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Gridded matrices of climatic data can be mapped by computer with a variety of symbolization methods, all of which have some shortcomings. A new mapping procedure, dot-density shading, is herein proposed as an alternative mapping form. Dot-density shading produces continuous-appearing dot patterns whose density is proportional to the data. Significant algorithms are those for computing dot numerosity and quasi-random dot placement. A number of application including data error detection and anomaly recognition are discussed.