Tag: C38

MARGINALISED ZONES AS STATISTICAL INSTRUMENTS TO NAVIGATE PERMACRISIS IMPACTS IN EUROPEAN REGIONS

Cristina LINCARU

PhD, FeRSA, Department of Labour Market, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

cristina.lincaru@yahoo.de

ORCID ID: 0000-0001-6596-1820

Gabriela TUDOSE

PhD, Senior Researcher, II-nd degree, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

gabriela_tudose@yahoo.com

ORCID ID: 0000-0002-340-9987

Adriana GRIGORESCU

PhD Full Professor, SNSPA; Director of Global Economy & Governance Interdisciplinary Research Platform; AOSR; INCE; LEAD Cambridge, MA; UCLM Spain

adrianagrigorescu11@gmail.com

ORCID ID: 0000-0003-4212-6974

Speranța PÎRCIOG

PhD, Scientific Director, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

pirciog@incsmps.ro

ORCID ID: 0000-0003-0215-038X

Cristina STROE

Senior Researcher II-nd degree, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania

cristina.radu@incsmps.ro

ORCID ID: 0000-0001-8384-6084

Abstract

In the context of overlapping and interrelated crises—economic, ecological, social, and geopolitical—European regions are confronted with new governance challenges. Marginalised zones, often treated as residual spaces in policy discourse, must be reimagined as analytical and governance instruments in the transition toward sustainability and territorial resilience. This article explores how marginalised areas can be conceptualised and operationalised through spatial statistical methodologies and policy frameworks that support just transition processes. Drawing on a critical review of empirical studies and strategic European and Romanian documents, we synthesise the main tools used to identify territorial disparities, such as Principal Component Analysis (PCA), clustering algorithms, fuzzy logic, spatial econometrics, and machine learning. We confirm that these methods allow for more nuanced territorial diagnostics and typologies, which are essential for evidence-based and place-based policies. The article advances a transdisciplinary framework that repositions marginalised zones as strategic levers in adaptive territorial governance. Ultimately, we argue for a paradigm shift: from periphery to policy, where marginalised regions evolve from passive recipients of aid to active instruments of just transition.

Keywords: Marginalised regions, Just transition, Spatial inequality, Territorial resilience, Governance instruments, PCA, Clustering, Fuzzy logic, Regional typologies, Permacrisis

JEL classification: R11, R58, O18, Q56, C38

pp.155-165

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DETECTING INTERREGIONAL PATTERNS IN TOURISM SEASONALITY OF GREECE: A PRINCIPAL COMPONENTS ANALYSIS APPROACH

Dimitrios TSIOTAS

Adjunct Lecturer, Department of Regional and Economic Development, Agricultural University of Athens, Greece, Nea Poli, Amfissa, 33100, Greece, Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38334, Greece, tsiotas@aua.gr

tsiotas@uth.gr

Thomas KRABOKOUKIS

Ph.D. candidate, Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38334, Greece

tkrabokoukis@uth.gr

Serafeim POLYZOS

Professor, Department of Planning and Regional Development, University of Thessaly, Pedion Areos, Volos, 38334, Greece

spolyzos@uth.gr

Abstract

Tourism seasonality is a complex phenomenon incorporating a temporal, a spatial, and a socioeconomic (ontological) dimension. This paper builds on principal component analysis (PCA) to provide an integrated methodological framework for studying all three dimensions of tourism seasonality. The proposed method classifies the seasonal patterns of tourism demand of the Greek prefectures into regional groups, which are examined in terms of their geographical and socioeconomic characteristics. The study aims to configure distinguishable seasonal profiles in terms of their socioeconomic attributes. The proposed method is applied to monthly data of tourism overnight stays for the period 1998-2018 and detects seven principal components described by diverse socioeconomic attributes. The overall analysis proposes a useful tool for tourism management and regional policy, it advances PCA to be used as a tool of regional classification, and it incorporates a multivariate consideration based on the socioeconomic evaluation of the principal components. The proposed methodology develops an integrated framework dealing with complexity describing socioeconomic research and particularly tourism seasonality.

Keywords: regional development; seasonal classification; spatiotemporal patterns; pattern recognition.

JEL classification: C18, C38, O52, R10, R58, Z30

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PATTERNS OF MAINLY TOURISM SECTORS AT LOCAL LEVEL BY EMPLOYEE’S CHARACTERISTICS USING GIS MULTIVARIATE CLUSTERING ANALYSIS – ROMANIA CASE STUDY

Cristina LINCARU

Dr, FeRSA, Department of Labour Market, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0001-6596-1820

cristina.lincaru@yahoo.de

Speranța PÎRCIOG

Dr, Scientific Director, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0003-0215-038X

pirciog@incsmps.ro

Draga ATANASIU

Senior Researcher, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0002-9695-8592

incsmps1@incsmps.ro

Cristina STROE

Senior Researcher, Department of Social Policies, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0001-8384-6084

cristinaradu@incsmps.ro

Vasilica CIUCĂ

Dr, Dr, General Director, National Scientific Research Institute for Labour and Social Protection, Bucharest, Romania ORCID ID: 0000-0003-4687-6377

silviaciuca@incsmps.ro

Adriana GRIGORESCU

Dr., Department of Public Management, National University of Political Studies and Public Administration,  Correspondent Member of Academy of Romanian Scientists, Bucharest, Romania ORCID ID: 0000-0003-4212-6974

adrianagrigorescu11@gmail.com

Abstract

The tourism sector, before the Corona Strikes, works as a inclusive development engine for many countries’ economies and labour markets. In a global world, with increasing travel opportunities, tourism offers both labours intensive and knowledge-intensive activities, across many economic sectors. Tourism is a spatially dependent sector and also a tradable one. The Methodology for tourism statistics (Eurostat 2014),  Tourism Satellite Accounts (TSA 2010) and The International Recommendations for Tourism Statistics 2008 (IRTS 2008) differentiate the “mainly tourism” industries at four digits. We identify the natural cluster by number and pattern, at 3189 local spatial units (NUTS 5) by eight attribute variable employees: gender (male, female), age (youth, adult and aged) and education detained level (low, medium and high). Sectors are detailed at two digits only (H51- Air transport, I55 – Hotels and other accommodation facilities and N79-Activities of tourist agencies and tour operators; other reservation services and tourist assistance). Romanian National Institute of Statistics provides 2011 Census data. We apply the Multivariate Clustering Analysis with K Means algorithm as a Spatial Statistical Tool in Arc Gis Pro 2.3, an unsupervised machine learning an Artificial Intelligence technique, appropriate for Big Data. Clusters resulted illustrates natural hidden patterns of local labour markets pooling in the sense of Urban& Jacobian economies, but also some insight regarding the Morettian externalities sources. These results are useful for Regions Smart Specialisation Strategies development of human resources & talents to increase innovation capabilities and inclusive job creation, but also for a prompt recovery post-Covid Pandemic.

Keywords: tourism, labour force characteristics, Multivariate Clustering Analysis, local labour markets, regional specialisation, education level, age and gender analysis

JEL classification: J210, C38, R23

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