Baradello et al. (2026) Climate change and geostatistical indicators for landscape analysis

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Edoardo Baradello, Marco Maialetti, Donato Scarpitta, Pavel Cudlín, Leonardo Bianchini, Ioannis Konaxis, Gianluca Egidi, Luca Salvati
• DOI: 10.1016/b978-0-443-44133-2.00017-x

Research Groups

• Department for Humanistic, Scientific and Social Innovation, University of Basilicata, Italy
• Academy of Sciences of the Czech Republic, Global Change Research Institute (CAS), CzechGlobe, Czech Republic
• Department of Agriculture and Forest Science, University of Tuscia, Italy
• Department of Tourism Studies, University of Piraeus, Greece
• Department of Methods and Models for Economics, Territory and Finance (MEMOTEF), Faculty of Economics, Sapienza University of Rome, Italy
• Independent Researchers (Lawyer, Architect)

Short Summary

This chapter presents an exploratory synthesis of statistical methodologies and spatial tools for analyzing rapidly evolving regional systems, focusing on indicator theory, natural resource valuation, and sociodemographic dynamics, with an application to agricultural irrigation requirements.

Objective

• To advance an exploratory synthesis of statistical methodologies tailored to the analysis of rapidly evolving regional systems, with particular reference to economic analysis and interdisciplinary domains.
• To illustrate selected tools and topics including indicator theory, physical and monetary valuation of natural resources, and sociodemographic system dynamics.
• To apply spatial tools to generate representations that inform the irrigation requirements of the agricultural sector.

Study Configuration

• Spatial Scale: Local and regional scales, pertinent to robust observation and interpretation.
• Temporal Scale: Analysis of rapidly evolving regional systems and phenomena distributed across temporal dimensions.

Methodology and Data

• Models used: Exploratory synthesis of statistical methodologies, indicator theory, physical and monetary valuation models, systematic account of official statistics, thematic indicators, and composite indices. Spatial tools are employed for generating representations.
• Data sources: Official statistical data.

Main Results

• The chapter provides a comprehensive framework for applying statistical methodologies and spatial tools to analyze complex regional systems.
• It demonstrates the utility of integrating indicator theory, resource valuation, and sociodemographic dynamics for interdisciplinary analysis.
• The proposed analytical framework can generate spatial representations useful for informing specific applications, such as the irrigation requirements of the agricultural sector.

Contributions

• Offers an exploratory synthesis of statistical methodologies specifically designed for rapidly evolving regional systems.
• Provides accessible yet rigorous explanations of official statistical data, making them intelligible to readers with limited quantitative expertise.
• Illustrates the transdisciplinary utility of the analytical framework, particularly for economic analysis and environmental management (e.g., agricultural irrigation).
• Enables novel interpretations of phenomena across temporal and spatial dimensions by integrating official statistics, thematic indicators, and composite indices.

Funding

• Not specified in the provided text.

Citation

@article{Baradello2026Climate,
  author = {Baradello, Edoardo and Maialetti, Marco and Scarpitta, Donato and Cudlín, Pavel and Bianchini, Leonardo and Konaxis, Ioannis and Egidi, Gianluca and Salvati, Luca},
  title = {Climate change and geostatistical indicators for landscape analysis},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-44133-2.00017-x},
  url = {https://doi.org/10.1016/b978-0-443-44133-2.00017-x}
}