Spatial distribution of GDP based on integrated NPS-VIIRS nighttime light and MODIS EVI data: a case study of Turkey
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Spatial distribution of GDP based on integrated NPS‑VIIRS nighttime light and MODIS EVI data: a case study of Turkey E. Ustaoglu1 · R. Bovkır1 · A. C. Aydınoglu1 Received: 24 March 2020 / Accepted: 14 October 2020 © Springer Nature B.V. 2020
Abstract Satellite-derived nighttime light data have been increasingly used as a proxy measure for investigating economic activity. However, there are few studies focusing on the spatialised mapping of the GDP at pixel level and further analysis of the economic differences in agricultural and non-agricultural sectors for the different regions using the VIIRS-NPP data. This paper aims to fill this gap in the literature through developing a pixel-level agricultural and non-agricultural GDP map for Turkey in 2015 by combining the VIIRS-NPP nighttime imagery, Terra MODIS-Enhanced Vegetation Index, and land use/cover data from CORINE. The inclusion of vegetation indices and land cover data would significantly improve the estimates of sectorial GDP for Turkey where agriculture is one of the dominating sectors in the Country. GDP density map offers a significant database for both researchers and policy makers in the analysis of regional economic dynamics that will assist in formulating sustainable regional growth strategies. Keywords Gross Domestic Product · Nighttime lights · VIIRS-NPP · MODIS EVI · Regression model · Turkey
1 Introduction The issue of measuring economic growth has played a crucial role in many socio-economic studies given that it is an important reference for planning and political decision making. Despite the progress achieved in methodology and techniques for measuring socio-economic activities using traditional survey-based approaches, reliable data at different administrative and geographical scales are often scarce (Thieken et al. 2006; Yue et al. 2014). For instance, in the under-developed countries, there are no usable national economic data * E. Ustaoglu [email protected] R. Bovkır [email protected] A. C. Aydınoglu [email protected] 1
Department of Geomatics Engineering, Gebze Technical University, 41400 Gebze, Kocaeli, Turkey
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due to lack of resources and capacity to acquire such data; in undemocratic regimes, there is lack of reliable statistical data at different administrative levels due to unreal declaration by local authorities; and even in developed regions, measurement errors may exist due to ignorance of shadow economies (Wu et al. 2013; Keola et al. 2015). Statistical data comprising income and socio-economic indicators are generally developed based on administrative boundaries such as regions, counties or cities. Physical data, on the other hand, are geographically extensive given that they are represented in grid or raster formats. Attempts to integrate socio-economic data with physical data may be inappropriate considering inconsistencies of statistical scales and uniformity of socio-economic data within statistical unit, which makes it difficult to represent regional economic data
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