Research PaperFrom viewsheds to viewscapes: Trends in landscape visibility and visual quality research
Introduction
Visibility and visual quality analysis is a critical aspect of human-environment interaction research. An observer’s visual field is fundamental to the formation of spatial preferences (Nijhuis et al. 2011) and affects almost every aspect of human-environment experience on a vast range of scales, from internal emotions (Millar et al., 2021) to tourism economics (Schirpke, Timmermann, Tappeiner, & Tasser, 2016). Visibility and visual quality—which we will refer to as VVQ—analysis can be traced back more than half a century in multiple scientific domains, where it has become common practice in assessing and understanding visual experiences (Nijhuis et al. 2011). Given the growing accessibility and increasingly widespread use of VVQ analysis across a range of disciplines, there is an emerging need for cross-disciplinary understanding of current progress and future directions for the field.
VVQ analysis consists of calculating spatial models of what geographical areas can be seen from a set point, known as a viewshed (Fig. 1). The term “viewshed” was first published in 1967 by surveyor and landscape architect Clifford Tandy (Tandy, 1967). In 1968, computer scientists at the U.S. Forest Service developed a program called VIEWIT (Amidon & Elsner, 1968) that computed “seen area” based on gridded elevation cells. While VIEWIT’s documentation doesn’t use the terms “viewshed,” “raster,” or “GIS,” it was a harbinger of VVQ research to come. The use of geospatial computation in modeling visibility has proliferated in recent decades, with advances in Geographic Information Systems (GIS) making visibility modeling accessible by operationalizing viewshed algorithms with easy-to-use and widely distributed software (ESRI., 2021, GRASSGIS, 2021). Historically, calculating line-of-sight for VVQ analysis has been a computationally intensive process (Zhao, Padmanabhan, & Wang, 2013). Researchers continue to develop new algorithms, leveraging high performance computing and GPU processing to reach new heights in computational efficiency and accuracy (Zhao et al., 2013, Zhu et al., 2019). Thus, GIS-based visibility models have become a mainstay of human-environment interaction research such as landscape planning (Anderson and Rex, 2019, Inglis and Vukomanovic, 2020), architecture (Rød and van der Meer, 2009, Weitkamp, 2011), archaeology (Garcia-Moreno, 2013, Van Dyke et al., 2016) and natural resources (Chamberlain and Meitner, 2013, Depellegrin, 2016, Aben et al., 2018).
VVQ analysis is applied with a wide variety of standards and practices, owing to its multidisciplinary applications. Various vocabulary terms represent overlapping concepts: for example, “isovist,” “visualscape,” “viewshed” and “line-of-sight”, are scattered throughout the literature, which could lead to disparate interpretations (Table 1). For example, the term “viewshed” often refers to the area that can be seen from an observer point, but some studies interpret viewshed area as the quality of “openness” (Wilson et al., 2008, Weitkamp et al., 2011). More recently, the term “viewscape” aims to characterize not just what can be seen, but how humans visually connect to their surrounding 3-dimensional terrain and built environment (Vukomanovic, Singh, Petrasova, & Vogler, 2018). While perhaps consistent in their respective fields, these discrepancies in terminology may encumber cross-disciplinary understanding and promote advancements in VVQ research that remain in disciplinary silos.
Interdisciplinary inquiry is integral to the geospatial sciences (Gilbert, 1909, Baerwald, 2010) and VVQ research exemplifies this synergy between disciplines. Questions about visibility and visual quality often involve phenomena that cross disciplines, and that can benefit from cross-fertilization of ideas, technology, frameworks and insight (Baerwald, 2010). Given discrepancies in terminology (Table 1), and the rapid advancement of geospatial tools and technology—such as GPU-based parallel processing (Osterman, Benedičič, & Ritoša, 2014), lidar-derived surface models (Vukomanovic et al., 2018) and a glut of spatial big data (Lee & Kang, 2015) —the lack of recent and multidisciplinary reviews represents a mounting barrier in VVQ research. Now is the opportune moment for a literature review that takes stock of the state of the field, highlights research opportunities that leverage emerging geospatial technology, and serves as a launchpad for novel and interdisciplinary applications.
