Abstract
Viewshed analysis is one of geographic information system (GIS) applications commonly used in archaeology. With the availability of large and high-resolution terrain data, viewshed analysis offers a significant computational opportunity while also posing a challenge in GIS and landscape archaeology. Although a number of studies adopted high-performance and parallel computing (HPC) for handling the compute- and data-challenge of viewshed analysis, there are few archaeological studies involving HPC to address such a challenge. It could be because of the complexity of HPC techniques that are difficult for archaeologists to apply. Therefore, this study presents a simple solution to accelerate viewshed analysis for archaeological studies. It includes a parallel computing approach with shared-nothing parallelism (each computing node accesses some specific pieces of datasets, and it has own memory and storage). Moreover, it is powerful for handling compute- and data-intensive research in landscape archaeology. In addition, the unique features of visibility patterns (irregular, fragmented, and discontinuous) may introduce useful information for landscape archaeologists. Thus, we added fragmentation calculation following viewshed analysis to further examine the influence of visibility patterns. We draw our case study from the metropolitan area of Oyo Empire, West Africa (1600–1830 AD). This parallel computing approach used an equal-viewpoint decomposition strategy on a Windows-based computing cluster. Our results showed that our parallel computing approach significantly improve computing performance of viewshed fragmentation analysis. Also, the results of viewshed fragmentation analysis demonstrated that there exists a relationship between visibility patterns and terrain information (elevation).
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Notes
- 1.
“For each stone circle/control point the second smaller geographical region is used to constrain the area that must be examined when one is computing the viewshed” (Lake and Ortega 2013, p. 224).
- 2.
Total viewshed is calculating viewshed results from all points from a given terrain dataset (Llobera et al. 2010).
- 3.
Cumulative viewshed is to calculate viewshed results from all viewpoints of interests on a given terrain dataset and then adding them together (Wheatley 1995).
- 4.
In Benedict’ study, he used “isovist” instead of “viewshed.” The definition of “isovist” is “ taking away from the architectural or landscape site a permanent record of what would otherwise be dependent on either memory or upon an unwieldy number of annotated photographs” (Tandy 1967, p. 9).
- 5.
The number of visible cells stands for how many cells can be seen from other cells.
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Zheng, M., Tang, W., Ogundiran, A., Chen, T., Yang, J. (2020). Parallel Landscape Visibility Analysis: A Case Study in Archaeology. In: Tang, W., Wang, S. (eds) High Performance Computing for Geospatial Applications. Geotechnologies and the Environment, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-030-47998-5_5
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