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- Andersson, Magnus, et al.
(författare)
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Impact and Recovery : An Analysis of the Disintegration of Yugoslavia
- 2019
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Ingår i: Investigating Spatial Inequalities. - : Emerald Group Publishing Limited. - 9781789739428 - 9781789739411 ; , s. 71-85
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Bokkapitel (refereegranskat)abstract
- This chapter investigates how night-time light images acquired from the Defense Meteorological Satellite Program’s Operational Linescan System provide spatial and temporal insight into the economic impact of the disintegration of Yugoslavia. First, the chapter provides an overview of the economic development in Yugoslavia using conventional statistics, and second, it presents an analysis of the disintegration of the federation by comparing official statistics with night-time light data. Evaluating the impact of the disintegration of Yugoslavia as a federation and the conflicts arising in the wake of the break up is challenging since reliable data is missing. Therefore, satellite images, as one of the few sources of objective information, are potentially of great importance. We used yearly Operational Linescan System composites covering the period 1992–2013. The analysis is divided into small geographical units (districts) based on the republics in the former Yugoslavia.
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- Souknilanh, Keola, et al.
(författare)
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Monitoring economic development from space : using nighttime light and land cover data to measure economic growth
- 2015
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Ingår i: World Development. - : Elsevier. - 0305-750X .- 1873-5991. ; 66, s. 322-334
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Tidskriftsartikel (refereegranskat)abstract
- This study demonstrates estimations of economic activities on global, national, and subnational levels using remote sensing data, with a focus on developing economies. It extends a recent statistical framework which uses nighttime lights to estimate official income growth by accounting for agriculture and forestry which emit less or no additional observable nighttime light. The study argues that nighttime lights alone may not explain value-added by agriculture and forestry. By adding land cover data, our framework can be used to estimate economic growth in administrative areas of virtually any size.
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