A machine learning analysis of drought and rural population change on the North American Great Plains since the 1970s

Machine learning techniques have to date not been widely used in population-environment research, but represent a promising tool for identifying relationships between environmental variables and population outcomes. They may be particularly useful for instances where the nature of the relationship is not obvious or not easily detected using other methods, or where the relationship potentially varies across spatial scales within a given study unit. Machine learning techniques may also help the researcher identify the relative strength of influence of specific variables within a larger set of interacting ones, and so provide a useful methodological approach for exploratory research. In this study, we use machine learning techniques in the form of random forest and regression tree analyses to look for possible connections between drought and rural population loss on the North American Great Plains between 1970 and 2020. In doing so, we analyzed four decades of population count data (at county-size spatial scales), monthly climate data, and Palmer Drought Severity Index scores for Canada and the USA at multiple spatial scales (regional, sub-regional, national, and county/census division levels), along with county level irrigation data. We found that in some parts of Saskatchewan and the Dakotas − particularly those areas that fall within more temperate/less arid ecological sub-regions − drought conditions in the middle years of the 1970s had a significant association with rural population losses. A similar but weaker association was identified in a small cluster of North Dakota counties in the 1990s. Our models detected few links between drought and rural population loss in other decades or in other parts of the Great Plains. Based on R-squared results, models for US portions of the Plains generally exhibited stronger drought-population loss associations than did Canadian portions, and temperate ecological sub-regions exhibited stronger associations than did more arid sub-regions. Irrigation rates showed no significant influence on population loss. This article focuses on describing the methodological steps, considerations, and benefits of employing this type of machine learning approach to investigating connections between drought and rural population change.