Special issue 2
AI, Spatial Methods and the Green-Digital Transformation of Official Statistics
scarica qui la pubblicazione
Editors
Fabio Crescenzi, Luigi Fabbris, Andrea Mazzitelli, Alessandra Righi, Alessandro Rinaldi, Maurizio Vichi
Articles
Leveraging Convolutional Neural Networks for Urban Vegetation Statistics from Satellite Imagery: A Study over Major Italian Cities
Fabrizio De Fausti, Michelangelo Tronti
Leveraging Expert Knowledge and Data-Driven Classification for Decision-Making: A Case Study in Official Population Statistics
Antonella Bernardini, Angela Chieppa, Nicoletta Cibella, Fabrizio Solari
Leveraging Industry 5.0 for Official Business Statistics Innovation
Diego Distefano, Paola Bosso, Giovanni Gualberto Di Paolo, Pasquale Papa
Geostatistical and Spatial Applications to Optimize the Management of Olive Groves by Agricultural Companies within the European Carbon Emission Reduction Strategy
Angela Maria Digrandi, Pasquale Cimmino
Business Alliances and Spatial Network Backbones: A National-Scale Analysis of Formal Collaborations
Andrea Enrico Vurro, Alessio Bumbea, Annamaria Giuffrida, Andrea Mazzitelli, Giuseppe Espa
Enterprises Governance and Vocational Training Strategy: Insights from Official Statistic
Roberto Di Manno, Manuela Nicosia, Emanuela Trinca
Are Nearby Firms Getting Greener? The Domino Effect of Environmental Performance in Europe
Emma Bruno, Rosalia Castellano, Gennaro Punzo
AI, Spatial Methods and the Green-Digital Transformation of Official Statistics brings together a selection of original contributions that explore how artificial intelligence, spatial methodologies, and integrated data sources jointly enhance the production and interpretation of official statistics.
The volume stems from the scientific discussions developed during the international conference “Measuring and Interpreting World Changes with Statistics, Data Science and AI”, held in Rome from 18 to 20 September 2024. The conference was jointly organised by the Association for Applied Statistics (ASA), the Department of Statistical Sciences of Sapienza University of Rome, and the Italian National Institute of Statistics (ISTAT), with the participation of several academic and institutional partners. The event provided a multidisciplinary forum for examining the contribution of statistics, data science, and artificial intelligence to the understanding of contemporary economic, environmental, and social transformations.
The Special Issue addresses both methodological and applied perspectives, focusing on the growing availability of complex, high-dimensional, and heterogeneous data generated by administrative systems, satellite infrastructures, business registers, and digital platforms. Particular attention is devoted to the integration of artificial intelligence within statistical production processes, the use of spatial and geostatistical models for territorial analysis, and the measurement of green and digital transitions in economic and governance systems.
The contributions collected in this volume investigate advanced methodological frameworks, empirical applications, and interdisciplinary approaches aimed at improving data-driven strategies in official statistics. By combining statistical rigour with advanced machine-learning techniques, the volume highlights both the opportunities and the challenges associated with the adoption of innovative tools, including issues related to interpretability, data quality, and the responsible use of algorithms.
Intended for researchers, practitioners, and policymakers, this Special Issue provides a rigorous and coherent overview of current developments at the intersection of artificial intelligence, spatial analysis, and sustainability measurement, contributing to the ongoing scientific debate on how official statistics can effectively support evidence-based decision-making in complex socio-economic contexts.
This volume is published within the TESI & TEMI editorial series, jointly promoted by Universitas Mercatorum and the Centro Studi delle Camere di Commercio G. Tagliacarne, and reflects their shared commitment—developed in cooperation with the Association for Applied Statistics—to fostering high-quality research and scientific dialogue in applied statistics and official data analysis.
