Sampling of a population is frequently required to understand trends and patterns in
natural resource management because financial and time constraints preclude a complete
census. A rigorous probability-based survey design specifies where to sample so that inferences
from the sample apply to the entire population. Probability survey designs should be used in
natural resource and environmental management situations because they provide the
mathematical foundations for statistical inference. Development of long-term monitoring designs
demand survey designs that achieve statistical rigor and are efficient, but remain flexible to
inevitable logistical or practical constraints during field data collection. The Reversed Randomized
Quadrant-Recursive Raster (RRQRR) algorithm is an implementation of the Generalized Random
Tessellation Stratified (GRTS) algorithm. The RRQRR toolbox allows for probability-based
spatiality balanced sample designs to be implemented within a Geographic Information System
This toolbox consists of three tools that generate a RRQRR sequence raster, filter the RRQRR sequence raster against a probability raster, and generate sample site locations. The RRQRR toolbox is flexible because it allows existing points to be incorporated into the RRQRR sequence raster, uses the ArcInfo raster format as its data backbone, and is scripted in Python for ArcGIS version 9.1. The functionality and flexibility of the RRQRR toolbox makes it useful for natural resource sampling applications.
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