https://hdl.handle.net/10259/5645 2026-05-31T21:43:40Z https://hdl.handle.net/10259/11625 Innovating data-driven tourism reputation management: methodological foundations of the tourism online reputation index (TORI) Puche Regaliza, Julio César; Marcilla Lombraña, Isabel; Antón Maraña, Paula; Arranz Val, Pablo The widespread use of social media has significantly amplified the role of online reputations in shaping the image and competitiveness of tourism destinations. This study proposes an innovative methodology that combines big data techniques with geolocated user-generated content to develop a comprehensive tourism online reputation index (TORI). The TORI aims to quantify and monitor tourists’ perceptions of destinations in a structured and scalable way. The methodology integrates the cross-industry standard process for data mining (CRISP-DM) and knowledge discovery in databases (KDD) frameworks to ensure a rigorous, systematic approach to data collection, processing, and analysis. An ontology is developed to categorize and structure the diverse attraction points within destinations, and natural language processing (NLP) techniques are employed to perform sentiment analysis and generate tourist profiles on the basis of online reviews. The proposed methodology is validated through a case study in the province of Burgos, Spain, illustrating its practical relevance for enhancing data-driven decision-making in the context of smart tourism destinations (STDs). The results are presented through an interactive scorecard that facilitates intuitive interpretation by tourism stakeholders and supports strategic planning. From a theoretical perspective, this study contributes to the literature by offering a quantitative and standardized approach to measuring online reputation, addressing the lack of integrated tools and human-centered vision in current tourism research. In practice, it provides a replicable and adaptable solution for destination managers, particularly in rural and sparsely populated areas, to improve reputation management, support sustainable development, and strengthen destination competitiveness in the digital era. 2026-03-01T00:00:00Z https://hdl.handle.net/10259/11517 Efficient assignment of flight levels: a variable neighbourhood search approach Pacheco Bonrostro, Joaquín; Casado Yusta, Silvia; Solana Ezquerra, Mario Air traffic management (ATM) is a complex process that includes objectives such as minimising delays, costs, and environmental impacts, with safety being a top priority. Growth in air traffic over the past few decades has increased the complexity of this process. In European airspace, ATM is more crucial because of the high traffic density. A key aspect of this management strategy is the allocation of flight levels in the planning phase to avoid conflicts before takeoff. Therefore, a model is required for the allocation of flight levels in the planning phase to eliminate all conflicts and meet objectives such as minimising cost and emissions. In the absence of conflicts, planning delays in takeoff are avoided and in-flight corrective manoeuvres (such as changes in altitude or speed during flights) are reduced, reducing the workload of the controller. This study proposed a method based on a metaheuristic variable neighbourhood search (VNS) strategy on a multistart framework. Various experiments with simulated instances demonstrated that this method improves the results obtained using commercial software. In addition, tests based on real data (both in Europe and Spain) showed satisfactory results in terms of economics and sustainability. 2025-12-01T00:00:00Z https://hdl.handle.net/10259/11496 Iterative Stepped Search algorithm for the clique partitioning problem Solana Ezquerra, Mario; Pacheco Bonrostro, Joaquín; Casado Yusta, Silvia The dataset is a set of txt files with the fictitious instaces used in the paper "Iterative Stepped Search algorithm for the clique partitioning problem" The dataset is a text file containing the solutions obtained in the article “Iterative Stepped Search Algorithm for the Clique Partitioning Problem” for a set of instances from the DIMACS library. 2026-03-21T00:00:00Z https://hdl.handle.net/10259/11478 Spanish Provinces Banking and Tourism Panel Dataset (2015-2024) Moreno Molina, Adrián; Antón Maraña, Paula; Lima Santos, Luís; Puche Regaliza, Julio César This dataset comprises annual panel data for 52 Spanish provinces (NUTS-3) from 2015 to 2024, supporting the analysis in the manuscript "The Impact of Banking Deserts on Tourism Development: Evidence from Spanish Provinces." It includes 13 key variables on banking consolidation (e.g., HHI concentration, offices, firms_bank, workers_bank), provincial controls (gdp_pc, int_rate, unemploy, density), and tourism outcomes (credit_tour, firms_tour, workers_tour, overnight, travellers). 2026-03-12T00:00:00Z