“…The problem is even more acute when we base our analyses on legacy data. When working with old survey data, one can consider the survey methodology and test for the resulting biases (Casarotto et al, 2018;Purtill, 2022).…”
Point pattern analysis (PPA) has gained momentum in archaeological research that models large-scale distributions of sites and explanatory covariates. As such, there has been increased interest in the bias of archaeological distributions, which mostly have an impact due to modern land-use change. These interactions, however, have not yet been fully explored. In order to better understand archaeological point patterns as functions of explanatory covariates, we offer three different approaches: (i) environmental preference modelling of archaeological records in different chronological phases; (ii) a custom bias surface that represents the variability of the regional landscape; (iii) an R-package (rbias) allowing the generation of a fuzzified bias surface based on Open Street Map (OSM) data.
“…The problem is even more acute when we base our analyses on legacy data. When working with old survey data, one can consider the survey methodology and test for the resulting biases (Casarotto et al, 2018;Purtill, 2022).…”
Point pattern analysis (PPA) has gained momentum in archaeological research that models large-scale distributions of sites and explanatory covariates. As such, there has been increased interest in the bias of archaeological distributions, which mostly have an impact due to modern land-use change. These interactions, however, have not yet been fully explored. In order to better understand archaeological point patterns as functions of explanatory covariates, we offer three different approaches: (i) environmental preference modelling of archaeological records in different chronological phases; (ii) a custom bias surface that represents the variability of the regional landscape; (iii) an R-package (rbias) allowing the generation of a fuzzified bias surface based on Open Street Map (OSM) data.
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