Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know

By Alan M. MacEachren, Anthony C. Robinson, S. Hopper, Steven Gardner, Robert Murray, Mark Gahegan, Elizabeth G. Hetzler. 2005

612 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Epidemiology, Geography, Planning and Development

View Paper: https://www.semanticscholar.org/paper/53a19efce568a86608a476a9a0a33462d94fcbab

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Citations in the corpus

Guidance for representing uncertainty on global temperature change maps (2016, background): “, 2009) and that including uncertainty on maps may be helpful to decision makers (MacEachren et al., 2005).”
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets (2015, methodology): “The GIScience community has been actively researching methods to visualise uncertainty in geospatial data [29]; as we become capable of measuring bias too, similar lines of research open up, which may lead to the development of new and alternative approaches to map-data visualisation, and of the eff”
Research directions in data wrangling: Visualizations and transformations for usable and credible data (2011, neutral): “MacEachren et al.(23) report a review of models of information uncertainty with the goal of informing visualizations for geospatial information analysis.”
Uncertainty modeling and visualization for tsunami hazard and risk mapping: a case study for the 2011 Tohoku earthquake (2016, background): “tsunami inundation map), visual parameter cues, such as color, texture, transparency, clarity, and size, can be varied in a cartographic graphical system (MacEachren et al. 2005; Pang 2008; Kunz et al. 2011).”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, neutral): “[160], moreover, review approaches for geospatial uncertainty visualization.”
How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates (2010, background): “Unfortunately, our ability to present error and uncertainty spatially has not kept pace (Gahegan and Ehlers 2000, MacEachren et al. 2005).”
Space, time and visual analytics (2010, methodology): “…methods must be tuned to the uncertainty in the data and visual representations have to convey inherently different aspects of uncertainty (MacEachren et al. 2005): • Accuracy/error – difference between observation and reality, • Precision – exactness of measurement/estimate, •…”
The Role of Uncertainty, Awareness, and Trust in Visual Analytics (2016, background): “We deﬁne trust on the human side as a counter part to the machine’s uncertainties (similar to MacEachran et al.’s [54] distinction between human and machine uncertainties).”