help to identify geographic concentrations of QIs. In this study, we use year-2010 NUTS3 Eurostat data, and combine them with data on ALAN intensities, obtained from the U.S. Defense Meteorological Satellite Program (US-DMSP) for the years 2000 and 2010. In both ordinary least squares (OLS) and spatial dependency (SD) models, ALAN emerged as a statistically significant predictor (t = 8.392–14.608; P <.01), helping to explain, along with other predictors, up to 75% of QIs regional variation. The obtained models and regional data presently available enabled estimates of QIs concentrations for European NUTS3 regions with missing data.
Bibliographical noteFunding Information:
This work was supported by the Ilan Ramon Scholarship, provided by the Israel Ministry of Science, Technology and Space.
© 2016 Informa UK Limited, trading as Taylor & Francis Group.
- NUTS3 regions
- Quaternary industries (QIs)
- artificial light-at-night (ALAN)
ASJC Scopus subject areas
- Computer Science Applications
- Earth and Planetary Sciences (all)