Replication across space and time must be weak in the social and environmental sciences
Replication across space and time must be weak in the social and environmental sciences
复制标题
DOI：
10.1073/pnas.2015759118
复制
发表时间：
2021-08-25
影响因子：
11.1
通讯作者：
Li, Wenwen
中科院分区：
文献类型：
--
作者：
Goodchild, Michael F.;Li, Wenwen
可复制性在研究深埋于空间和时间中的现象，包括分布在地球表面和近地表的现象时具有特殊的意义。两个原则，空间依赖性和空间异质性，通常是这种现象的特点。在处理空间异质性方面，出现了各种做法，包括使用基于地点的模型。我们回顾了人工智能在空间和时间分布的现象中的快速新兴应用，并推测如何解决空间异质性的原则。我们引入了弱可复制性的概念，并讨论了可能的方法来衡量。
Replicability takes on special meaning when researching phenomena that are embedded in space and time, including phenomena distributed on the surface and near surface of the Earth. Two principles, spatial dependence and spatial heterogeneity, are generally characteristic of such phenomena. Various practices have evolved in dealing with spatial heterogeneity, including the use of place-based models. We review the rapidly emerging applications of artificial intelligence to phenomena distributed in space and time and speculate on how the principle of spatial heterogeneity might be addressed. We introduce a concept of weak replicability and discuss possible approaches to its measurement.