You can help adding them by using this form. We have no bibliographic references for this item. ![]() It also allows you to accept potential citations to this item that we are uncertain about. This allows to link your profile to this item. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. See general information about how to correct material in RePEc.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa16p256. You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. This paper is an essay with first ideas for discussion the approach is explorative-methodological rather than one putting an empirical focus on a defined research item. However, as the synopsis of literature shows, the software has a broader range of applications - beyond microscopy and medical imaging - such as astronomy, environmental analyses, earth sciences including remote sensing, material sciences, viticulture archaeology and others. Its primary purpose has been to process images in medical and biological sciences where has obtained a de facto standard for image analysis. ImageJ is a widely used open source image analysis software that had been developed at the US National Institutes of Health since the end of the 1990s supported by a large international community of contributors. We explore whether 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), opens a useful perspective for the analysis of spatial change. ![]() The night imagery data have a spatial resolution of 30 arc-seconds and are recorded between 75°N and 65°S. Global nighttime lights imagery data are collected by the Defense Meteorological Satellite Program Operational Line Scanner (the DMSP-OLS). The images analysed in this paper are satellite images of the National Oceanic and Atmospheric Administration (NOAA). More importantly, underlying patterns of spatial heterogeneity, such as Zipf's law, or spatial dependence (change of spatial autocorrelation over time) can be made visible - without distortion implied by (changing) administrative boundaries. Spatial distribution of rural areas, urban agglomerations, border areas or other spatial categories are to be mentioned. In fact, there is also reason to use this tool in the observation of (spatial) economic patterns and trends in the more industrialised countries. However, this differentiation only holds for purposes to derive proxies for production or population data. In industrialised countries official socio-economic data are deemed sufficiently reliable, a the reason why night satellite analysis has been more of relevance for developing countries. Error variance of light emission is constant over space and independent from error in official statistics. This essentially applies to less developed countries where weak data infrastructure is often part of overall underdeveloped administrative capacities. 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. cities or rural areas, and how spatial units interact over time. ![]() Nocturnal satellite images may offer an interesting tool to generate socio-economically relevant data and to analyse the evolution of space, e.g.
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