Mapping geographical concentrations of economic activities in Europe using light at night (LAN) satellite data

References

• Amaral, S., A. M. V. Monteiro, G. Camara, and J. A. Quintanilha. 2006. “DMSP/OLS Night-Time Light Imagery for Urban Population Estimates in the Brazilian Amazon.” International Journal of Remote Sensing 27 (5): 855–870. doi:10.1080/01431160500181861.
   Web of Science ®Google Scholar
• Anderson, S. J., B. T. Tuttle, R. L. Powell, and P. C. Sutton. 2010. “Characterizing Relationships between Population Density and Night-Time Imagery for Denver, Colorado: Issues of Scale and Representation‏.” International Journal of Remote Sensing 31: 5733–5746. doi:10.1080/01431161.2010.496798.
   Web of Science ®Google Scholar
• Antonakis, J., S. Bendahan, P. Jacquart, and R. Lalive. 2014. “Causality and Endogeneity: Problems and Solutions.” Accessed March, 2014. http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199755615.001.0001/oxfordhb-9780199755615-e-007
   Google Scholar
• Bhandari, L., and K. Roychowdhury. 2011. “Night Lights and Economic Activity in India: A Study Using DMSP-OLS Night-Time Images.” Proceedings of the Asia-Pacific Advanced Network 32: 218–236. doi:10.7125/APAN.32.24.
   Google Scholar
• Cauwels, P., N. Pestalozzi, and D. Sornette. 2014. “Dynamics and Spatial Distribution of Global Night-Time Lights.” EPJ Data Science 3: 2. doi:10.1140/epjds19.
   Web of Science ®Google Scholar
• Chen, X., and W. D. Nordhaus. 2010. “The Value of Luminosity Data as a Proxy for Economic Statistics.” NBER Working Paper. Accessed March, 2014. http://www.nber.org/papers/w16317
   Google Scholar
• Cortright, J. 2006. Making Sense of Clusters: Regional Competitiveness and Economic Development/J. Cortright, 58 pp. Washington, DC: Brookings Institution.
   Google Scholar
• Doll, C. N. H., J. P. Muller, and C. D. Elvidge. 2000. “Night-Time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions.” A Journal of the Human Environment 29 (3): 157–162.
   Web of Science ®Google Scholar
• Doll, C. N. H., J.-P. Muller, and J. G. Morley. 2006. “Mapping Regional Economic Activity from Night-Time Light Satellite Imagery.” Ecological Economics 57 (1): 75–92. doi:10.1016/j.ecolecon.2005.03.007.
   Web of Science ®Google Scholar
• Ebener, S., C. Murray, A. Tandon, and C. C. Elvidge. 2014. “From Wealth to Health: Modelling the Distribution of Income per Capita at the Subnational Level Using Night-Time Light Imagery.” International Journal of Health Geographics doi:10.1186/1476-072X-4-5.
   Google Scholar
• ESRI (Economic and Social Research Institute). 2013. “ESRI GeoDatabase.” Accessed December. http://www.esri.com/software/arcgis/geodatabase/interoperability
   Google Scholar
• Elvidge, C. D., K. E. Baugh, E. A. Kihn, H. W. Kroehl, E. R. Davis, and C. W. Davis. 1997. “Relation between Satellite Observed Visible-Near Infrared Emissions, Population, Economic Activity and Electric Power Consumption.” International Journal of Remote Sensing 18: 1373–1379. doi:10.1080/014311697218485.
   Web of Science ®Google Scholar
• Elvidge, C. D., K. E. Baugh, M. Zhizhin, and F.-C. Hsu. 2013. “Why VIIRS Data are Superior to DMSP for Mapping Night-Time Lights.” Proceedings of the Asia-Pacific Advanced Network 35: 62–69. doi:10.7125/APAN.35.7.
   Google Scholar
• Elvidge, C. D., P. Cinzano, D. R. Pettit, J. Arvesen, P. Sutton, C. Small, R. Nemani, T. Longcore, C. Rich, J. Safran, J. Weeks, and S. Ebener. 2007. “The Nightsat Mission Concept.” International Journal of Remote Sensing 28: 2645–2670. doi:10.1080/01431160600981525.
   Web of Science ®Google Scholar
• EG (Eurostat Glossary). 2014. “Glossary: Nomenclature of Territorial Units for Statistics (NUTS).” Accessed February, 2014. http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:NUTS
   Google Scholar
• EP (Eurostat Portal). 2013. “Statistical Databases.” Accessed December, 2013. http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database
   Google Scholar
• EP (Eurostat Portal). 2014a. “Geographical Information: Administrative Units/Statistical Units.” Accessed February, 2014. http://epp.eurostat.ec.europa.eu/portal/page/portal/gisco_Geographical_information_maps/popups/references/administrative_units_statistical_units_1
   Google Scholar
• EP (Eurostat Portal). 2014b. “History of NUTS.” Accessed January, 2014. http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/documents
   Google Scholar
• EMS (Eurostat’s Metadata Server). 2014. “Statistical Classification of Economic Activities in the European Community, Rev. 2 (2008).” Accessed January, 2014. http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL_LINEAR&IntCurrentPage=1&StrNom=NACE_REV2&StrLanguageCode=EN
   Google Scholar
• Fotheringham, A. S., and P. A. Rogerson. 2009. The SAGE Handbook of Spatial Analysis, 528 p. London: SAGE.
