Small area population forecasting using geospatial big datasets and national census in low and middle income countries

利用地理空间大数据集和中低收入国家人口普查进行小区域人口预测

• 批准号: 2751200
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2751200
• 关键词: Small; area; population; forecasting; using

Africa's population is forecasted to double over the next 40 years, contributing to a high rate ofurbanisation, and as a consequence, this trigger environmental, social-economic and health variations.Small geographic area details are crucial to plan and be abreast of these changes. Despite theirimportance, small area forecasts remain under-explored which limit the potential of low and middlecountries including Namibia to apply evidence-based solutions. Primary data sources for small areaforecasts include population and housing censuses, surveys and administrative data. Although thecensus provides a full count of the population as well as data on fertility, mortality and migration, theseoperations are usually done only every ten years. National statistics offices typically generate projectionsto produce inter-censual estimates; however, these are done at very coarse spatial scales. Hence,making it challenging to capture demographic trends and patterns at finer geographic scales, especiallythrough migration. Mapping from satellites can capture the geographically uneven population growththat is often difficult to predict and account for using standard projection models (Wardrop et al., 2018).Likewise, mobile phone data offer opportunities to capture seasonal variation over time and space inmigration patterns. I am proposing a study that aims to assess how geospatial metrics can improve theaccuracy of population projection models. The study will explore different projection methods withcensus 2011 as a baseline and use top-down models to disaggregate to small areas, and results will bevalidated against 2021 Census data to identify the best estimates.

预计非洲的人口将在未来40年内翻一番，这导致了高腹化率，因此，这种触发了环境，社会经济和健康变化。小地理领域的细节对于计划至关重要，并与这些变化保持一致。尽管有重要的率，但小面积的预测仍然不足探索，这限制了包括纳米比亚在内的低和中间的潜力，以应用基于证据的解决方案。小区域预测的主要数据源包括人口和住房普查，调查和行政数据。尽管这些人提供了人口的全部计数以及有关生育，死亡率和迁移的数据，但通常仅每十年进行一次这些工作。国家统计局通常会产生投影量产生企业间估计；但是，这些是在非常粗糙的空间尺度上完成的。因此，在更细长的地理量表，尤其是通过迁移时，捕获人口趋势和模式变得具有挑战性。卫星的映射可以捕获地理上不平坦的人口增长，通常很难预测和使用标准投影模型来预测和解释（Wardrop等，2018）。就像一样，手机数据为捕获随时间和空间移民模式的季节性变化提供了机会。我提出的一项研究旨在评估地理空间指标如何改善人口投影模型的核能。该研究将探索2011年Census 2011的不同投影方法，并使用自上而下的模型将其分解为小区域，结果将与2021年的人口普查数据相比，以确定最佳估计。