No matter how good technology becomes, we will always face challenges in data uncertainty and error; the question is, can we develop appropriate techniques to mitigate the effects of these noises, and come away with the correct signal. As MacEachren et al. (2005) point out in their article titled “Visualizing Geospatial Information Uncertainty”, we use this information to base decisions off of, and the uncertainty is inherent in the data and must be taken into account.
There are multiple dimensions of uncertainty, as the authors point out, ranging from credibility of a source to precision of a physical variable, and these will compound, effecting the amount of correctness the end result will have. They function across many scales, including the direct attribute of the information, the specific context or location of the information (which may not be what you want to apply the information to), as well as temporally. It all seems very complicated when examined through this framework… but it is important to take these into account in order to have confidence in your product.
Personally, i have experienced a lot of uncertainty while trying to create a global map of administrative subdivisions. Every County collects data at different resolutions and time, however these countries are supposed to be contiguous as we well know. The borders do not always align, but who is right? Furthermore, this issue is compounded when you consider the global land mass as a whole. We want to have an accurate total area of land surface, however if you trust each country to represent their land correctly and then end up with an incorrect total, who is wrong? Where do you remove land? Where do you add it? These are some of the challenges I have faced with uncertainty, and I was not qualified to make the adequate decision.
What I didn’t do at the time was try to quantify and visualize the uncertainty, which as the authors say, is crucial to making sure the data is useable, and that you are confident it is correct for answering the questions you are trying to answer.
Pointy McPolygon