Integrating Spatial Statistics into Archaeological Data Modeling

Neanderthal activity and resting areas from stratigraphic unit 13 at the Middle Palaeolithic site of Oscurusciuto (Ginosa - Taranto, Southern Italy)

“…Consequently, this field has been set on the actual extension of excavated area (Study area Method\User_provided_study_area_feature_class). The 99 permutation-based confidence envelopes have been calculated to more accurately evaluate the result (Bevan and Conolly, 2006;Schwarz and Mount, 2006;Spagnolo, 2017;Spagnolo et al, 2016;Winter-Livneh et al, 2010). Kernel density maps (with search radius at 0.25 or 0.5 m, according with the results of Ripley's K cluster analysis) have been realized for a better visual rendering of finds distribution (Conolly and Lake, 2006: 175e177;Spagnolo, 2017;Spagnolo et al, 2016).…”

Electromagnetic induction in subsurface metal targets: Cluster analysis using local point-pattern spatial statistics

“…The standard K-function statistic (Ripley, 1977(Ripley, , 1981Schwarz and Mount, 2006) detects spatial clustering of events with respect to some length scale d. An illustration of spatial clustering, with respect to a random distribution for which the expected number of events increases by one for each unit increase in radius around a specified event, is given in Figure 6. A K-function tests the observed distribution of event locations against the null hypothesis of CSR.…”

“…The data, carefully analyzed, can be used to test and discriminate hypotheses about the underlying site formation processes. Spatial cluster analysis is relevant to archaeological reconstructions or site assessments because artifacts that are associated with a past event such as a battle, or past industrial use such as a foundry or railyard, tend to be found in close proximity (Schwarz and Mount, 2006). At brownfield sites scheduled for reclamation or redevelopment, due to past land-use patterns there often develops clustering of targets of interest, such as underground pipes, drums, storage tanks, or the buried remnants of reinforced concrete foundations.…”

A GIS Modeling of Prehistoric Site Distribution in the Sarfirouzabad Plain of Kermanshah, Northwestern Iran

“…A simple analysis of prehistoric site distribution of Sarfirouzabad performed by GIS allowed us to show the patterns of settlement locating in the valley and its transformations through prehistoric times. When we integrate the resulted models with statistical analysis [19][20] it would be possible to gain a more comprehensive prospection necessary for further archaeological researches of the region. The analysis was based on (1) analysis of site-points to investigate the spatial changes of the sites through of the research period (2) Multiple Ring Buffering analysis to examine spatial relations of synchronized sites for every category; and (3) Buffer Analysis (1000 m. distance buffering) to discover the relationship between the sites and rivers, roads and contemporary settlements.…”