Use of a GIS predictive model for the identification of high altitude prehistoric human frequentations. Results of the Sessera valley project (Piedmont, Italy)

A model of spatial location: New data for the Gor River megalithic landscape (Spain) from LiDAR technology and field survey

“…The 230 new placements were categorized as clear (81), when the anomaly is absolutely equal to the shape and size observed in the maps for the already recorded dolmens, probable (79), when the anomaly is highly similar to the shape and size observed for the preserved dolmens, and possible (70), when the anomaly presents similarities with the shape and size of the documented dolmens but they seem to be more fuzzy. The objective of this internal classification was to check the reliability of the LiDAR DTM, to see if the possible structures apparently more similar in the digital plan to the currently preserved dolmens correspond more often to real burial mounds or if the resolution prevents more precise characterization, which has been a core pillar in other similar researches that, on the other hand, start from more defined archaeological data (Caracausi et al, 2018; Gárate et al, 2020; Verhagen & Whitley, 2012) (Figure 5).…”

Tracing the Palaeolithic settlement patterns in the Western Tian Shan piedmont: an example of predictive GIS modelling use

“…The development of spatial analysis methods and the increased availability of the corresponding equipment (Stirn, 2014) have led to a continuous increase in the number of studies conducted in high mountain areas. These often produce results that significantly increase understanding of the use of these areas in both the Palaeolithic (Avni et al ., 2021; Efstratiou et al ., 2014; Gasparyan et al ., 2014; Gassiot et al ., 2017; Chen et al ., 2019; Ossendorf et al ., 2019) and in other periods (Caracausi et al ., 2018; Taylor et al ., 2019). The peculiar nature of high‐mountain areas has repeatedly led researchers to use spatial analyses, whether based on easily accessible routes (Kondo et al ., 2018; Li et al ., 2019), geology (Iovita et al ., 2020), march range (Loyola et al ., 2019), the course of palaeochannels (Breeze et al ., 2015), or conclusions drawn mainly on slope parameters and hydrology (Caracausi et al ., 2018).…”

“…In our research, we decided to create a model based on previous experience in selecting the probability of the occurrence of sites (Stančič and Kvamme, 1999; Wheatley and Gillings, 2002; Kompatscher and Hrozny Kompatscher, 2007; Caracausi et al ., 2018) adapting the means of conducting analyses and research to the existing environmental conditions. Thanks to the use of the predictive model it was possible to reduce the area for surface prospecting by five times.…”

“…In the model, it was decided to use Boolean logic, which is one of the approaches adopted in building similar GIS models (Vullo et al ., 1999; Wheatley and Gillings, 2002; Caracausi et al ., 2018). The approach used is based on a zero and one (binary) evaluation of the parameters used.…”

“…The theoretical assumptions for the slope inclination analysis were adopted following Caracausi et al . (2018), but considering slope values of 0–20°. For the purpose of the model, training fields were generated based on the DEM file that cover the areas with the above‐mentioned slope (Fig.…”