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Random selection algorithms for spatial and temporal sampling

J A Byers1

  • 1Department of Plant Protection, Swedish University of Agricultural Sciences, Alnarp, Sweden.

Computers in Biology and Medicine
|January 1, 1996
PubMed
Summary

This study introduces seven BASIC programs for selecting treatments and samples in spatial and temporal contexts, aiding experimental design. These algorithms ensure non-redundant, random selection for various scientific sampling needs.

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Area of Science:

  • Experimental Design
  • Computational Biology
  • Statistical Sampling

Background:

  • Effective experimental design requires robust methods for selecting treatments and samples.
  • Spatial and temporal sampling present unique challenges in data collection.
  • Randomized and systematic sampling strategies are crucial for unbiased results.

Purpose of the Study:

  • To present seven BASIC programs utilizing algorithms for treatment and sample selection.
  • To facilitate spatial and temporal sampling in scientific research.
  • To provide tools for generating Latin squares and cubes for experimental design.

Main Methods:

  • Program (1): Random selection from sequential samples within groups for treatments.
  • Program (2): Proportional matching of items across categories to treatments.

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  • Program (3): Random selection of time or distance intervals with minimum spacing.
  • Program (4): Random coordinate selection in a rectangular area with minimum distance.
  • Programs (5-7): Generation of Latin squares, cubes, and Graeco-Latin cubes.

    • Main Results:

    • Algorithms provide systematic approaches for non-redundant sample selection.
    • Tools facilitate random sampling in both spatial (coordinates) and temporal (time/distance) dimensions.
    • Programs generate complex experimental designs like Latin squares and cubes.

    Conclusions:

    • The presented BASIC programs offer versatile tools for optimizing experimental sampling strategies.
    • These algorithms can be applied across various scientific disciplines, including entomology (e.g., bark beetle studies).
    • The software aids in generating unbiased, statistically sound experimental setups.