Assessing visibility and geomorphological biases in regional field surveys: The case of Roman Aesernia

Abstract

Archaeological field survey data can be biased by many factors, such as ground visibility conditions (e.g. vegetation, plowing) and geomorphological processes (erosion, deposition). Both visibility and geomorphological factors need, therefore, to be assessed when patterns of settlement and location preferences are inferred from survey data. Although both factors have been taken into account in a variety of fieldwork projects and studies, their combined effects remain hard to predict. In this paper, we aim to address this issue by presenting a visualization method that helps in evaluating in combination the possible visibility and geomorphological effects in regional, site-oriented field surveys. Capitalizing on first-hand data on both archaeology and soil types produced by the recent Leiden University field survey project in the area of Isernia (Roman Aesernia, Central-Southern Italy), we propose a combined application of statistical tests and geopedological analysis to assess the extent and scale of the main biases possibly affecting the interpretation of the ancient settlement organization. Translating both sets of biases into GIS maps, we indicate the likelihood that negative field survey observations (absence of sites), in specific parts of the landscape, are genuine or rather distorted by biasing factors. The resulting "archaeological detectability" maps allow researchers to formally highlight critical surveyed zones where the recording of evidence is likely unreliable, and thus provide a filter through which archaeologists can calibrate their interpretations of field survey datasets.

Agrarian landscapes in the Iberian Iron Age: Mountain communities and land use in southeastern Iberia

“…settlements and the ritual area (Figures and ), very little was gleaned in the agricultural areas selected for study. Certain perspectives, such as the combined application of statistical tests and geopedological analysis (Casarotto, Stek, Pelgrom, van Otterloo, & Sevink, ), could nuance these results by assessing the extent of possible biasing factors.…”

A systematic GIS-based analysis of settlement developments in the landscape of Venusia in the Hellenistic-Roman period

“…In this paper, a different approach to understanding colonial settlement strategies is offered, using a GIS-based quantitative and qualitative analysis of settlement behavior and location preferences in the colonial landscape of Venusia. This paper complements and further expands the research strategy outlined in a set of previous articles, which have, instead, focused on settlement pattern analysis and deductive reasoning (Casarotto et al 2016) and survey methodological issues (Casarotto et al 2017). As such, it offers a useful approach to use legacy, site-based datasets for territorial investigations.…”

“…To further test the trends highlighted before, and to see whether there are other correlations which can help explain the resulting patterns, we now describe the outcomes of the inductive location preference analysis. The technical explanation and discussion of the methods and statistical tests used for carrying out such an analysis is provided elsewhere (Casarotto, 2017). Here, we only list the detected location preferences per period and size category and highlight whether these preferences change through time.…”

Using soil survey data as a predictive tool for locating deeply buried archaeological deposits in stream valleys of the Midwest, United States

“…Finally, given that soil survey databases exist for many other countries, including Africa (Leenaars, ; Paterson & Mushia, ), Australia (Johnston et al, ), Brazil (Cooper et al, ), Canada (Agriculture & Agri‐Food Canada, ), and China (Zhang et al, ), the approach taken in this paper to infer LSAs from soil data has widespread applicability. Also, local or regional soil data may exist in the form of soil maps created during archaeological surveys (e.g., Casarotto, Stek, Pelgrom, van Otterloo & Sevink, ). We contend that such data sets can similarly be mined for information pertaining to the four factors of the age of sedimentary deposits, soil stratigraphy, depositional environment, and drainage.…”