Past reviews of VVQ literature are either decades old or focused on a specific niche of VVQ analysis. A qualitative literature review in 2003 explored the use of GIS-based viewshed analysis in archaeology, where researchers use visibility models to locate historic sites and confirm theoretical suppositions (Lake, Woodman, & Mithen, 1998). Another qualitative review evaluated the influence of GIS-based visibility analysis across disciplines (Bishop, 2003). However, the technological advances in GIS and computing since this review suggest a timely opportunity to examine the growing field of VVQ research through a multidisciplinary lens. In 2018, researchers reviewed the usage of VVQ analysis in wildlife ecology research, encouraging future ecology researchers to consider viewsheds as a rich source of ecological information about the spatial patterns and behaviors of animals (Aben et al., 2018). A more recent literature review examined studies related to the visual assessment of landscapes, however it was limited to the journals Landscape and Urban Planning, Urban Ecology and Landscape Planning (Gobster, Ribe, & Palmer, 2019).The authors called for more systematic reviews with expanded definitions of what the visual quality research field entails beyond visual assessment. Our study directly builds on these past reviews and recommendations by conducting the first known multidisciplinary, systematic review of visibility and visual quality research.
In this study, we systematically reviewed and analyzed a subset of peer-reviewed research papers published from 2000 to 2019 to ask the following questions: 1) What are the geographic and temporal trends in VVQ research, and how do they vary across scientific discipline?, 2) What types of computational resources and algorithms are visibility researchers using?, and 3) How do different disciplines approach characterizing the visual quality and human dimensions of viewsheds? We describe the systematic review methods in section 2, and share descriptive statistics and visualizations of quantitative analysis results in section 3. In section 4, we elaborate on these results with a discussion of the papers reviewed and the implications of the trends and applications we observed. Finally, in section 5, we synthesize these concepts into a framework of suggested steps for VVQ researchers to consider in future studies.
The visual field is the primary way humans connect to our surroundings (Kandel et al., 2000), so visibility analysis could be a common lens through which to study human-landscape connection. Given advancements in geospatial technology and growing data availability, VVQ research has the potential to have far-reaching impacts in scientific research beyond architecture, archaeology and natural resources, and shape growing interdisciplinary research across human-environment systems. This study aims to promote insight into past VVQ research in order to empower researchers to bridge disciplinary divides and propel visibility analysis into the future.
Section snippets
Methods
We conducted a systematic literature review of peer-reviewed visibility and visual quality (VVQ) articles using the Web of Science database to characterize the past 20 years (2000–2019) of VVQ research. We collected a dictionary of search terms through preliminary analysis of the literature to capture the range of terms employed in visibility studies across disciplines. We aimed to limit our search to articles that digitally computed visibility using gridded elevation surfaces or other
Geographic and disciplinary research trends
The Web of Science search yielded 528 peer-reviewed articles published between 2000 and 2019. The number of publications increased steadily over the study period (Fig. 2; black line), starting with 3 publications in 2000 and 63 in 2019 (21x more). The International Journal of Geographical Information Science published the most VVQ research (4.5% of search results), followed by Landscape and Urban Planning (3.8%) and the Journal of Archaeological Science (3.6%). Of the 267 journals represented
Discussion
Visibility and visual quality (VVQ) analysis has a long history of use in a variety of scientific studies, from optimizing trade-offs in energy and urban development, to studying the patterns of movement and settlement in ancient civilizations, to conserving natural landscapes for their aesthetic value. Powered by rapidly advancing GIS-based computation, the cross-disciplinary nature of VVQ research benefits from a review to understand past uses, current trends and opportunities for
The Four R’s: A framework for planning and writing future VVQ studies
Moving forward, it will be vital to encourage consistent use of terms, to think critically about study designs, to compare and validate algorithms in a variety of landscapes, and to cultivate open science practices to improve reproducibility and prevent researchers from reinventing the wheel. Such measures would facilitate the use of accurate and precise VVQ modeling methods across disciplines. To help achieve these goals, we drew from the findings of this review to synthesize a set of
Conclusion
This study offers the first known multidisciplinary systematic review of visibility and visual quality research. From the total 528 peer-reviewed publications our search produced, an in-depth review of 176 studies yielded insight into the diverse assemblage of research that leverages VVQ methods. Our analysis highlights several trends and opportunities. First, VVQ research increased 21-fold over the past 20 years. Research was primarily in the fields of archaeology and natural resources and was
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank H. Mitosova for feedback and insight on viewshed history and classification categories. We thank L. Smart, J. Randall, C. Gonzales and A. Yoshizumi for helpful feedback on earlier versions of this manuscript.
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