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INTRODUCTION
Night light indicators of regional economic activity
Katarzyna Kopczewska
Faculty of Economic Sciences, University of Warsaw, Warszawa, Poland
Correspondence
Katarzyna Kopczewska, Faculty of Economic Sciences, University of Warsaw. Warszawa, Poland.
Email: kkopczewska@wne.uw.edu.pl
Light: in many traditions and cultures, it is linked with making things visible and understandable, and also with safety
and wealth. Conversely, darkness becomes unwanted, as it is associated with punishment or being lost, as well as
with poverty and crime. On the other hand, the speed of light is the quickest we can imagine. Light, when pouring
into a place, is unstoppable –it dominates all other phenomena, does not respect borders and squeezes into every
corner. This makes light a very spatial and objective phenomenon, and relatively easy to measure in terms of appear-
ance and intensity.
For many years light was used in science, mainly in physics and astronomy, for example in neutrino detectors,
observing the spectrum of light, understanding the variable nature of the mains voltage, generating absorption spec-
tra of molecules further used in explaining the mechanism of the greenhouse effect or providing arguments for the
quantum structure of the atom. Light is also commonly used in biology and chemistry, in plant vegetation and in
chemical reactions. But light was not considered important information in regional and social sciences for a long time.
Demography, sociology, economics or spatial management did not even think light could be the object of their stud-
ies. That was due to data and methods availability, but also the missing idea of what to do with this information.
This special issue elucidates light as a reliable and quick source of information on social issues. Firstly, one can
see it is universal worldwide –studies by Cecchini et al. (2021) for Chile, Sheludkov and Starikova (2021) for Russia,
W
ojcik and Andruszek (2021) for Poland, and Sangkasem and Puttanapong (2020) for Thailand show that currently
we can study with light any place we wish. Secondly, its application as a source of information is limited only by the
researcher's invention, as shown in Sangkasem and Puttanapong (2020) and Cecchini et al. (2021), who study
poverty, Sheludkov and Starikova (2021), who study mobility, and W
ojcik and Andruszek (2021), who analyse
wellbeing. Third, papers in this issue show that night light (NTL) data are easily accessible as satellite images and
spectral data –from the US Defense Meteorological Satellite Program (DMSP) and Operational Linescan System
(OLS) (Sangkasem & Puttanapong, 2020), or the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band
from the US Earth Observation Group (EOG) (Cecchini et al., 2021) to the Suomi National Polar-orbiting Partnership
satellite (Sheludkov & Starikova, 2021). Night light data are included in models as images or as time series of VIIRS.
Also, high-resolution daytime satellite images, for example, from Google Maps (W
ojcik & Andruszek, 2021), can sup-
port those studies. Fourth, papers show that light data are highly comparable to traditional data from statistical
offices and reveal a high correlation –as in the case of poverty in Thailand and Chile. Furthermore, they are highly
attractive owing to their time availability –they are usually available almost the next day after measurement, while
one must sometimes wait even a few years for official statistics. The data’s greatest advantage is that they can not
Received: 3 August 2022 Accepted: 3 August 2022
DOI: 10.1111/rsp3.12572
© 2022 The Author. Regional Science Policy & Practice © 2022 Regional Science Association International.
826 Reg Sci Policy Pract. 2022;14:826–827.
wileyonlinelibrary.com/journal/rsp3
only substitute but also supplement data that are unavailable from other sources, as in the case of second-house
mobility around Moscow.
A high value of this collection of papers lies first in applications to regional science problems and secondly in
methodological instructions on how to conduct similar analyses. NLT data, when retrieved from satellite or spectral
datasets, can be normalized (NLT) and transformed into indicators as a sum of lights (SL), a standard deviation of
lights (SDL) or Urban Light Index (ULI). Pre-processed data can be analysed with traditional measures such as
Moran's I, Gini index, Theil index and LISA, or used in typical models such as logit. Daytime images can provide
information on local building density and areas of greenness. Advanced topics such as VIIRS data cleaning as well as
machine learning modelling are also covered.
Interested readers will find in this special issue a comprehensive guide on how and why to analyse night light
data and daytime images to conduct modern research in regional science. This cutting-edge methodology is a novelty
in the regional field and is truly worth attention.
How to cite this article: Kopczewska, K. (2022). Night light indicators of regional economic activity. Regional
Science Policy & Practice,14(4), 826–827. https://doi.org/10.1111/rsp3.12572
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