The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1403–1409, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1403-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1403–1409, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1403-2015

  30 Apr 2015

30 Apr 2015

Using Light-at-Night (LAN) Satellite Data for Identifying Clusters of Economic Activities in Europe

N. A. Rybnikova and B. A. Portnov N. A. Rybnikova and B. A. Portnov
  • Department of Natural Resources and Environmental Management, Faculty of Management, University of Haifa, Haifa, Israel

Abstract. Enterprises organized in clusters are often efficient in stimulating urban development, productivity and profit outflows. Identifying clusters of economic activities (EAs) thus becomes an important step in devising regional development policies, aimed at facilitating regional economic development. However, a major problem with cluster identification stems from limited reporting of specific EAs by individual countries and administrative entities. Even Eurostat, which maintains most advances regional databases, provides data for less than 50% of all regional subdivisions of the 3rd tier of the Nomenclature of Territorial Units for Statistics (NUTS3). Such poor reporting impedes identification of EA clusters and economic forces behind them. In this study, we test a possibility that missing data on geographic concentrations of EAs can be reconstructed using Light-at-Night (LAN) satellite measurements, and that such reconstructed data can then be used for the identification of EA clusters. As we hypothesize, LAN, captured by satellite sensors, is characterized by different intensity, depending on its source – production facilities, services, etc., – and this information can be used for EA identification. The study was carried out in three stages. First, using nighttime satellite images, we determined what types of EAs can be identified, with a sufficient degree of accuracy, by LAN they emit. Second, we calculated multivariate statistical models, linking EAs concentrations with LAN intensities and several locational and development attributes of NUTS3 regions in Europe. Next, using the obtained statistical models, we restored missing data on EAs across NUTS3 regions in Europe and identified clusters of EAs, using spatial analysis tools.