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.

预计非洲人口在未来 40 年将增加一倍，从而促进城市化率的提高，从而引发环境、社会经济和健康变化。地理区域的小细节对于规划和跟上这些变化至关重要。尽管小区域预测很重要，但仍未得到充分探索，这限制了包括纳米比亚在内的中低国家应用基于证据的解决方案的潜力。小区域预测的主要数据来源包括人口和住房普查、调查和行政数据。尽管人口普查提供了完整的人口统计以及生育率、死亡率和移民数据，但这些操作通常每十年进行一次。国家统计局通常会进行预测以得出普查间的估计值；然而，这些都是在非常粗糙的空间尺度上完成的。因此，在更精细的地理尺度上捕捉人口趋势和模式变得具有挑战性，特别是通过移民。卫星测绘可以捕捉地理上不均匀的人口增长，而这通常很难使用标准投影模型进行预测和解释（Wardrop 等人，2018）。同样，移动电话数据提供了捕捉随时间和空间迁移模式变化的季节性变化的机会。我提出一项研究，旨在评估地理空间指标如何提高人口预测模型的准确性。该研究将以 2011 年人口普查为基线，探索不同的预测方法，并使用自上而下的模型分解为小区域，结果将根据 2021 年人口普查数据进行验证，以确定最佳估计。