technology in this area has greatly expanded with artificial intelligence (AI)
and the creation of new AI variables.
Current technology has advanced to produce affordable equipment such as
a bicycle with data recorder to collect data from a multi-spectrum scanner and
instantly obtain visual quality scores (thousands of them) along a route that
nicely covary with past visual quality variables and equations (in a
forthcoming publication by Burley, Hui, Yue, Yilma, and Kuinii). This predictive
science has advanced from explaining about 60% of the variance (Burley 1997) to
over 90% of the variance with an accuracy of 9,999 correct predictions in
10,000 (Burley and Yilmaz 2014, Yilmaz and Burley 2013) . There is a need to
explore this technology on the US highway roadsides and investigate its
mapping visual quality has greatly changed as illustrated by (Jin et al. 2018;
Ylimaz, Liu, and Burley 2018; and Lu et al. 2012). They completed their formative work in
Michigan concerning the validity of predicting visual quality based upon land
cover. Currently they are working upon a
map of Eurasia and North America.
Landscape management and transportation professionals employing these metric
approaches could be highly useful in quantifying the impacts of proposed
projects and alternatives.
Furthermore, very little has been accomplished
in partitioning and comparing environmental perception of various stakeholders.
Studies have shown that cultural background affects how environmental features
are perceived (Mo et al 2011). There is much more work that could be researched
in this area (across the nation) with various stakeholders: African Americans,
Latin Americans, and Native American populations. Stakeholder perception is
important in transportation planning and design and relatively little has been
accomplished in this area. Various
stakeholder groups should be sampled to see how they are ordinated with the
A. Sample stakeholder populations across
the nation in various states/regions with methods established by Burley and
Yilmaz 2014, Yilmaz and Burley 2013, Burley et al. 2011, Mo et al. 2011, and
B. Construction state/regional maps of
visual/environmental quality (using a process similar to Yilmaz et al. 2018,
Jin et al. 2012, and Lu et al. 2012) as a baseline for assessing projects and
C. Employ real time artificial
intelligence sensing methods (in a forthcoming article) along corridors across
states/regions to build a set of case studies and to train practitioners on how
to gather inventory and analysis information to assess environmental/visual
quality for projects.
Burley, J.B. 1997. Visual and ecological environmental quality
model for transportation planning and design.
Transportation Research Record, 1549:54-60.
Burley, J.B., G. Deyoung, S. Partin and J.
Rokos. 2011. Reinventing
Detroit: grayfields—new metrics in evaluating
urban environments. Challenges, 2011 (2):45-54.
Burley, J. B. and R.
Yilmaz. 2014. Visual quality preference: the Smyser index
variables. International Journal of
Energy and Environment, 8:147-153.
J.B. Burley, P. Machemer, P Crawford, H. Xu, Z. Wu, and L. Loures. 2018. The Corbusier dream and Frank Lloyd
Wright vision: cliff detritus vs. urban
savanna. Ergen, M. (ed.) In: Urban Agglomeration. Intech Rijeka, Croatia, 211-230.
Lu, Di, J.B.
Burley, P. Crawford, R. Schutzki, and Luis Loures. 2012.
Chapter 7: Quantitative methods in environmental and visual quality
mapping and assessment: a Muskegon, Michigan watershed case study with urban
planning implications. Advances in
Spatial Planning, InTech: 127-142.
Mo, F., G.
Le Cléach, M. Sales, G. Deyoung, and J.B. Burley. 2011.
Visual and environmental quality perception and preference in the
People's Republic of China, France, and Portugal. International Journal of Energy and Environment, 4(5): 549-556.
Yilmaz, R. and J.B.
Burley. 2013. Dissecting the Smyser index in visual
quality. Modern Landscape Architecture. Proceedings of the 6th WSEAS
International Conference on Landscape Architecture (LA’13), Nanjing, China,
C.Q. Liu, and J.B. Burley. 2018 A visual quality predication map for
Michigan, USA: an approach to validate spatial content. Loures, L. (ed.) In: Land Use – Assessing the Past, Envisioning the Future. Intech Rijeka, Croatia.