   Google Scholar
• GeoDa (GeoDa Center for geospatial analysis and computation). 2014. “Glossary of Key Terms.” Accessed March, 2014. https://geodacenter.asu.edu/node/390
   Google Scholar
• Ghosh, T., S. Anderson, C. Elvidge, and P. Sutton. 2013. “Using Night-Time Satellite Imagery as a Proxy Measure of Human Well-Being.” Sustainability 5 (12): 4988–5019. doi:10.3390/su5124988.
   Web of Science ®Google Scholar
• Ghosh, T., S. Anderson, R. L. Powell, P. C. Sutton, and C. D. Elvidge. 2009. “Estimation of Mexico’s Informal Economy and Remittances Using Night-Time Imagery.” Remote Sensing 1 (3): 418–444. doi:10.3390/rs1030418.
   Web of Science ®Google Scholar
• Ghosh, T., R. Powell, C. D. Elvidge, K. E. Baugh, P. C. Sutton, and S. Anderson. 2010. “Shedding Light on the Global Distribution of Economic Activity.” The Open Geography Journal 3: 147–160. doi:10.2174/1874923201003010147.
   Google Scholar
• Han, X., Y. I. Zhou, S. Wang, R. Liu, and Y. Yao. 2012. “GDP Spatialization in China Based on Night-Time Imagery.” Geo-information Science 14: 128–136. doi:10.3724/SP.J.1047.2012.00128.
   Google Scholar
• Henderson, J. V., A. Storeygard, and D. Weil. 2009. “Measuring Economic Growth from Outer Space.” Accessed March, 2014. http://www.nber.org/papers/w15199
   Google Scholar
• IBM SPSS (IBM SPSS Statistics). 2014. “Two-Stage Least-Squares Regression.” Accessed March. http://pic.dhe.ibm.com/infocenter/spssstat/v22r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fspss%2Fregression%2Fidh_tsls.htm
   Google Scholar
• Imhoff, M. L., W. T. Lawrence, D. C. Stutzer, and C. D. Elvidge. 1997. “A Technique for Using Composite DMSP/OLS City Lights Satellite Data to Map Urban Area.” Remote Sensing of Environment 61 (3): 361–370. doi:10.1016/S0034-4257(97)00046-1.
   Web of Science ®Google Scholar
• Kloog, I., A. Haim, R. G. Stevens, M. Barchana, and B. A. Portnov. 2008. “Light at Night Co-Distributes with Incident Breast but Not Lung Cancer in the Female Population of Israel.” Chronobiology International 25 (1): 65–81. doi:10.1080/07420520801921572.
   PubMed Web of Science ®Google Scholar
• Kloog, I., A. Haim, R. G. Stevens, and B. A. Portnov. 2009. “Global Co-Distribution of Light at Night (LAN) and Cancers of Prostate, Colon, and Lung in Men.” Chronobiology International 26 (1): 108–125. doi:10.1080/07420520802694020.
   PubMed Web of Science ®Google Scholar
• Kloog, I., R. G. Stevens, A. Haim, and B. A. Portnov. 2010. “Night-Time Light Level Co-Distributes with Breast Cancer Incidence Worldwide.” Cancer Causes Control 21: 2059–2068. doi:10.1007/s10552-010-9624-4.
   PubMed Web of Science ®Google Scholar
• Kulkarni, R., K. Haynes, R. Stough, and J. Riggle. 2011. “Light Based Growth Indicator (LBGI): Exploratory Analysis of Developing a Proxy for Local Economic Growth Based on Night Lights.” Regional Science Policy and Practice 3 (2): 101–113. doi:10.1111/j.1757-7802.2011.01032.x.
   Google Scholar
• Li, X., F. Chen, and X. Chen. 2013. “Satellite-Observed Night-Time Light Variation as Evidence for Global Armed Conflicts.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (5): 2302–2315. doi:10.1109/JSTARS.2013.2241021.
   Web of Science ®Google Scholar
• Li, X., L. Ge, and X. Chen. 2013. “Detecting Zimbabwe’s Decadal Economic Decline Using Night-Time Light Imagery.” Remote Sensing 5 (9): 4551–4570. doi:10.3390/rs5094551.
