Author Climbing in the Queyras, Summer 2013

Wednesday, December 15, 2010

Getting into Shape:
What Do Butterfly Wings and Renaissance Maps Have in Common?

A Note on Method
There are spaces in which the determination of position requires not a finite number, but either an endless series or a continuous manifold of determinations of quantity. Such manifolds are, for example, the possible determinations of a function for a given region, the possible shapes of a figure, and so on.
--Bernhard Riemann

The comparison of the accuracy and geometric outline of an early map with a modern map is really just the calculation of the change in shape of two planar surfaces. It is a coordinate transformation that can be as complex or as simple as one likes. My interest in the mathematics of shape change came about while I was working in the Entomology Department of the Smithsonian Institution’s National Museum of Natural History.

I was first specifically interested in trying to quantify the variation in the shape of butterfly wing patterns. At first, my interest was centered on the genus Erebia from the Alps of France. Erebia are high mountain butterflies that speciated a great deal during the last large scale glaciations that drove them out of the valleys and isolated them on peaks throughout the Alps. The amazing complexity of their spatial distribution is not well understood. Some of the variation between species can be quanitifed by looking at the shape variation in their eyespots and the surrounding color bands on both pairs of wings.

Later, I worked, with Marc Epstein at the Smithsonian, on a group of moths where I became interested in one species known as Euclea Delphini. Within this species we find a great deal of variation in the wing patterns. In the case of Delphini there are five different forms that are not totally discrete but tend to make-up a pseudo-continuum. In order to study this with some meaningful statistics it is necessary to describe the shape of the features that are varying mathematically. These features take a variety of forms from lines and polygons through more complex curved concave and convex shapes.

There are no natural measures associated with the two-dimensional variation of these features in the wings of butterflies so what to measure becomes a problem. Nijhout’s(1) work in this area was very helpful in trying to define what the basic elements of wing patterns were and in helping to narrow down what parts of the patterns are varying in a meaningful way. ( I began experimenting with some simple morphometric models and was naturally led to the work done on shape spaces by David Kendall and Fred Bookstein. The studies of shape spaces that they pioneered are very interesting and are themselves objects of mathematical research. It is not always useful to think of shapes as collections of points in Euclidean space. Shape is something that has a spatial structure that is quite peculiar and generally these spaces and subspaces do not occur in other contexts. Huiling Le and Kendall(2) wrote a beautiful paper on this entitled, “The Riemannian Structure of Euclidean Shape Spaces: A Novel Environment for Statistics” that shows that these spaces have a local Riemannian structure. I immediately became fascinated with these spaces and read more deeply than the application to the problem of butterfly wings suggested. The structure of these spaces is complex and unfortunately I still do not think that I have a good grasp of all of Kendall’s work. The key point here is that there exists a well defined mathematical machinery that lets us apply non-linear transformations to these spaces while maintaining some meaningful definitions of physical distance.

Wing Patterns or Coastlines?

Modeling the shape change on a modern and ancient map is not all that different from the butterfly wing problem, but it does vary in a number of ways. For example, when working with two maps, we do not have a large sample of objects that we are comparing. We are not looking for clusters or variations that seem to group themselves together in statistically meaningful ways. We really are comparing two discrete objects with the hope of quantifying an ill-defined concept that we call accuracy. The question of accuracy in historical cartometric studies is, at least according to the J.B Harley (3) , one of the least understood problems in the history of cartography and it is especially difficult to quantify.

Fortunately morphometrics does have a great deal to tell us about how to begin to study this problem of comparative accuracy. For instance in both cases we are still operating on sets of homologies. Homologies are points, locations or landmarks that are the same on the objects of interest. They could be the location of a particular structure on a butterfly’s wing or the location of Rome on two maps from different historical periods. In historical cartometry we select a set of these points and then transform one set of points into the other using any number of possible algebraic or differential transformations. We are performing a type of image warping which maps all the selected positions in one image plane (the modern map) to the positions in a second plane (the ancient map). The choice of the mathematical function that actually performs the “warp” is always a compromise between insisting that the distortion is smooth and achieving a good match between the two sets of points. There are and have been many approaches to this.

