MAPS IN LANDSCAPE INTERPRETATION

ions of potential vegetation and to look more closely at existing vegetation. The most common mapping technique has been some variation on the time-worn digital or fractional code system, wherein broad patterns may be depicted with colors or screens while a printed sequence of numbers or letters is used to communicate key information for local areas on the map (Figure 5). Various cartographers have tried to make their maps easier to read by employing different systems of symbol placement, alternation of capital and lower case letters, mixing of letters and numbers, or use of interrupters such as colons or dashes to separate symbols into logical groups. Each refinement may add detail to the message but at the same time can make the medium more difficult to interpret, both by cluttering each symbol group and by increasing the precision of description and therefore multiplying the number of areal subdivisions on the map. Despite these problems, the widespread use, occasional abandonment, and repeated reintroduction of the method suggests that fractional coding still might be the most satisfactory solution to the problem of mapping present vegetation associations (at least on two-dimensional paper rather than in an n-dimensional computer memory). ' Crop Associations as Dot Mosaics Like soil and vegetation, agriculture usually appears on the landscape as a mosaic with varying degrees of dominance by different crops in different places, gradual transitions between dominant types, and repetitive patterns among associated land uses. At an appropriate scale with reasonably distinguishable symbols, a superimposed set of dot maps appears to be an especially felicitous solution to the problem of depicting a distribution that has two distinct scales of interest: 1 A macro scale — what is the distribution of the producing lands in the area? 2 A micro scale — what are the crops that occur together in a particular district? Some time ago, an experimental map of crops used varicolored dots to address the dual-scale problem: a single glance can reveal the broad pattern of agricultural land, whereas a closer look at individual symbols discloses the mix of crops to be found in a given area. l 8 One could quibble with the choice of colors for the dots (or even with the necessity or desirability of uniform dots as opposed to colored letters or other distinct and easily remembered symbols).' However, the dot map does manage to communicate pattern quite effectively, where pattern is construed to mean mix as well as dominance and degree of areal coverage. Thus, the technique could be useful for mapping many soil and vegetation features. A preference for dot maps probably grows out of predilection toward broadscale questions about distributions rather than precise questions about magnitudes in particular places. A well-constructed dot map of a distribution usually interposes fewer assumptions between observation and map than does any other MAPS IN LANDSCAPE I N T E R P R E T A T I O N 9 1 map technique. Certainly, I have found it easy to use dot maps to teach students about distributions of soil features or plant species, but that ease might simply reflect the cartographic poverty of the common alternative: a range map on which the area where a species occurs is colored or shaded and the remainder of the map is left blank. At a sufficiently detailed scale, of course, this kind of map is basically 92 PHILIP J GERSMEHL a dot map, but at a broader scale it loses key information about the internal pattern of the distribution, its areas of high and low density (see Figure 2 again). An isoline map works well for a distribution with well-defined areas of high density and smooth gradients from high to low, but the spatial patterns of soil, vegetation or crops are usually complex mosaics rather than the kind of smooth coherent pattern that characterizes atmospheric pressure or other easily isolined phenomena. An intriguing new development is the grid-based pseudo-dot map now possible on many computer mapping systems. These programs can vary the size of the printed symbol in each grid square according to the local density of the phenomenon being mapped, and the result is a very fine-grained unit-area map that can be quite faithful to the original distribution. A color printer allows a limited number of distributions to be superimposed, although the result is not nearly as readable as a well-constructed dot map. A color plotter with software capable of adjusting distributions to avoid symbol overlap may be a solution to this problem, but suitable programs are not yet widely available, and the proposed solution does raise one more methodological question: how much pattern adjustment can a cartographer justify in the interests of map aesthetics? The data input requirements of dot-mapping are high if one is to avoid the use of unjustified pattern-smoothing and interpolation algorithms. The strength of the dot technique is its ability to portray the complex pattern of major components of a typical soil or vegetation mosaic. This purpose is defeated if the mapper chooses to use an interpolation routine to produce a smooth map from a limited number of actual measurements. At the very least, the map reader should learn to recognize the "fingerprints" of this kind of non-data-based pattern smoothing, and their presence should elicit a mental warning about the possible unreliability of the map in portraying a soil or vegetation pattern (Figure 6). Land Systems as Linked Mosaics of Life Forms A radically different approach to the question of landscape interpretation and mosaic cartography is typified by the Biophysical (Ecological) Land Classification Program currently being debated and tried out in Canada. The roots of this ambitious program can be traced to a number of governmental attempts at land classification — the USDA Land Capability Classification system, the older Canadian Land Inventory, and the Australian Land Assessment, among others. However, the identification of political roots does not prepare one for the striking sophistication of the basic structure of the proposed land classification. In broad outlines, the classification is based on the rather revolutionary idea that the essential definition of homogeneity can be quite different at each of the four different scales of analysis. A broad climatic uniformity is the criterion that separates one large Land Region from another, whereas geologic homogeneity is employed to differentiate the smaller Land Districts within a Land Region. MAPS IN LANDSCAPE INTERPRETATION 93