Abstract
Uncertainty is an unavoidable part of any spatial analysis, which makes quantifying and communicating uncertainty a requirement of any spatial data science study. However, current curricula leave students understanding the importance of uncertainty and concerned about potential bias, but without an actionable framework to improve their workflows or inferences. We propose a framework rooted in Bloom’s Taxonomy for introducing these concepts to spatial data science students.
Document Type
Paper
Start Date
15-10-2024 1:50 PM
End Date
15-10-2024 2:50 PM
DOI
10.5703/1288284317799
Recommended Citation
Kedron, Peter and Chen, Jiahua, "Communicating Uncertainty and Cataloging Bias in Spatial Data Science Education" (2024). I-GUIDE Forum. 3.
https://docs.lib.purdue.edu/iguide/2024/presentations/3
Included in
Curriculum and Instruction Commons, Geographic Information Sciences Commons, Higher Education Commons, Spatial Science Commons
Communicating Uncertainty and Cataloging Bias in Spatial Data Science Education
Uncertainty is an unavoidable part of any spatial analysis, which makes quantifying and communicating uncertainty a requirement of any spatial data science study. However, current curricula leave students understanding the importance of uncertainty and concerned about potential bias, but without an actionable framework to improve their workflows or inferences. We propose a framework rooted in Bloom’s Taxonomy for introducing these concepts to spatial data science students.