The journal article referenced in the NY times piece is at http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803 and is worth a read. Lots more pretty pictures. Also some intriguing stuff about how they built this map. Basically, it's from readers' clickstreams: this map is actually saying "these are the subjects that readers tend to look at during a single session in front of the computer". An interesting take on how academic disciplines are inter-related.
Also there's a note at the bottom of the article saying that the authors will make their data set available to anyone who wants it. So which of you visualizers is up for a weekend project? ;-) Robert On Tue, Mar 17, 2009 at 11:59 PM, Steve Smith <[email protected]> wrote: > > Map of knowledge at > http://www.nytimes.com/2009/03/16/science/16visuals.html?_r=2&emc=eta1 built > by scientists from LANL, SFI etc. > I must admit, I have a hard time working out what these network > visualizations are meant to be telling me. That academic disciplines are > connected? Did I *really* not know that before looking at the pretty > picture? > > You are not alone in this observation... but I, for one, do get a lot out > of it, and can imagine getting a lot more if I had direct access to the data > (and tools not unlike the one used to create this "map"). > > http://www.nytimes.com/imagepages/2009/03/13/science/16visual-popup.html > is the entire map, not a cropped subset. > > 1. Let me start with the disclaimer that I was in no way involved in > this work. There are others on the list who are at least close to this > work > if not directly involved. I hope they will chime in... > 2. I don't think the goal of the project was to create this particular > visual. This particular visual (the whole one, not the cropped one in the > article) is surely used mostly "iconically" to give the layman a sense of > what the work is about. Imagine if no such visual were included in the > article... even more opaque I think. A list of how many articles and the > major (conventional classifications they were in) and the number of links > between the classifications seems like about as far as you could go, > especially for a lay publication. > 3. To whatever extent the researchers use visualizations like this for > Analysis, they probably use many... with different thresholding criteria, > different subsets, etc. I myself, prefer a completely dynamic, > interactive > network layout for analysis. In fact, I prefer one embedded in a 3D > environment which I can explore more directly. > 4. In my work in SciViz, InfoViz, and Visual Analytics, I would claim > that virtually none of the visualizations my colleagues use for doing > analysis would be immediately useful to the casual observer. Those which > are not particularly abstract (fluid flows) or very familiar (conventional > charts and graphs) might be recognizable, but not necessarily useful. How > many people would know to look for or recognize a "bowtie" in a > computational mesh? How many would see that the adaptive meshing technique > was failing in a region of high change? Etc. Even simple charts and > graphs > intended for analytical use are opaque to the layman. So, I can tell that > the concentration of a particular ion goes up roughly exponentially with > one > factor and more linearly with another... so what? > 5. Even Geography/Cartography can elicit a "so what"? There are big > deserts in along parts of the equator, rain forests along other parts, I > bet > it is hot there. Mountains seem to come in long skinny ranges or big > clumps. Coastlines are ragged. The names of countries in South America > seem to be Spanish. There are a lot of countries in Europe I never thought > about because they were formerly lumped in with the Soviet Union. Didn't I > know all those things before I looked at a world map with geopolitical > features marked? Actually, I probably learned them from maps I have seen > all my life. > 6. In my experience, especially with Visual Analytics, the goal is > Exploration, Discovery and then maybe, sometimes Analysis. Exploration and > Discovery are a lot more "fun" even if the real work is in the Analysis. > 7. Network Science is not new, but it has only been about 10 years that > it has become highly popular and widely used. The visual (and linguistic) > idioms are still somewhat young and we haven't all learned to read/think > with them. > > Going to the actual network diagram... > > http://www.nytimes.com/imagepages/2009/03/13/science/16visual-popup.html > > Without knowing the key to the node size and colors... I can intuit, or > extract some interesting (to me) things. > > 1. There are a few large clusters of relatively tightly coupled > subjects which are relatively distinct from eachother. > 1. Soft Sciences, Religion, etc. > 2. Biology, Environmental Science, Ecology, Agriculture > 3. Hard Sciences, Physics, Chemistry, etc. > 4. Health Sciences > 2. The biggest "wad" are what some of us would call the "soft > sciences". It might not surprise some of us to notice that Law, and > Education and Philosophy are fairly entertwined. It *might* surprise some > of us that statistics is so connected. > 3. there is another "big wad that we might generally refer to as the > hard sciences. > 4. It might surprise some of us that Biology seems to be somewhat > distinct from the other sciences, connected through biochemistry, > toxicology > and biotechnology. > 5. It might inform, if not surprise some of us to realize that > Psychology might be tied to Biochemistry and that Biology ties to > Architecture and Design through Biodiversity and Ecology. > 6. It surprises me that the wad on the left in Red which roughly seems > to relate to Medicine in general, doesn't tie in with Physiology and > Genetics from within the Biology Cluster. > 7. Does it surprise us that Statistics is tied to Medicine through > Demographics and then through Clinical Trials? > > It may be my experience and normal role, but an important thing I think I > see in this visualization is that either the data or the tuning of the > parameters might have artifacts. This Visualization was probably not tuned > for Analysis, or if it was, it was tuned for one aspect of the data. It was > probably tuned to make a pretty picture so folks who know nothing about what > they are doing, would at least be able to see the rough structure and > symmetry. No criticism of their work here. > > 1. Why is Pharmaceutical research disconnected from Clinical > Pharmacology? > 2. Cognitive Science and Neurology? > 3. Where is Engineering? > 4. Why is Tourism there? > 5. What else is missing, obscured, or that I'm not noticing? > > I immediately want to do several things: > > 1. move this into 3D so there is more "conceptual layout space" and so > I can adjust perspectives to see different otherwise occluded features. > 2. make it dynamic so that I can "pluck" portions of it and watch the > disturbances propagate, adjust parameters and watch it evolve. > 3. play with the parameters to accentuate tight clusters or lightly > connected subsets (this view is good that way). > 4. Select smaller subsets (zoom in on details). > 5. Interrogate specific nodes for their details. > 6. Manually aggregate what my visual judgement suggests are "clusters", > building a hypergraph. > > > > And all this without really knowing what the data is and what they are > really trying to show here. The more I look at it, the more I get out of it > (and the more questions I have). Does anyone else have this experience? Or > is everyone else equally puzzled by this kind of "map"? > > - Steve > PS. Yes, Doug, I am avoiding a deadline, why else would I dive in so deep > on this! > > ============================================================ > FRIAM Applied Complexity Group listserv > Meets Fridays 9a-11:30 at cafe at St. John's College > lectures, archives, unsubscribe, maps at http://www.friam.org >
============================================================ FRIAM Applied Complexity Group listserv Meets Fridays 9a-11:30 at cafe at St. John's College lectures, archives, unsubscribe, maps at http://www.friam.org
