Settlements and Urban Morphological Quality in Landscape Planning–Analytical Models and Regulating Tools in the Landscape Plan of Regione Toscana

Abstract

Settlement and urban landscape quality is a vast field of research, ranging from studies on “the shape of the city” to studies on functions and services performed by the urban landscape. In Italy, a decisive steering role is entrusted to regional landscape planning, as introduced through the current Code of Cultural Heritage and Landscape. Regional landscape plans define the rules to which all municipal plans must conform. This paper aims to assess the effectiveness with which the general principles and rules regarding settlement and urban landscape quality defined on a regional level through landscape plans are transposed and implemented on a local level through municipal plans. We chose the case study of Regione Toscana, which has a Regional Landscape Plan approved in 2015 (PIT/PPR) that identifies “settlement morphotypes” and “contemporary urbanisation morphotypes” and presents “Guidelines for landscape redevelopment of urbanised fabric in the contemporary city”. We examined how the eight municipal plans approved so far are addressed in the PIT/PPR contents. We also conducted a more in-depth study on topics of interest through structured interviews with four designers who were responsible for most of the plans analysed. We also conducted a more in-depth study on topics of interest through structured interviews with several plan designers. The results from the discussion showed some weakness in the PIT/PPR’s ability to guide local planning tools to improve urban quality. Additionally, the PIT/PPR’s effectiveness appears more evident in the rhetoric of arguments used by local plans than in the results of the transformations that they prefigure.

Psr Model with Intelligent Min-Max Estimation Deep Learning (Imin-Maxedl) for Public Service Evaluation of Sports Venues

“…These algorithms can then identify intricate patterns and relationships within the data that may not be readily apparent to human analysts [13]. Deep learning models can uncover correlations between specific psychological interventions, such as visualization techniques or self-talk strategies, and improvements in athletic performance across different sports and skill levels [14]. Moreover, deep learning enables the development of predictive models that anticipate how individual athletes may respond to different psychological interventions.…”

Data Analysis of Linguistic Landscape in the Context of “Internationalization +” Urban Construction

“…The issue of internationalization and localization of the urban linguistic landscape has become an important issue in the current study of the urban linguistic landscape [5]. How to find a balance between internationalization and localization so that the urban linguistic landscape has both the modern atmosphere of internationalization [6][7]. It is a difficult problem that needs to be solved in current urban language landscape research to reflect the unique charm of the local culture at the same time [8].…”

Anthropogenic Transformation of the River Basins of the Northwestern Slope of the Crimean Mountains (The Crimean Peninsula)

“…At the same time, a large number of research is devoted to the study of anthropogenic transformation of landscapes [23][24][25][26] as well as the emergence of risks arising from these changes [27][28][29]. To achieve the goals of sustainable development and leveling the adverse effects of anthropogenic impact, many researchers use the concept of landscape planning [30][31][32].…”