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Exploring regional and urban clusters and patterns in Europe using satellite observed lighting

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  • Vassilis Tselios
  • Demetris Stathakis

Abstract

We explore regional and urban clusters and patterns in Europe by using satellite images of nighttime lights and by employing Exploratory Spatial Data Analysis. We map Defense Meteorological Satellite Program nighttime lights data onto the nomenclature of territorial units for statistics III, Local Administrative Units II and pixel (i.e. 1 km 2 grid cell system of Europe) level and apply global and local statistics of spatial association. Under the assumption that nighttime light data are a good proxy for economic activity, the analysis at regional level shows that the regions of global cities and megacities and their surrounding areas are hot spots of high economic activity levels. The regional analysis also reveals the polycentric hierarchical structure of Europe. Using the case studies of the regions of London and Île - de - France, the analysis at the urban level reveals the different urban structure of these two global regions and identifies the functional urban areas of London and Paris.

Suggested Citation

  • Vassilis Tselios & Demetris Stathakis, 2020. "Exploring regional and urban clusters and patterns in Europe using satellite observed lighting," Environment and Planning B, , vol. 47(4), pages 553-568, May.
    • Handle: RePEc:sae:envirb:v:47:y:2020:i:4:p:553-568
    DOI: 10.1177/2399808318788567
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    2. Chakraborty, Tanika & Mukherjee, Anirban, 2022. "Economic geography of contagion: A study on Covid-19 outbreak in India," SocArXiv gp2wr, Center for Open Science.
    3. Chakraborty, Tanika & Mukherjee, Anirban, 2022. "Economic geography of contagion: A study on Covid-19 outbreak in India," GLO Discussion Paper Series 1028, Global Labor Organization (GLO).
    4. Cats, Oded & Birch, Nigel, 2021. "Multi-modal network evolution in polycentric regions," Journal of Transport Geography, Elsevier, vol. 96(C).
    5. Chengming Li & Xiaoyan Wang & Zheng Wu & Zhaoxin Dai & Jie Yin & Chengcheng Zhang, 2021. "An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data," Sustainability, MDPI, vol. 13(9), pages 1-16, April.

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