Department of Geography, Faculty of Sciences, University of Namur, Namur, Belgium.
Institute of Life, Earth, and Environment, University of Namur, Namur, Belgium.
PLoS One. 2024 Nov 12;19(11):e0310809. doi: 10.1371/journal.pone.0310809. eCollection 2024.
Knowing where people are is crucial for policymakers, particularly for the efficient allocation of resources in their country and the development of effective, people-centred policies. However, rural population distribution maps suffer from biases related to the type of dataset used to predict population density, such as the use of nighttime lights datasets in areas without electricity. This renders widely used datasets irrelevant in rural areas and biases nationwide models towards urban areas. To compensate for such biases, we aim at understanding the importance and relationship between water-related covariates and population densities in a random forest model across the urban-rural gradient. By extending a recursive feature elimination framework, we show that commonly used covariates are only selected when modelling the whole country. However, once the highest density areas are removed, water-related characteristics (especially distance to boreholes) become important covariates of population density outside of densely populated areas. This has important implications for modelling population in rural areas, including for a better estimation of the size of remote communities. When seeking to produce country-level population maps, we encourage further studies to explicitly account for rural areas by considering the urban-rural gradient and encourage the use of water-related datasets.
了解人们的位置对于政策制定者至关重要，特别是对于在本国有效分配资源和制定以人为本的有效政策而言。然而，农村人口分布地图受到用于预测人口密度的数据集类型的影响，例如在没有电力的地区使用夜间灯光数据集。这使得广泛使用的数据集在农村地区变得无关紧要，并使全国性模型偏向于城市地区。为了弥补这种偏差，我们旨在了解在城乡梯度上，与水相关的协变量与人口密度之间的重要性和关系。通过扩展递归特征消除框架，我们表明，在对全国进行建模时，仅选择常用的协变量。但是，一旦去除了人口密度最高的地区，与水相关的特征（特别是与水井的距离）就成为人口密度在人口稠密地区之外的重要协变量。这对农村地区的人口建模具有重要意义，包括更准确地估计偏远社区的规模。在寻求制作国家级人口地图时，我们鼓励进一步的研究通过考虑城乡梯度来明确考虑农村地区，并鼓励使用与水相关的数据集。
