Nataliya A Rybnikova is a PhD student. She has her expertise in testing and empirical validating a possibility that missing data on geographic concentrations of economic activities can be reconstructed using remote techniques, that is, satellite imagery of artificial light-at-night, provided by US-DMSP, VIIRS-DNB and ISS.

Boris A Portnov is Professor at the University of Haifa. His current research covers interrelated aspects of environmental studies, population geography, and urban & regional planning, such as Environmental Epidemiology, Environmental Factors of Real Estate Appraisal, Urban Clustering, Internal Migration, Interregional Inequality and Sustainability of Urban Growth in Peripheral Areas.


Statement of the Problem: Educational and research activities (R&EAs) are major forces behind modern economic growth. However data on geographic location of such activities are poorly reported, which complicates a comparative analysis of their patterns and forces behind their geographic concentrations. The purpose of this study is to check the hypothesis, whether intensities and spectral properties of artificial light-at-night (ALAN) could be used for effective identification of different economic activities on the ground, due to the unique light "signature" of each economic activity. Methodology & Theoretical Orientation: In order to develop activity identification models, in situ measurements of ALAN intensities and spectral properties were carried out at the locations of different economic activities in the Greater Haifa Metropolitan Area. For this task we used an illuminance spectrophotometer CL-500A portable device, measuring the total and spectral irradiance of ALAN, incremented by a 1-ηm pitch, from 360 to 780 ηm. The total number of measurements was 610, including 148 measurements, carried out near four research institutions, located in the City of Haifa. Findings: As our analysis shows, ALAN intensities, emitted by different economic activities at peak wavelengths, help with their identification. In particular, logistic regressions, incorporating ALAN intensities at the peak or near-peak wavelengths, and geographical attributes of the sites as controls, succeeded to predict correctly 98.6% of the actual locations of existing R&EAs. A multispectral image of the Haifa bay area, obtained from the Astronaut Photography Database, was used for the model's validation. Conclusion & Significance: The current study is apparently the first one which uses ALAN spectral properties to identify on-ground economic activities, using R&EAs as a test case, and the proposed approach may be used in future studies for the identification of various on-ground EAs, access to which is restricted or information unavailable, by using remote sensing tools.