Effective Geographic Sample Size in the Presence of Spatial Autocorrelation

By Daniel A. Griffith. 2005

182 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/d400c024faff924688593b0097abeeb6140db6a6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. R. Walker
2. Stephen G. Perz
3. E. Arima
4. C. Simmons
5. Marcellus Marques Caldas
6. Yong Ge
7. Alfred Stein
8. Andrew O. Finley

In Environmental Engineering

1. Terje Gobakken
2. Erik Næsset
3. Timothy G. Gregoire
4. Ross Nelson
5. Michael Alonzo
6. Bruce Douglas Cook
7. Douglas C. Morton
8. Hans-Erik Andersen

Citations in the corpus

• A review of spatial sampling (2012, background): “The stronger the spatial autocorrelation on the map, with high levels over long distances, the more redundancy will be present in a sample if the inter-point sampling distance is small (Griffith, 2005).”
• Theorizing Land Cover and Land Use Change: The Peasant Economy of Amazonian Deforestation (2007, background): “Such a sample possesses desirable statistical properties (Griffith 2005).”
• Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations (2017, methodology): “The SD metrics for total (mean) AGB resulting from the Null models are underestimated because the errors are falsely assumed to be independent of one another (Griffith, 2005).”