Letters in Spatial and Resource Sciences

, Volume 8, Issue 3, pp 307–334 | Cite as

Using light-at-night (LAN) satellite data for identifying clusters of economic activities in Europe

  • Natalya A. Rybnikova
  • Boris A. PortnovEmail author
Original Paper


Enterprises organized in clusters are often efficient in stimulating urban development, productivity and profit outflows. Identifying the clusters of economic activities thus becomes an important step in devising regional development policies, aimed at the formation of clusters of economic activities in geographic areas in which this objective is desirable. However, a major problem with the identification of such clusters stems from limited reporting by individual countries and administrative entities on the regional distribution of specific economic activities, especially for small regional subdivisions. In this study, we test a possibility that missing data on geographic concentrations of economic activities in the European NUTS3 regions can be reconstructed using light-at-night satellite measurements, and that such reconstructed data can then be used for cluster identification. The matter is that light-at-night, captured by satellite sensors, is characterized by different intensity, depending on its source—production facilities, services, etc. As a result, light-at-night can become a marker of different types of economic activities, a hypothesis that the present study confirms. In particular, as the present analysis indicates, average light-at-night intensities emitted from NUTS3 regions help to explain up to 94 % variance in the areal density of several types of economic activities, performing especially well for professional, scientific and technical services \(({R}^{2}=0.742{-}0.939)\), public administration \(({R}^{2}=0.642{-}0.934)\), as well as for arts, entertainment and recreation \((\hbox {R}^{2}=0.718{-}0.934)\). As a result, clusters of these economic activities can be identified using light-at-night data, thus helping to supplement missing information and assist regional analysis.


Economic activities Clusters Satellite photometry Light-at-night Europe Nomenclature of Territorial Units for Statistics (NUTS) 

