论文推荐 | 评估城市动态人口制图中的空间统计和机器学习模型-规划问道

导读

本期为大家推荐的内容为论文《Evaluating spatial statistical and machine learning models in urban dynamic population mapping》（评估城市动态人口制图中的空间统计和机器学习模型），发表在Transactions in Urban Data, Science, and Technology期刊，欢迎大家学习与交流。

及时准确掌握精细时空粒度人群动态对开展以人为中心的研究有着重要价值。过去几十年，随着高频人类数字足迹数据可用性的不断提升，精细时空尺度人群动态制图研究得到了飞速发展。然而，此类研究在建模策略和辅助数据选择方面仍缺乏统一标准，尤其缺乏不同协变量条件下新兴机器学习技术与传统空间统计方法的系统比较。本文以北京市百米格网逐小时人群动态制图为例，对比了不同协变量组合下，两种空间统计模型（贝叶斯框架下时空模型与时空地理加权回归模型）与两种机器学习方法（随机森林模型与XGBoost梯度增强算法）的性能。研究发现，融合城市功能协变量的贝叶斯框架下时空模型能够获得最佳估算精度与最低估算偏差。基于该模型，研究构建了城市空间逐小时人群动态分布数据集并进一步挖掘了不同城市功能设施的人群活动模式差异。本文强调了耦合时空关联信息在精细时间尺度人群分布制图中的重要性，以及城市功能协变量在城市空间人群分布制图中的重要价值。

论文相关

题目：Evaluating spatial statistical and machine learning models in urban dynamic population mapping

（评估城市动态人口制图中的空间统计和机器学习模型）

作者：Zhifeng Cheng, Jianghao Wang, Kaixin Zhu, Yong Ge and Chenghu Zhou*

发表刊物：

Transactions in Urban Data, Science, and Technology

DOI:https://doi.org/10.1177/27541231221114169

摘要ABSTRACT

Understanding population dynamics at fine spatiotemporal granularities are valuable to human-centered studies. With the increasing availability of high-frequency human digital footprint data, the past decades have witnessed numerous efforts in mapping populations at fine spatiotemporal scales. However, such research still lacks a unified standard in modeling strategy and auxiliary data selection, especially a systematic comparison between newly developed machine learning techniques and traditional spatial statistical methods under different covariates provisions. Here, we compared two spatial statistical models, the Bayesian space-time model and geographically and temporally weighted regression, with two machine learning techniques, random forest and eXtreme gradient boosting, in a case study of hourly population mapping at 100 m resolution in Beijing. We evaluated the model performance with varied covariates combinations and found that the Bayesian space-time model achieved the best in conjunction with urban function data. Leveraging the optimal model constructed, we mapped dynamic population distribution and concluded human activity patterns on diverse city amenities. This paper emphasizes the importance of spatiotemporal dependency information in fine temporal scale population mapping and the urban function covariates in urban population mapping.