The point-radius method for georeferencing locality descriptions and calculating associated uncertainty

By John Wieczorek, Qinghua Guo, Robert J. Hijmans. International Journal of Geographical Information Science, 2004

402 citations recorded by the source corpus.

Subfields: Ecological Modeling, Environmental Engineering, Geography, Planning and Development

View Paper: https://www.semanticscholar.org/paper/ac4b1cfc70556e634d3da2172a7be0e41024c9b8

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Citations in the corpus

Climate‐based models of spatial patterns of species richness in Egypt’s butterfly and mammal fauna (2009, methodology): “The maximum error of each location was estimated by the point radius method (see Wieczorek et al., 2004) and excessively inaccurate records rejected.”
Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory (2014, background): “The easiest of these data gaps to fix is the geographical coordinates, as the challenge is frequently simply that of adding numerical coordinates to known geographical features (Wieczorek et al., 2004; Guralnick et al., 2006).”
The ability of climate envelope models to predict the effect of climate change on species distributions (2006, background): “Further, most data used for CEMs are from natural history collections and may have inaccurate georeferences (Wieczorek et al., 2004), and are biased in geographic space (Hijmans et al., 2000) which can lead to biased distributions in environmental space (Kadmon et al., 2004).”
A World Malaria Map: Plasmodium falciparum Endemicity in 2007 (2009, background): “Moreover, at these national scales, the fidelity of the geo-positioning of the input PfPR survey data may have an important influence on the uncertainty of the predictions, so procedures that can help incorporate these effects into the modelling may also need to be investigated [91–93].”
A comparison of methods for mapping species ranges and species richness (2006, background): “In contrast, through on-going georeferencing (Wieczorek et al ., 2004) and species occurrence data-sharing initiatives (Stein & Wieczorek, 2004) additional occurrence records are continuously becoming available.”
Effects of species and habitat positional errors on the performance and interpretation of species distribution models (2009, methodology): “Methods also exist for handling georeferencing errors and calculating uncertainty (Wieczorek et al., 2004), although these have not been used in SDM to our knowledge.”
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models (2010, methodology): “Wieczorek et al. (2004) present a method for combining these various sources of uncertainty to calculate the overall uncertainty in each georeferenced location.”
Where is positional uncertainty a problem for species distribution modelling (2014, neutral): “Measures of spatial autocorrelation are either global or local, as reviewed by Getis (2010). Global measures provide a statistic for the entire field under the assumption that the mean, and covariance do not vary over the study area.”
Ecological niche modelling of montane mammals in the Great Basin, North America: examining past and present connectivity of species across basins and ranges (2009, methodology): “To avoid basing ENMs on imprecise occurrence data, only records with a radius of geographical uncertainty of less than 5 km (Wieczorek et al., 2004) were retained for analysis.”
Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines (2007, methodology): “…(Freitag et al ., 1998; Dennis & Hardy, 1999; Hortal & Lobo, 2005), understanding and dealing with errors in identification and spatial location (Wieczorek et al ., 2004; Frey, 2006), choosing appropriate grain sizes for analysis (McPherson et al ., 2006; Guisan et al ., 2007), selecting…”