JEL Classification

C13 C38 O52 


  1. Amaral, S., Monteiro, V., Camara, G., Quintanilha, J.A.: DMSP/OLS night-time light imagery for urban population estimates in the Brazilian Amazon. Int J Remote Sens 27(5), 855–870 (2006)CrossRefGoogle Scholar
  2. Anselin, L.: Local indicators of spatial association–LISA. Geogr Anal 27, 93–115 (1995)CrossRefGoogle Scholar
  3. Atzema, O., Dijk, J.: The persistence of regional unemployment disparities in The Netherlands. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 147–168. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. Bhandari, L., Roychowdhury, K.: Night lights and economic activity in India: a study using DMSP-OLS night time images. Proc. Asia-Pacific Adv. Netw. 32, 218–236 (2011)CrossRefGoogle Scholar
  5. Campos, C., Prothero, R.: The Spatial Distribution of Industries. Office for National Statistics (2012). Accessed April 2014
  6. Cauwels, P., Pestalozzi, N., Sornette, D.: Dynamics and spatial distribution of global nighttime lights. EPJ Data Sci. 3, 2 (2014). Accessed March 2014
  7. Chen, X., Nordhaus, W.D.: The value of luminosity data as a proxy for economic statistics. NBER Working Paper (2010). Accessed March 2014
  8. Cortright, J.: Making Sense of Clusters: Regional Competitiveness and Economic Development. The Brookings Institution, Washington, DC (2006)Google Scholar
  9. Cuadrado-Roura, J.R., Rubalcaba-Bermejo, L.: Specialization and competition amongst European cities: a new approach through fair and exhibition activities. Reg. Stud. 32(2), 133–147 (1998)CrossRefGoogle Scholar
  10. Defence meteorological satellite program (DMSP). DMSP Nighttime Lights Data (2014). Accessed January 2014
  11. Desrochers, P., Sautet, F.: Cluster-based economic strategy, facilitation policy and the market process. Rev. Aust. Econ. 17(2/3), 233–245 (2004)CrossRefGoogle Scholar
  12. Doll, C.N.H., Muller, J.P., Elvidge, C.D.: Nighttime imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. J. Human Environ. 29(3), 157–162 (2000)CrossRefGoogle Scholar
  13. Doll, C.N.H., Muller, J.P., Morley, J.G.: Mapping regional economic activity from night-time light satellite imagery. Ecol. Econ. 57(1), 75–92 (2006)CrossRefGoogle Scholar
  14. Duranton, G., Morrow, P., Turner, M.: Roads and Trade: Evidence from the US. CEPR Discussion Paper No. DP9393 (2013). Accessed April 2014
  15. Ebener, S., Murray, C., Tandon, A., Elvidge, C.D.: From wealth to health: modeling the distribution of income per capita at the subnational level using nighttime light imagery. Int. J. Health Geogr. (2005). Accessed February 2014
  16. Economic and Social Research Institute (ESRI): ESRI GeoDatabase (2013). Accessed December 2013
  17. Elvidge, C., Baugh, K., Kihn, E., Kroehl, H., Davis, E., Davis, C.: Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. Int. J. Remote Sens. 18, 1373–1379 (1997)CrossRefADSGoogle Scholar
  18. Eurostat Portal (EP): Statistical Databases (2013). Accessed December 2013
  19. Eurostat Portal (EP). Geographical Information: Administrative Units/Statistical Units (2014). Accessed February 2014
  20. Eurostat Portal (EP) (2014a). History of NUTS. Accessed January 2014
  21. Eurostat Portal (EP). Nomenclature of Territorial Units for Statistics (2014b). Accessed April 2014
  22. Eurostat’s Metadata Server (EMS). Statistical Classification of Economic Activities in the European Community, Rev. 2 (2008) (2014). Accessed January 2014
  23. Feser, E.J., Koo, K., Renski, H.S., Sweeney, S.H.: Incorporating Spatial Analysis in Applied Industry Cluster Studies. Working paper. University of North Carolina, Chapel Hill (2001)Google Scholar
  24. Fotheringham, A.S., Rogerson, P.A.: The SAGE Handbook of Spatial Analysis (2009)Google Scholar
  25. Fujita, M., Krugman, P.: The new economic geography: past, present and the future. Paers Reg. Sci. 83(1), 139–164 (2004)CrossRefGoogle Scholar
  26. GeoDa Center for geospatial analysis and computation (GeoDa). Glossary of Key Terms (2014). Accessed March 2014
  27. Ghosh, T., Anderson, S., Powell, R.L., Sutton, P.C., Elvidge, C.D.: Estimation of Mexico’s informal economy and remittances using nighttime imagery. Remote Sens. 1(3), 418–444 (2009)CrossRefADSGoogle Scholar
  28. Ghosh, T., Anderson, S.J., Elvidge, C.D., Sutton, P.C.: Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability 5(12), 4988–5019 (2013)CrossRefGoogle Scholar
  29. Ghosh, T., Powell, R., Elvidge, C.D., Baugh, K.E., Sutton, P.C., Anderson, S.: Shedding light on the global distribution of economic activity. Open Geogr. J. 3, 147–160 (2010)CrossRefGoogle Scholar
  30. Gil, C., Pascual, P., Rapun, M.: Does decentralisation matter to regional inequalities? The case of small countries. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 211–232. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  31. Gordon, I., McCann, P.: Industrial clusters: complexes, agglomeration and/or social networks? Urban Stud. 37(3), 513–532 (2000)CrossRefGoogle Scholar
  32. Green, H.: The Company Town: The Industrial Edens and Satanic Mills that Shaped the American Economy. Basic Books Press, New York (2010)Google Scholar
  33. Haim, A., Portnov, B.A.: Light Pollution as a New Risk Factor for Human Breast and Prostate Cancers. Springer, Dordrecht (2013)CrossRefGoogle Scholar
  34. Henderson, J.V.: Cities and development. J. Reg. Sci. 50(1), 515–540 (2010)PubMedCentralCrossRefPubMedGoogle Scholar
  35. Henderson, J.V., Storeygard, A., Weil, D.N.: Measuring Economic Growth from Outer Space (2009). Accessed March 2014
  36. IBM, SPSS Library (IBM SPSS). General linear modeling in SPSS for Windows (2014). Accessed March 2014
  37. Imhoff, M.L., Lawrence, W.T., Stutzer, D.C., Elvidge, C.D.: A technique for using composite DMSP/OLS city lights satellite data to map urban area. Remote Sens. Environ. 61(3), 361–370 (1997)CrossRefGoogle Scholar
  38. Jacobs-Crisioni, C., Rietveld, P., Koome, E.: The impact of spatial aggregation on urban development analyses. Appl. Geogr. 47, 46–56 (2014)CrossRefGoogle Scholar
