Abstract:Introduction People move to maintain relationships, to flee natural disasters, escape violence, fulfill spiritual callings, access resources, and for a host of other reasons. At times, human movement patterns reflect culturally defined values. Landscape archaeologists have embraced this line of inquiry, attempting to track movement patterns in ancestral contexts as a means of illuminating social dynamics. To facilitate movement-oriented analyses, archaeologists are capitalizing on new technologies and developi… Show more
“…The second quantitative method used to compare the actual trails to the LCPs was through an assessment of correlation factors [ 21 ]. The correlation factor is defined as the percentage that the modelled LCPs overlap with the buffered trails, as mapped with the Fitbit® Surge.…”
Section: Resultsmentioning
confidence: 99%
“…Research utilizing this approach has commonly produced low interaction overlaps, such as a study in the Chaco Canyon area using 500 m buffers resulted in an intersection overlap as low as 25% [ 21 ]. In comparison, our high interaction overlaps at smaller buffer zones, such as approximately 45% overlap at a 20m buffer as seen in Fig 6 , points to the strength of our model, as our model deviates less than 75m away from the recorded trails.…”
Least Cost Path (LCP) analysis allows a user to define a cost parameter through which cost of movement can be assessed using Geographical Information Systems (GIS). These analyses are commonly used to construct theoretical movement through a landscape, which has been useful for creating hypotheses concerning prehistoric archaeology and landscape genomics. However, LCP analysis is commonly employed without testing the generated LCP(s), complicating its usefulness as a methodological tool. This paper proposes a model for analyzing movement in ArcGIS by using topography data to calculate slope. This slope data is then then used to calculate LCPs based on travel time and kilocalorie expenditure. LCPs were constructed in the Nature Preserve at Binghamton University, a 182-acre area that consists of wetland and mountainous terrain, and a Fitbit® Surge activity monitor was used to test the accuracy of the model's predictions. Paired sample t-tests show a lack of significant difference between calculated and walked time in our analysis (p = .953), suggesting that our model can estimate travel time between two points based solely on slope of the region. Paired sample t-tests also show a lack of significant difference between calculated and observed kilocalorie expenditure (p = .930), suggesting that our model is also capable of estimating kilocalorie expenditure associated with movement between two points. Finally, paired sample t-tests confirm that straight line distances do not reflect real movement through a terrain (p = .009), highlighting the need for alternate measures of movement when analyzing the effects of local landscape on movement. Our current model shows strength in its estimations of travel time and kilocalorie expenditure based on topography of a region-future iterations of the model need to establish the statistical similarity between our model's estimations and recorded values for walking time and kilocalorie expenditure.
“…The second quantitative method used to compare the actual trails to the LCPs was through an assessment of correlation factors [ 21 ]. The correlation factor is defined as the percentage that the modelled LCPs overlap with the buffered trails, as mapped with the Fitbit® Surge.…”
Section: Resultsmentioning
confidence: 99%
“…Research utilizing this approach has commonly produced low interaction overlaps, such as a study in the Chaco Canyon area using 500 m buffers resulted in an intersection overlap as low as 25% [ 21 ]. In comparison, our high interaction overlaps at smaller buffer zones, such as approximately 45% overlap at a 20m buffer as seen in Fig 6 , points to the strength of our model, as our model deviates less than 75m away from the recorded trails.…”
Least Cost Path (LCP) analysis allows a user to define a cost parameter through which cost of movement can be assessed using Geographical Information Systems (GIS). These analyses are commonly used to construct theoretical movement through a landscape, which has been useful for creating hypotheses concerning prehistoric archaeology and landscape genomics. However, LCP analysis is commonly employed without testing the generated LCP(s), complicating its usefulness as a methodological tool. This paper proposes a model for analyzing movement in ArcGIS by using topography data to calculate slope. This slope data is then then used to calculate LCPs based on travel time and kilocalorie expenditure. LCPs were constructed in the Nature Preserve at Binghamton University, a 182-acre area that consists of wetland and mountainous terrain, and a Fitbit® Surge activity monitor was used to test the accuracy of the model's predictions. Paired sample t-tests show a lack of significant difference between calculated and walked time in our analysis (p = .953), suggesting that our model can estimate travel time between two points based solely on slope of the region. Paired sample t-tests also show a lack of significant difference between calculated and observed kilocalorie expenditure (p = .930), suggesting that our model is also capable of estimating kilocalorie expenditure associated with movement between two points. Finally, paired sample t-tests confirm that straight line distances do not reflect real movement through a terrain (p = .009), highlighting the need for alternate measures of movement when analyzing the effects of local landscape on movement. Our current model shows strength in its estimations of travel time and kilocalorie expenditure based on topography of a region-future iterations of the model need to establish the statistical similarity between our model's estimations and recorded values for walking time and kilocalorie expenditure.
“…Judge (1989:243) proposed that “the sites of the Chacoan system were physically integrated by a road system that may have comprised five major segments, linking all but the northeast quadrant of the Basin.” Nevertheless, researchers have speculated earnestly about road functions without much resolution (Clark and Fritschle 2011; Judd 1964:141–142; Kantner and Hobgood 2003; Kantner and Kintigh 2006; Lekson 2009; Roney 1992; Snygg and Windes 1998; Sofaer et al 1989; Vivian 1997; Weinig 2017). And despite the persistent influence of popular assumptions about a Chacoan road “system” (see reviews in Ebert and Hitchcock 1980; Kantner 2003; Snead 2017), physical evidence for roads or trunk routes other than the North and South Roads remains elusive (e.g., Marshall 1994; Marshall et al 1979; Field et al 2019).…”
The construction of great houses during the Bonito Phase (ca. AD 850–1200) in Chaco Canyon, New Mexico, required massive amounts of building material and efficient mobilization and coordination of large labor pools. We employ least cost path analysis (LCA) to explore the potential communication network among great house communities in the Chaco “core” area and its relevance in managing sustained labor for constructing great houses. The results suggest that that the primary sources of labor for communal building projects were agricultural communities located within one to three hours of the largest buildings in the canyon.
“…Other roads likely served local purposes, given that they extend only a short distance away from a community and do not articulate with any other roads (Stein 1983). Local and regional Chaco roads were probably used for many reasons—from pilgrimage routes (Judge 1989; Van Dyke 2007) to long-range transportation paths (Betancourt et al 1986; Field et al 2019) and as symbolic connections with important geographic features, directions, and/or noncontemporaneous sites (Fowler and Stein 1992; Kantner 1997; Kincaid 1983; Lekson 2015; Mills 2002; Roney 1992; Sofaer et al 1989; Vivian 1997). Figure 1.Location of major or well-studied Chaco roads within the San Juan Basin.…”
Despite considerable developments in the archaeological application of lidar for detecting roads, less attention has been given to studying road morphology using lidar. As a result, archaeologists are well equipped to locate but not thoroughly study roads via lidar data. Here, a method that visualizes and statistically compares road profiles using elevation values extracted from lidar-derived digital elevation models is presented and illustrated through a case study on Chaco roads, located in the US Southwest. This method is used to establish the common form of ground-truthed Chaco roads and to measure how frequently this form is across non-ground-truthed roads. This method is an addition to the growing suite of tools for documenting and comparing roads using remotely sensed data, and it can be particularly useful in threatened landscapes where ground truthing is becoming less possible.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
