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Spatiotemporal Distance Class Analysis of Plant Disease Epidemics. Scot C. Nelson, Assistant professor, University of Hawaii at Manoa, Department of Plant Pathology, Honolulu 96822; Phytopathology 85:37-43. Accepted for publication 21 September 1994. Copyright 1995 The American Phytopathological Society. DOI: 10.1094/Phyto-85-37.

Spatiotemporal distance class analysis is proposed as a new form of spatiotemporal analysis of intensively mapped, binary data. The method detects and quantifies attributes of nonrandom patterns of disease increase in regularly spaced plant populations. The method tests the hypothesis that healthy plants in a population have an equal (random) chance of becoming diseased in the period between two disease assessment dates. Mapped disease incidence evaluations from two assessment dates are needed for the analysis. Expected spatial patterns of diseased plants for the second assessment date are generated by assigning the number of newly diseased plants to random spatial positions among the healthy plant population observed on the first assessment date. Distance class analysis techniques are used to compare these expected patterns with the actual spatial pattern observed on the second assessment date. The number, location, and configuration of significant distance classes are used to evaluate the randomness of the observed spatial distribution. Hypothetical examples and data from an epidemic of citrus variegated chlorosis in Brazil and an epidemic of papaya ringspot in Hawaii are presented to illustrate the procedure.