Landscape Planning of Infrastructure through Focus Points’ Clustering Analysis. Case Study: Plastiras Artificial Lake (Greece)

Abstract

Even though landscape quality is largely a subjective issue, the integration of infrastructure into landscapes has been identified as a key element of sustainability. In a spatial planning context, the landscape impacts that are generated by infrastructures are commonly quantified through visibility analysis. In this study, we develop a new method of visibility analysis and apply it in a case study of a reservoir (Plastiras dam in Greece). The methodology combines common visibility analysis with a stochastic tool for visual-impacts evaluation; points that generate high visual contrasts in landscapes are considered Focus Points (FPs) and their clustering in landscapes is analyzed trying to answer two questions: (1) How does the clustering of Focus Points (FPs) impact the aesthetic value of the landscape? (2) How can the visual impacts of these FPs be evaluated? Visual clustering is calculated utilizing a stochastic analysis of generated Zones of Theoretical Visibility. Based on the results, we argue that if the visual effect of groups of FPs is positive, then the optimal sitting of FPs should be in the direction of faint clustering, whereas if the effect is negative, the optimal sitting of FPs should be directed to intense clustering. In order to optimize the landscape integration of infrastructure, this method could be a useful analytical tool for environmental impact assessment or a monitoring tool for a project’s managing authorities. This is demonstrated through the case study of Plastiras’ reservoir, where the clustering of positively perceived FPs is found to be an overlooked attribute of its perception as a highly sustainable infrastructure project.

Spatial Hurst–Kolmogorov Clustering

“…The 1D clustering behavior has been identified in many scientific fields (see reviews in [7,[15][16][17]). The 2D spatial clustering behavior has also been explored in many fields such as hydrology (e.g., [18][19][20], and references therein), biology and ecosystems (e.g., [21,22]), life sciences (e.g., [23][24][25][26]), networks (e.g., [27][28][29]), urban structures (e.g., [30,31]), rock formation (e.g., [32]), turbulence (e.g., [7,33]), art (e.g., [34][35][36]), landscape analysis (e.g., [37,38]), simulated evolution of the universe [39] and many others (e.g., [40]). A unified approach for the quantification of the 2D spatio-temporal clustering in terms of variability in the scale domain (instead of in the common lag and frequency domains) can be found in the applications of the current entry, where a stochastic methodology is presented that quantifies clustering in 2D spatial fields by analyzing the spatial structures over time, and by exploring how the HK dynamics highly increase the induced uncertainty in terms of spatio-temporal variability in the scale domain.…”

“…For a review of such studies, see [7,13] and references therein, where also a massive globalscale analysis in the scale domain is included for several hydrological-cycle processes (i.e., near-surface air temperature, dew point, humidity, atmospheric pressure, near-surface wind speed, streamflow and precipitation) and microscale turbulent processes (such as grid turbulence and turbulent jets). Alternative scientific fields, where the analysis is performed in the scale domain and by using the climacogram, include studies of rock formations [32], landscapes [37,38], water-energy nexus [60,61], time-irreversible processes [62,63], multilayer perceptron [64] and many others [65] as shown in the applications of the entry. It is again emphasized that this entry focuses on the multi-dimensional spatio-temporal stochastic metrics in the scale domain, as presented in the next sections.…”

Threats in Water–Energy–Food–Land Nexus by the 2022 Military and Economic Conflict

“…Partitioning is a method of protection and can be applied to many different threats such as viruses (social distancing), wars, and wildfires [2,63]. Using satellite images and publicly available data, we evaluate by Climacogram Integral the evolution of spatial clustering in Europe (1990-2010) using Hurst-Kolmogorov dynamics [2,[64][65][66][67][68][69][70][71]. Urbanization is reflected in the increasing trend of cities' clustering, which we have observe lately.…”