   Web of Science ®Google Scholar
• Mellander, S., K. Stolarick, Z. Matheson, and J. Lobo. 2013. “Night-Time Light Data: A Good Proxy Measure for Economic Activity?” CESIS Electronic Working Paper Series. Accessed February, 2014. https://static.sys.kth.se/itm/wp/cesis/cesiswp315.pdf
   Google Scholar
• EU (European Union). 2014. “Member States of the EU.” Accessed February, 2014. http://europa.eu/about-eu/countries/index_en.htm
   Google Scholar
• Nagendra, H., J. Southworth, and C. Tucker. 2003. “Accessibility as a Determinant of Landscape Transformation in Western Honduras: Linking Pattern and Process.” Landscape Ecology 18: 141–158. doi:10.1023/A:1024430026953.
   Web of Science ®Google Scholar
• NASA (National Aeronautics and Space Administration). 2014. “Visible Earth: Catalogue of NASA Images and Animations of Our Home Planet”. Night Lights 2012 – Flat map. Accessed June, 2014. http://visibleearth.nasa.gov/view.php?id=79765
   Google Scholar
• NOAA (National Oceanic and Atmospheric Administration). 2014b. “Global Radiance Calibrated Night-Time Lights.” Accessed January, 2014. http://ngdc.noaa.gov/eog/dmsp/download_radcal.html
   Google Scholar
• NOAA (National Oceanic and Atmospheric Administration). 2014c. “Global Radiance Calibrated Night-Time Lights Product (Readme).” Accessed January, 2014. http://ngdc.noaa.gov/eog/dmsp/radcal_readme.txt
   Google Scholar
• NOAA (National Oceanic and Atmospheric Administration). 2014a. “DMSP Night-Time Lights Data.” Accessed January, 2014. http://ngdc.noaa.gov/eog/
   Google Scholar
• NIST (NIST/SEMATECH e-Handbook of Statistical Methods). 2014. “How Can I Tell if a Model Fits My Data?” Accessed March, 2014. http://www.itl.nist.gov/div898/handbook/pmd/section4/pmd44.htm
   Google Scholar
• Pacheco, A. I., and T. J. Tyrrel. 2002. “Testing Spatial Patterns and Growth Spillover Effects in Clusters of Cities.” Geographical Journal 4: 275–285.
   Google Scholar
• Portnov, B. A. 2012. “Does the Choice of Geographic Units Matter for the Validation of Gibrat’s Law?” Region & Development 36: 79–106.
   Google Scholar
• Roux, A. V. D., T. G. Franklin, M. Alazraqui, and H. Spinelli. 2007. “Intraurban Variations in Adult Mortality in a Large Latin American City.” Journal of Urban Health: Bulletin of the New York Academy of Medicine 84 (3): 319–333. doi:10.1007/s11524-007-9159-5.
   PubMed Web of Science ®Google Scholar
• Sutton, P. 1997. “Modeling Population Density with Night-Time Satellite Imagery and GIS.” Computers, Environment and Urban Systems 21 (3–4): 227–244. doi:10.1016/S0198-9715(97)01005-3.
   Google Scholar
• Sutton, P. C., C. D. Elvidge, and T. Ghosh. 2007. “Estimation of Gross Domestic Product at Sub-National Scales Using Night-Time Satellite Imagery.” International Journal of Ecological Economics & Statistics 8: 5–21.
   Google Scholar
• Ullah, A., and D. E. A. Giles. 1998. Handbook of Applied Economic Statistics. New York: Marcel Dekker.
   Google Scholar
• Witmer, F. D. W., and J. O’Loughlin. 2011. “Detecting the Effects of Wars in the Caucasus Regions of Russia and Georgia Using Radiometrically Normalized DMSP-OLS Night-Time Lights Imagery.” GIScience & Remote Sensing 48 (4): 478–500. doi:10.2747/1548-1603.48.4.478.
   Web of Science ®Google Scholar
• WA (World Atlas). 2014. “World Atlas: Continents.” Accessed February, 2014. http://www.worldatlas.com/aatlas/infopage/contnent.htm
   Google Scholar
• Wu, J., Z. Wang, W. Li, and J. Peng. 2013. “Exploring Factors Affecting the Relationship between Light Consumption and GDP Based on DMSP/OLS Night-Time Satellite Imagery.” Remote Sensing of Environment 134: 111–119. doi:10.1016/j.rse.2013.03.001.
   Web of Science ®Google Scholar
• Zhao, N., N. Currit, and E. Samson. 2011. “Net Primary Production and Gross Domestic Product in China Derived from Satellite Imagery.” Ecological Economics 70 (5): 921–928. doi:10.1016/j.ecolecon.2010.12.023.
   Web of Science ®Google Scholar
• Zhuo, L., T. Ichinose, J. Zheng, J. Chen, P. J. Shi, and X. Li. 2009. “Modelling the Population Density of China at the Pixel Level Based on DMSP/OLS Non-Radiance-Calibrated Night-Time Light Images.” International Journal of Remote Sensing 30 (4): 1003–1018. doi:10.1080/01431160802430693.
   Web of Science ®Google Scholar