  39. Ketels, C.: The development of the cluster concept–present experiences and further developments. In: NRW Conference on Clusters (2003). Accessed on July 2013
  40. Ketels, C., Memedovic, O.: From clusters to cluster-based economic development. Int. J. Technol. Learn. Innov. Dev. 1(3), 375–392 (2008)CrossRefGoogle Scholar
  41. Kies, U., Mrosek, T., Schulte, A.: Spatial analysis of regional industrial clusters in the German forest sector. Int. Forest. Rev. 11(1), 38–51 (2009)CrossRefGoogle Scholar
  42. Kloog, I., Haim, A., Stevens, R.G., Barchana, M., Portnov, B.A.: Light at night co-distributes with incident breast but not lung cancer in the female population of Israel. Chronobiol. Int. 25(1), 65–81 (2007)CrossRefGoogle Scholar
  43. Kloog, I., Haim, A., Stevens, R.G., Portnov, B.A.: Global co-distribution of light at night (LAN) and cancers of prostate, colon, and lung in men. Chronobiol. Int. 26(1), 108–125 (2009)CrossRefPubMedGoogle Scholar
  44. Kloog, I., Stevens, R.G., Haim, A., Portnov, B.A.: Nighttime light level co-distributes with breast cancer incidence worldwide. Cancer Causes Control 21, 2059–2068 (2010)CrossRefPubMedGoogle Scholar
  45. Krugman, P.A.: Increasing returns and economic geography. J. Polit. Econ. 99, 483–499 (1991)CrossRefGoogle Scholar
  46. Krugman, P.: The new economic geography, now middle-aged. Reg. Stud. 45(1), 1–7 (2011)CrossRefGoogle Scholar
  47. Kulkarni, R., Haynes, K., Stough, R., Riggle, J.: Light based growth indicator: exploratory analysis of developing a proxy for local economic growth based on night lights. Reg. Sci. Policy Pract. 3(2), 101–113 (2011)CrossRefGoogle Scholar
  48. Loikkanen, H., Riihelä, M., Sullström, A.: Regional income convergence and inequality in boom and bust: results from micro data in Finland 1971–2000. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 109–128. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  49. Mellander, S., Stolarick, K., Matheson, Z., Lobo, J.: Night-time light data: a good proxy measure for economic activity? In: CESIS Electronic Working Paper Series (2013). Accessed February 2014
  50. Meunier, O., Mignolet, M.: Regional employment disparities in Belgium: some empirical results. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 85–108. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  51. Morgenroth, E.: Exploring the Economic Geography of Ireland. ESRI: Working Paper No. 271 (2008). Accessed April 2014
  52. National geophysical data center (NOAA). Global Radiance Calibrated Nighttime Lights (2014). Accessed March 2014
  53. O’Leari, E.: Regional disparities in Ireland: the roles of demography, profit outflows, productivity, structural change and regional policy 1960–1996. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 129–146. Springer, Heidelberg (2005)Google Scholar
  54. Openshaw, S.: The modifiable areal unit problem. In: Concepts and Techniques in Modern Geography, Monograph Series #38. Geo Books, London (2014)Google Scholar
  55. Pacheco, A.I., Tyrrel, T.J.: Testing spatial patterns and growth spillover effects in clusters of cities. Geogr. J. 4, 275–285 (2002)Google Scholar
  56. Porter, M.: Location, competition, and economic development: local clusters in a global economy. Econ. Dev. Q. 14(1), 15–20 (2000)CrossRefGoogle Scholar
  57. Porter, M.E.: The Competitive Advantage of Nations. The Free Press, New York (1990)CrossRefGoogle Scholar
  58. Portnov, B.: Interregional disparities in Israel: patterns and trends. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 187–210. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  59. Portnov, B.A.: Does the choice of geographic units matter for the validation of Gibrat’s law? Region Dev. 36, 79–106 (2012)Google Scholar
  60. Portnov, B.A., Erell, E.: Urban Clustering: The Benefits and Drawbacks of Location. Ashgate, Aldershot (2001)Google Scholar
  61. Portnov, B., Schwartz, M.: Urban clusters as growth foci. J. Reg. Sci. 49(2), 287–310 (2009)CrossRefGoogle Scholar
  62. Reidsma, P., Ewert, F., Lansink, A.O., Leemans, R.: Adaptation to climate change and climate variability in European agriculture: the importance of farm level responses. Eur. J. Agron. 32(1), 91–102 (2010)CrossRefGoogle Scholar
  63. Roux, A.V.D., Franklin, T.G., Alazraqui, M., Spinell, H.: Intraurban variations in adult mortality in a large Latin American city. J. Urban Health Bull. New York. Acad. Med. 84(3), 319–333 (2007)Google Scholar
  64. Saxenian, A.: Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Harvard University Press, Cambridge (1994)Google Scholar
  65. Scott, A.J.: On Hollywood: The Place. Princeton University Press, The Industry (2004)Google Scholar
  66. Sutton, P.: Modeling population density with night-time satellite imagery and GIS. Comput. Environ. Urban Syst. 21(3/4), 227–244 (1997)CrossRefGoogle Scholar
  67. Sutton, P.C., Elvidge, C.D., Ghosh, T.: Estimation of gross domestic product at sub-national scales using nighttime satellite imagery. Int. J. Ecol. Econ. Stat. 8, 5–21 (2007)MathSciNetGoogle Scholar
  68. Ullah, A., Giles, D.E.A.: Handbook of Applied Economic Statistics (1998). Accessed March 2014Google Scholar
  69. World Atlas (WA). Worldatlas: Continents (2014). Accessed February 2014
  70. Wostner, P.: The dynamics of regional disparities in a small country: the case of Slovenia. In: Felsenstein, D., Portnov, B.A. (eds.) Regional Disparities in Small Countries, pp. 169–186. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  71. Xiangdi, H., Yi, Z., Shixin, W., Ryu, L., Yao, Y.: GDP spatialization in China based on nighttime imagery. Geo-Inf. Sci. 14, 128–136 (2012)Google Scholar
  72. Zhao, N., Currit, N., Samson, E.: Net primary production and gross domestic product in China derived from satellite imagery. Ecol. Econ. 70(5), 921–928 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Natural Resources and Environmental Management, Faculty of ManagementUniversity of HaifaMt. CarmelIsrael

Personalised recommendations