spsurvey-package: spsurvey: Spatial Sampling Design and Analysis

In spsurvey: Spatial Sampling Design and Analysis

spsurvey: Spatial Sampling Design and Analysis

Description

spsurvey implements a design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. This R package has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Author(s)

Maintainer: Michael Dumelle [e-mail] (ORCID)

Authors:

• Tom Kincaid [e-mail]

• Tony Olsen [e-mail]

• Marc Weber [e-mail]

Other contributors:

• Don Stevens [contributor]

• Denis White [contributor]

See Also

Useful links:

• https://usepa.github.io/spsurvey/

• https://github.com/USEPA/spsurvey

• Report bugs at https://github.com/USEPA/spsurvey/issues

spsurvey documentation built on May 31, 2023, 6:25 p.m.