Test and develop high resolution mapping and modelling methods to support inter-censal population estimates

测试和开发高分辨率绘图和建模方法以支持普查间人口估计

• 批准号: 2891457
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891457
• 关键词: Test; develop; resolution; mapping; modelling

Sierra Leone has recently concluded its georeferenced mid-term census that collected up-to-date, accurate andcomplete demographic information on its residents. This provides a unique opportunity to compare and validatedifferent types of small area population estimation models and therefore inform approaches to producingintercensal estimates going forward. By utilizing the full georeferenced mid-term census results, the proposedresearch will:(i) Test various population estimation methods using sub-samples from the mid-term census:a. test the performance of different sample design strategies in 'bottom-up' model applications (e.g., stratification,weighted, etc.)b. quantify the performance of such 'bottom-up' estimates against the mid-term census when using routinelycollected surveys as inputsc. develop and test a wide range of geospatial data (i.e., covariates) and identify the best suited for populationestimation. This will include the exploration of machine learning options to model building usage (residential/nonresidential,and other characteristics) using high-resolution satellite imagery, building footprint data and labels anddata from various surveys.d. identify the best method to estimate age/sex structures at high resolution,(ii) test different types of sub-national projection methods from the last census to examine which work mostaccurately and what ancillary datasets are most valuable, and(iii) compare various top-down disaggregation of population projections with the geo-referenced census data.

塞拉利昂（Sierra Leone）最近结束了其地理参与的中期人口普查，该人口普查收集了有关其居民的最新，准确且完整的人口统计信息。这提供了一个独特的机会，可以比较和验证的小面积人口估计模型类型，因此为未来的生产估计方法提供了信息。通过利用完整的地理参考的中期普查结果，提出的研究将：（i）使用中期普查中的子样本测试各种人群估计方法：a。测试“自下而上”模型应用中不同样本设计策略的性能（例如，分层，加权等）b。当使用常规收费调查作为InputSC时，对中期普查的此类“自下而上”估计的性能量化。开发和测试广泛的地理空间数据（即协变量），并确定最适合种群的地理空间数据。这将包括使用高分辨率卫星图像，从各种调查中构建足迹数据和标签和data的机器学习选项（住宅/非住宅/非住宅和其他特征）建模。确定高分辨率估计年龄/性别结构的最佳方法，（ii）测试来自上一次人口普查的不同类型的亚国家投射方法，以检查哪些最有价值的工作以及哪些辅助数据集最有价值，并且（iii）将各种自上而下的人口投影分解与地理参考的人口普查数据进行比较。