ArticleProceedings of the National Academy of Sciences of the United States of America2021
Replication across space and time must be weak in the social and environmental sciences.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Predicting human mobility flows in cities using deep learning on satellite imagery.Nature communications · 2025Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Artificial intelligence in urban science: why does it matter?Annals of GIS · 2025Article
- The Imperative for Hazard- and Place-Specific Assessment of Heat Vulnerability.Environmental health perspectives · 2025Article
- MetaCity: Data-driven sustainable development of complex cities.Innovation (Cambridge (Mass.)) · 2025Review
- Deep learning classification of drainage crossings based on high-resolution DEM-derived geomorphological information.Frontiers in artificial intelligence · 2025Article
- Predicting Monthly Community-Level Radon Concentrations with Spatial Random Forest in the Northeastern and Midwestern United States.Environmental science & technology · 2023Article
- Harnessing heterogeneity in space with statistically guided meta-learning.Knowledge and information systems · 2023Article
- Accelerating ethics, empathy, and equity in geographic information science.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.