Thesis Title: Can GDP be measured from space? Investigating the extent to which economic output can be estimated using remote sensing data.
Primary Supervisor: Sarah Wise
Second Supervisor: Mat Disney
Funding Source: Overseas Research Scholarship
Start Date: Oct 2019
Melda is a doctoral researcher at the Bartlett Centre for Advanced Spatial Analysis. Prior to commencing her PhD, she worked as a management consultant at Monitor Deloitte in Dubai, UAE, where she focused on economic development projects for public sector clients across the Middle East. She is also the co-founder of Open Map Lebanon, a community created after the Beirut 2020 Blast that is committed to open data and supporting local organisations.
Melda has a BA in Economics and Peace & Justice Studies (double major) from Wellesley College, Boston, USA. She also has an MSc in Urban Economic Development and an MRes in Spatial Data Science & Visualisation, both from University College London, UK.
The world has witnessed historically unprecedented economic growth over the past 200 years, the impact of which is difficult to overstate. Despite being one of the critical enablers of human progress, measuring economic activity has been a continuous challenge, with few, mainly expensive solutions. The lack of high-quality, fit-for-purpose data has been cited as a key hindrance in furthering global development, including meeting the Sustainable Development Goals. This dissertation aims to substantially build upon previous research, which mainly focused on nightlight imagery, and explore completely novel approaches to understand the extent to which economic output can be estimated using satellite data. It will achieve this goal by analysing all potentially relevant publicly available datasets and conducting analysis at various scales. It will factor in heterogeneity of country and sub-country units by conducting analysis both at the global scale as well as the local scale, while also leveraging appropriate methods, such as geographic weighted regression and multilevel modelling.
Results will be evaluated and interpreted across scale and temporal resolution to address the research question from multiple dimensions. Furthermore, the variable-specific findings will be analysed to determine any notable trends, such as whether certain variables follow the hypothesised environmental Kuznets curve, which predicts a positive relationship between environmental degradation and economic growth for early development levels, then a reverse relationship after an undetermined income per capita threshold. Given the global coverage of this research, many unexpected findings could emerge, such as unique predictive variables for small island nations or regions that have experienced effects of climate change. The aim of this research will be to understand if and how economic activity using remote sensing data, with the overall goal of contributing to the effort to address the critical data gap that is rampant across many developing countries.
GDP; Economic Output; Remote Sensing; Nighttime lights; Satellite Data