Senior Global Futures Scientist Wenwen Li is co-author on a new publication shedding light on the challenges and opportunities the scientific community faces in replicating place-based research.
Across the scientific community, the repeated testing of studies has always been central to progress. Reproducing and replicating research not only validates prior findings, but it also validates research methods and data that could then be applied to solve other elusive problems and accelerate future research.
But compared with the scientific fields of physics, chemistry and biology, dialogue around the reproducibility and replicability of research in the social and environmental sciences, like geography, has been largely absent and focused on computation challenges.
In a recent perspective paper published in the Proceedings of the National Academy of Sciences of the United States of America, Li and lead author Michael Goodchild conduct a novel analysis, shedding new light on the challenges and opportunities the scientific community faces in replicating place-based research.
Read more in ASU News. The abstract follows.
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.ASU research reveals 'weak' replicability of place-based research
Senior Global Futures Scientist Wenwen Li is co-author on a new publication shedding light on the challenges and opportunities the scientific community faces in replicating place-based research.
Across the scientific community, the repeated testing of studies has always been central to progress. Reproducing and replicating research not only validates prior findings, but it also validates research methods and data that could then be applied to solve other elusive problems and accelerate future research.
But compared with the scientific fields of physics, chemistry and biology, dialogue around the reproducibility and replicability of research in the social and environmental sciences, like geography, has been largely absent and focused on computation challenges.
In a recent perspective paper published in the Proceedings of the National Academy of Sciences of the United States of America, Li and lead author Michael Goodchild conduct a novel analysis, shedding new light on the challenges and opportunities the scientific community faces in replicating place-based research.
Read more in ASU News. The abstract follows.
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.