https://economyinformatics.ase.ro/ojs/index.php/eij/issue/feed 2024-07-03T20:50:31+00:00 Economy & Informatics Journal Board editor@economyinformatics.ase.ro Open Journal Systems <p class="style5 style4" align="center"> </p> <p class="style5 style4" align="center">Open Access Journal published by INFOREC Association.</p> <p class="style4 style5" align="center">Founded by members of the<br />Economic Informatics Department,<br />Faculty of Cybernetics, Statistics and Economic Informatics<br />Bucharest University of Economic Studies, Romania</p> <p class="style4 style5" align="center">ISSN 1582-7941<br />EISSN 2247-8523</p> <p class="style4 style5" align="center">Published with the support of Ministry of Education, Research and Innovation</p> <p class="style5 style4" align="center">CNCSIS B+ Category<br /><br />Listed in:<br /><a href="http://www.cabells.com/" target="_blank" rel="noopener">Cabell's Directories of Publishing Opportunities</a><br /><a href="http://www.doaj.org/openurl?genre=journal&amp;issn=15827941" target="_blank" rel="noopener">Directory of Open Access Journal</a><br /><a href="http://www.ebscohost.com/" target="_blank" rel="noopener">EBSCO</a><br /><a href="http://journals.indexcopernicus.com/index.php" target="_blank" rel="noopener">Index Copernicus</a><br /><a href="http://lamp.infosys.deakin.edu.au/journals/" target="_blank" rel="noopener">Index of Information Systems Journals</a><br /><a href="http://proquest.com/" target="_blank" rel="noopener">ProQuest</a></p> https://economyinformatics.ase.ro/ojs/index.php/eij/article/view/13 2024-07-03T20:50:31+00:00 Denis-Cătălin Arghir denis.arghir@ie.ase.ro

<p>This paper introduces an adaptable framework to facilitate comprehensive multidimensional analyses to assess regional resilience. The application of this framework is used for a case study in conceiving and synthesizing a suite of indicators tailored for Romanian counties' resilience assessment. These indicators are categorized into four distinct dimensions: "Socio-Economic Dynamics", "Urban Infrastructure and Green Areas", "Governance and Industry", and "Material and Energy Flows". Employing Principal Component Analysis (PCA), this study strategically condenses the dataset, emphasizes inter-variable relationships, and extracts valuable insights, thereby enabling an unbiased assignment of weights. The classification leverages discriminant variables to forge composite indicators for each dimension, creating an overall score and comparative counties' rankings. Additionally, combining PCA with k-means cluster-ing simplifies data interpretation through categorical grouping. The analysis results are en-riched using an interactive geographic information system (GIS) representation, vividly portraying regional disparities and commonalities across Romanian counties.</p>

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