A review of global gridded gross domestic product (GDP) datasets
Date
2021
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Publisher
University of Delaware
Abstract
Gross domestic product (GDP) is one of the most widely used socioeconomic indicators in development planning and studies. Spatially explicit gridded data products are an increasing area of research with applications in fields ranging from development activities, humanitarian crises, natural disasters, pollution monitoring to policy making at local, national, and regional levels. Gridded GDP data products help to understand the socio-economic dynamics in these different applications, when combined with other spatially-explicit gridded products such as population, emissions, agriculture, and precipitation. While historically, GDPs are mostly reported as a single metric at national or subnational administrative levels, gridded GDP data products provide robust economic outlook for regions at a much finer scale. Recent development in gridded data products – most notably, gridded population – is promising but a focus on gridded GDP is still lacking. This thesis provides a review and comparison of the most comprehensive global gridded GDP data products available by analyzing the datasets qualitatively. It focuses on examining their underlying approaches and input data to determine their goodness-of-fit and to fully understand the characteristics of the products. The goal of this thesis is to help the data user community make informed decisions on the appropriateness of the dataset. Analyzing in terms of the effects of scale, resolution (temporal and spatial), semantics, and modelling intensity, the characteristics of datasets are comprehensively examined and compared from an application perspective. Analysis of the gridded GDP data products show that their differences arise fundamentally from the source of their underlying input data such as gridded population datasets. Results also show how datasets with inherently similar input data can yield different GDP estimates because of differences in temporal agreement and spatial scale. The temporal resolution of underlying input data and other ancillary variables were found to have an important role in determining data accuracy. As gridded GDP becomes more widely used, it is increasingly important to discuss the uncertainties and the fitness-for-use of the data products. ☐ Keywords: global gridded GDP, fitness-for-use, uncertainties
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Keywords
Fitness-for-use, Global gridded gross domestic product, Uncertainties