One of the earliest applications of this type of coordinate transform or “warp” to maps came from Waldo Tobler (4) who experimented with them back in the 1970’s. Any discussion of the applications of complex transformations to cartography must begin with his seminal paper and computer program, “Bi-dimensional Regression”. Bi-dimensional Regression was a statistical regression technique that allowed inferences to be made between two planes from point distributions. Tobler studied medieval maps and Portolan charts but was also interested in general questions of shape and was heavily influenced by D’Arcy Thompson’s early studies on deformations found in his book On Growth and Form. Although Tobler’s work was graphically somewhat primitive, the numerical results from his regressions gave the first look at what types of information it might be possible to add to historical studies of early maps through the use analytical comparisons. Tobler’s methods were formalized to shape spaces by Small in several important papers such as “Techniques of Shape Analysis on Sets of Points. (5)

A weakness in Tobler’s methods was the question of error distribution (6) and what have become known as outliers. Outliers are points formed from pairs of homologies that when transformed produce an error that is significantly larger than the other points in the sample. These points if not identified and somehow accounted for in the calculations can destabilize many transformations and can effect correlations and regressions in adverse ways. Hampel and Huber (7) have developed an entire field of study around this problem that has become known as Robust Estimation. The family of estimators that they developed and that are known as M-estimators have been widely used and have even found their way into specialty software programs used in historical cartometry such as MAPANALYST. Intuitively, these estimators allow the control of how much influence on the regressions distant points have on nearby points. One can imagine this as an application of Tobler’s first law of geography, “everything is related to everything else, but near things are more related than distant things”.

My current research derives from the work of Bookstein (8) and his groundbreaking paper, “ Principal Warps: Thin-Plate Splines and the Decomposition of Deformations” (see link section to view this paper). Bookstein uses a particular distance function that defines a convex surface between homologies and that is a solution to the biharmonic equation.
Through some very interesting algebra Bookstein derives functions that effectively separate the global and local error into affine and nonlinear components. The functions are vector valued and if the pairing of the points that are being transformed correspond to the homologies on our two maps we have effectively modeled the difference between the two point sets as a deformation.

At the foundation of Bookstein's method is the function shown above which corresponds to a portion of the surface,

where r is the distance from the origin. The U(r) function satisfies,

and is the solution to the so-called biharmonic equation. We can imagine this surface as a piece of metal that is subject to deformations resulting from the displacements of fixed points on a reference surface. In this case we are comparing points on a modern map and the same corresponding points on an older or different map of the same region. For a thin plate of this type subject to bending, the energy change at any point minimizes,


If we pick a group of points on the reference map we can place them on the spline grid as shown below.

The corresponding points on the map to be studied can then be transformed yielding a deformation grid that minimizes the energy necessary for the deformation. This deformation has both global and local components that allow us to look at the induced error from different scales and is equivalent to the well known measure of cartographic error, Tissot’s indicatrix. The information from these deformations can be used to generate scale isolines as below or vector displacements resembling the indicatrix.

Thus far I have utilized this thin-plate spline method on many maps and in a study that compares the longitudinal error in the Mediterranean basin on the 1507 and 1516 World Maps by Martin Waldseemüller. The method does have significant advantages over methods such as Polynomial Warping that I used in my study, “Warping Waldseemüller: A Phenomenological and Computational Study of the 1507 World Map” (9).

I have since utilized these methods on Roman and some Medieval cartography, like Portolan charts. Using these methods one can calculate distortion grids and scale and rotation isolines.

For the historian of cartography comparing the accuracy on a modern and early map has a number of formal and logical difficulties that must be considered before the application of any cartometric process.

1. The identification of tie points on the two maps to be compared is sometimes difficult and once selected are seldom distributed evenly across the surface of the two images. The problem is essentially one of homology. Finding homologous points on two maps may seem trivial but differences in landmark or coastline shape, an incorrect assignment of place names and changing scales require insight into the target image. The selection of points is especially important when using the simplest linear models without M-estimators where results can be error distribution sensitive.

2. The accuracy and error to be tested and compared may not be a relevant concept across the whole surface of the map. Accuracy is seldom evenly distributed especially in the case of maps of small scale that may be composed from a variety of sources. Discontinuities in error from scale changes are not only scalar but are vector quantities whose direction is important to determine. Rapid changes in accuracy across map surfaces may be difficult to handle with simple linear models and are better approached using local non-linear radial basis functions whose correlations may be statistically more relevant, but mathematically more complex to program. Decisions regarding the use of local or global methods cannot usually be made ahead of time and require experimentation in order to characterize the accuracy and decide on a methodology.

3. The substrate that the map is draw or printed on may have undergone distortion through shrinking, folding, or stretching. Distortion of this sort is especially important to consider in the case of environmentally sensitive materials such as vellum. The distortion of the medium not only effects the accuracy of statistical transformations that we are trying to perform but can also obscure the intent of the cartographer.

4. Spatial association does not necessarily imply causality. The warps and correlations that we calculate using cartometric techniques give real mathematical results but these may be extremely difficult to link to any historical meaning, event or cause. Care must be taken not to over interpret the results of these calculations and not to override historical and documentary context in the rush towards accuracy measures.

5. Historical cartometry is a problem in equifinality or process convergence. Similar types of distortion may arise from different causes making it difficult to derive exact causes for particular distortion patterns.

