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Exploring the Spatial Economy by Night

Bergs, Rolf (2016): Exploring the Spatial Economy by Night.

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Nocturnal satellite images may offer an interesting tool to generate socio-economically relevant data and to analyse the evolution of space, e.g. cities or rural areas, and how spatial units interact over time. So far, the major purpose of using night satellite images for economic analysis has been the search for proxies for production and population density in countries with insufficient and unreliable data infrastructure. This essentially applies to less developed countries where weak data infrastructure is often part of overall underdeveloped administrative capacities. Error variance of light emission is constant over space and independent from error in official statistics. In industrialised countries official socio-economic data are deemed sufficiently reliable, a reason why night satellite analysis has been more of relevance for developing countries. However, this only holds for purposes to derive proxies for production or population data. In fact, there is also reason to use this tool in the observation of (spatial) economic patterns and trends in the more industrialised countries. Observation of spatial distribution of rural areas, urban agglomerations, border areas or other spatial categories are to be mentioned. More importantly, underlying patterns of spatial heterogeneity, such as Zipf’s law, and spatial dependence (change of spatial autocorrelation over time) can be made visible - without distortion implied by (changing) administrative boundaries. The images analysed in this paper are satellite images of the National Oceanic and Atmospheric Administration (NOAA). The time series of nocturnal satellite imagery and the application of adequate image analysis software, such as ImageJ (in some cases to be complemented by further statistics software), provide a useful perspective for the analysis of spatial change. This paper is an essay with preliminary ideas for discussion; the approach is explorative-methodological rather than one putting an empirical focus on a defined research item.

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