.png){kind=icon}
返回
Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model
.png){kind=icon}
OA
AI
DOI：10.1016/j.apgeog.2024.103416.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.
Keyword:
Population modelling
Bayesian dasymetric population
Ghana
Population disaggregation
Bayesian Additive Regression Tree
Random forest
WorldPop
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.4
论文数:
4.0K
被引数:
1.2W
机构
引用论文
Estimating vaccine coverage in conflict settings using geospatial methods: a case study in Borno state, Nigeria使用地理空间方法估算冲突环境中的疫苗覆盖率: 尼日利亚博尔诺州的案例研究.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏分享收藏
Phase 2 Study of Bevacizumab and Temsirolimus After VEGFR TKI in Metastatic Renal Cell CarcinomaOAAI
OAAI分享收藏
查看更多内容