Posts Tagged ‘maceachren’

Visualizing Uncertainty: mis-addressed?

Wednesday, April 3rd, 2013

“Visualizing Geospatial Information Uncertainty…” by  MacEachren et al. presents a good overall view of geospatial information uncertainty and how to visualize it. However that said, many parts seemed to convey that all uncertainty must be defined in order to make correct decisions. In my realm of study, although it would be nice to eliminate or place uncertainty in a category, just the recognition that there is uncertainty is often definition enough to make informed decisions based on the observed trends. Furthermore, the authors seem to separate the different aspects of the environment or factors that lead to uncertainty and how it may be visualized. The use of many definitions and descriptions convolutes what is really the factors that result in uncertainty and the resulting issues with visualization. The way visualized uncertainty is presented greatly contrasts the ambiguity of the definitions behind uncertainty and its representation presented by the authors. The studies and ways uncertainty can be visualized is a great help in decision making and the recognition of further uncertainties.

One aspect that would have help in addressing uncertainty and its visualization would have been to integrate ideas and knowledge from the new emerging field of ecological stoichiometry, which looks at uncertainty, the flow of nutrients and energy, and the balance within ecosystems to answer and depict uncertainty. I believe that ecological stoichiometry would address many of the challenges in identification, representation and translation of uncertainty within GIS and help to clarify many problems. This stoichiometric approach falls along the scheme of the multi-disciplinary approach to uncertainty visualization described within the article.  However, as the article is limited to more generally understood approaches, rather than more complex ones, such as stoichiometry, do some of the proposed challenges in recognition and visualization of uncertainty not exist?  I would argue yes, but then again more challenges may arise in depiction, understanding and translation of uncertainty.


Realized geovisualization goals

Thursday, January 31st, 2013

MacEachren and Kraak authored this article in 2000, a year before the release of Keyhole Earthview and five years before Google Earth. In the piece, the authors show the results of collaborations of teams of cartographers and their decisions on the next steps in geovisualization. They mention broad challenges pertaining to data storage, group-enabled technology, and human-based geovisualization. The aims are fairly clear, but there are very few, if any, actual solutions proposed by the authors.

While reading the article, I had to repeatedly remind myself that it was written a dozen years ago, when technologies were a bit more limited. Most notably, there appears to be a very clear top-bottom approach in the thinking here, very reminiscent of Web 1.0, where information was created by a specialized provider and consumed by the user. In the years since this piece was written, Web 2.0—stressing a sharing, collaborative, dynamic, and much more user-friendly paradigm—has largely eclipsed the Web as we understood it at the turn of the millennium. In turn, many of the challenges noted by MacEachren and Kraak have been addressed in various ways. For one, cloud storage and cheaper physical consumer storage have in large part solved the data storage issue. Additionally, Google has taken the driver’s seat in developing an integrated system of database creation and dynamic mapping, with Fusion Tables and KMLs, that are both extremely user-friendly. And there are constantly applications and programs being created and launched that enable group mapping and decision support. MacEachren and Kraak did not offer concrete solutions, but the information technology community certainly has.

– JMonterey

So many challenges, so many opportunities

Friday, February 10th, 2012

MacEachren and Kraak address the notion of visualizing the world and what this exactly entails. The article was written over a decade ago and is still as relevant today as it was then, and centuries ago. “…80 percent of all digital data generated today include geospatial referencing” (1). A powerful sentence that altered my perspective on geographic visualization (geoviz), when I first read this article a few years ago. There is so much to explore, to reveal; the sky is the limit.  Geoviz is about transformations and dichotomies; the unknown versus known, public versus private, and high versus low-map interaction (MacEachren, 1994). It aims to determine how data can be translated into information that can further be transformed into knowledge. MacEachren and Kraak provide a critical perspective into the world of geoviz and its vexing problems. They do a good job in convincing us that a map is more than a map. Maps have evolved by means that “maps [are] no longer conceived of a simply graphic representations of geographic space, but as dynamic portals to interconnected, distributed, geospatial data resources” (3). “Maps and graphics…do more than ‘make data visible’, they are active instruments in the users’ thinking process” (3).

Out of the many challenges that we still face (also by Elwood) there are some that have been tackled successfully. The one I will focus on is ‘interfaces’ in relation to digital earths. Arguably, I believe that no one would have imagined the progress made with digital earths, especially Google Earth (GE) back in 2001. GE remains untouchable in its user-friendly display, mash-ups are through the help of Volunteered Geographic Information(VGI), including programmers who are contributing free software, interoperable with GE (GE Graph, Sgrillo). However, the abstract versus realism issue is relevant as ever. The quality and accuracy of the data may be low yet the information visualized will look pristine, and vibrant, thus deceive the user to believe otherwise. How do we then address levels of accuracy? Abstraction? Realism? Thus, we have challenges but we also have progress. MacEachren and Kraak’s article refocuses our attention on the pertinent obstacles that we should be mindful when exploring, discovering, creating or communicating geoviz. To move away from the “one tool fits all mentality” (8). To unleash the creativity from within.

MacEachren’s simple yet powerful geovisualization cube.


-henry miller

Cognition’s Role in Geovisulation Research Programs

Thursday, February 9th, 2012

In their article outlining the research challenges faced by the field of Geovisualization, Alan MacEachren and Menno-Jan Kraak pose the problem of cognition as a direct relationship between how external, dynamic visual representations can “serve as prompts for creation and use of mental representations” (7). They note that the existing lack of paradigms for how to conduct research into the cognitive processes at work in geovisualization projects or into their usability as a major problem in this field. However, I wonder if this doesn’t put the cart before the horse.

Much of the existing research into geospatial cognition seeks to understand how the human mind works in processing spatial data, particularly how such data is acquired, processed and translated into knowledge. Before we can hope to create user interfaces utilizing geovisualization techniques, shouldn’t we follow this approach and attempt to understand how these digital interfaces might impact cognition of spatial data? The authors set out the goals of establishing a cognitive theory that supports and assess the usability of “methods for geovisualization” and those that take advantage of dynamic, animated displays (7). Yet this feels like we are trying to support the cognition of a new field without trying to understand how it actually impacts cognition.

The danger of such an approach is that we are simply writing theory to support pre-articulated goals. Shouldn’t we instead start from a blank slate and then ask what types of cognitive impacts geovisualization might have for how the public processes geospatial data? For example, one researcher into geospatial cognition found that people who learn geographic data from maps as opposed to experiential data (as in navigating an environment) often had better recall of the data and more accurate perceptions of spatial relationships. Shouldn’t we try to first figure out how cognition of geovisualized data fits into this paradigm before just drafting a research agenda for it?