Modelling global human settlement to better inform annual population modelling

Since 1950, the world’s population has shifted from being largely rural to majority urbanised. This trend of increasing urbanisation of population and increasing land use transitions promoting the growth of settlements and the built-environment, are expected to continue in future decades, particularly in low- and middle-income countries. These trends are accompanied by rapidly shifting subnational demographics and spatial distributions of populations, even within urbanised areas. Accurate and timely data is required to develop adaptive strategies for these shifting trends and minimising potential negative impacts. While multi-temporal, high-resolution datasets of built-settlement extent have become globally available, there remain gaps in their coverage and globally consistent methods of predicting future built-settlement expansion at regular intervals have not kept pace with these new data.This thesis develops and validates a country-specific yet globally applicable means of annually interpolating built-settlement extents and projecting built- settlement extents into the near future using relative changes in subnational population and lights at night radiance. Additionally, I demonstrate the utility of this modelling framework within a global population modelling context across a period of 13 years. This thesis improves upon previous urban growth modelling approaches by demonstrating that relative changes in population can be sufficient, in and of themselves and as causal proxies for changes in economics, for accurately predicting areas undergoing built-settlement expansion across time and space. Additionally, this thesis validates its predictions at the pixel level, something not done by previous global urban and settlement modelling approaches. By addressing the limits that exist within current global urban modelling approaches, such as large or specific data requirements and subjective assumptions of growth factors/parameters, the modelling frameworks presented in this thesis allows for more consistent, frequent, and accurate built-settlement predictions. By extension, these accurate, time-specific built-settlement predictions allow for better, time-specific population mapping across the globe. Improved knowing of where and when built-settlement appeared allows for further investigations into arable land use consumption in relation to population dynamics, temporally fine-scale changes in population distributions across space in relation to climate change stresses, built-settlement expansion and greenhouse gas emissions, and trends in built-settlement expansion in relation to sea level rise, to name a few