6. Experimentation is necessary and we must be prepared to use a variety of techniques to characterize the accuracy on a single map.

Considering the qualifications in the application of cartometric methods to historical materials it is obvious that we cannot hope to achieve exact “truth” using our methods or an absolute visualization and characterization of accuracy(10). Instead we assign meaningful notions of probability and statistical measures that are useful for historical comparisons. The analysis of accuracy is an important part of the historical characterization of early maps but the assignment of the adjectives, “accurate” or “inaccurate”, needs to be made more precise in our discourse. The addition of statements like “to this level of confidence by this method” to our use of these adjectives would go a long way to making the results of our calculations repeatable by others engaged in the same research. This more analytic understanding of accuracy requires a much more careful linguistic and conceptual use of these ideas than has been the norm in the historical literature.
Currently there are more algorithms being developed that will be useful for cartographic historians as the fields of medical imaging and shape analysis continue to provide fertile ground for the growth these mathematical techniques. There are many more ways to represent shapes on manifolds and to perform the types of similarity and coordinate transforms than we have discussed here and no doubt more applications shall be forthcoming. The areas of Stochastic Geometry(11) and Poisson Processes in Euclidean space are especially interesting (12).
In the end the researcher who would employ these methods needs to critically consider each map to be studied on a case-by-case basis and would be wise to consider the words of R.A. Skelton, which although written in a different context, can be used as model for the cautions we must recognize in historical cartometry.
“The content of the map, as a whole, cannot be assigned confidently to a single phase or horizon of geographical knowledge. Its outlines are in part transcribed from a map prototype or prototypes not precisely identifiable with any extant work; in part they illustrate texts, not all of which have come down to us. The information taken by the author of the map from these sources (graphical and textual) relates to events and concepts of various periods; most of it older by a least a century, and some of it much more, than the presumed date at which the existing map… was made. The delineations in the map before us are separated by long intervals of time not only from the original experience that they reflect, but also from the direct records of it. For the mapmaker was working always at one remove, sometimes (we cannot doubt) at two or more removes, from firsthand records; and it is evident that, to a degree and in senses which it is difficult for us to divine, he exercised his judgment in selection from and in adaptation of his sources, which are themselves partly unknown to us. (13)
(1)H.F. Nijhout, “Elements of Butterfly Wing Patterns,” Journal of Experimental Zoology 29 (2001): 213-225.
(2)Huiling Le and David Kendall, “The Riemann Structure of Euclidean Shape Spaces: A Novel Environment for Statistics,” Annals of Statistics 21 (1993): 1225-1271

(3) Brian Harley, “Concepts in the History of Cartography: A Review and Perspective,” Cartographica 17 (1980): 54.
(4)Waldo Tobler’s papers on the development, theory and applications of bi-dimensional regression include, “Computation of the Correspondence of Geographical Patterns”, Papers of the Regional Science Association 15 (1965): 131-39; “Medieval Distortions: The Projections of Ancient Maps”, Annals of Association of American Geographers 56 (1966): 351-61; “Bi-dimensional Regression”, reprinted in Geographical Analysis 26 (1994): 187-212.

(5) C.G. Small, “Techniques of Shape Analysis on Sets of Points,” International Statistical Review 56 (1988): 243-257.

(6) Tomoki Nakaya, “Statistical Inferences in Bi-dimensional Regression Models,” Geographical Analysis 29 (1997): 169-185.

(7)P.J. Huber, “Robust Estimation of a Location Parameter,” Annals of Mathematical Statistics 35 (1964): 73-101 and Robust Statistics (New York: John Wiley, 1981) see also F. Hampel, Contributions to the Theory of Robust Estimation, PhD Thesis (Berkeley: University of California, 1968).

(8) Fred Bookstein, “Principal Warps: Thin-Plate Splines and the Decomposition of Deformations”, IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989): 567-585 and Christopher G. Small, The Statistical Theory of Shape (Berlin: Springer-Verlag, 1996) 110
(9)John Hessler, “Warping Waldseemüller: A Phenomenological and Computational Study of the 1507 World Map,” Cartographica 41 (2006): 101-113.

(10) Timothy R. Wallace and Charles van der Hanvel, “Truth and Accountability in Geographic and Historical Visualizations”, Cartographic Journal 42 (2005): 173-181.

(11) A. Braddeley, “Stochastic Geometry: an introduction and reading list,” International Statistical Review 50 (1982): 179-193.
(12) F. Morgan, Geometric Measure Theory: An Introduction (Boston: Academic Press, 1988).
(13)R.A. Skelton et. al., The Vinland Map and the Tartar Relation (New Haven, CT: Yale University Press, 1965) :228.