Publicação
Analysis of Monetary Transactions in Oeste CIM: Insights into Tourism Spending Patterns and Economic Impact
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Jardim, João Bruno Morais de Sousa | |
| dc.contributor.advisor | Rodrigues, Duarte Nuno Antunes Caracol Barros | |
| dc.contributor.author | Spagnol, Alexandre Pires | |
| dc.date.accessioned | 2025-11-05T11:56:36Z | |
| dc.date.available | 2025-11-05T11:56:36Z | |
| dc.date.issued | 2025-10-27 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics | pt_PT |
| dc.description.abstract | This study addresses a significant gap in microeconomic tourism analysis for small regions by developing a scalable, data-driven framework for Oeste CIM, Portugal, a region heavily reliant on tourism and subject to seasonal economic fluctuations. Utilizing transactional records from 2021 to 2024, we implemented a Lakehouse architecture with Kimball dimensional modelling to facilitate parish-level spending analysis. Automated ETL pipelines process payment data, and CatBoost forecasting models predict tourism trends with a mean absolute percentage error (MAPE) of less than 15%, surpassing traditional methods. The integrated Power BI dashboard reveals critical microeconomic insights: weekends account for 70% of spending, and sectors such as "Petrol Stations" and "Supermarkets" exhibit high economic resilience. The findings validate the effectiveness of machine learning for forecasting in small regions despite data limitations and provide stakeholders with actionable tools for resource optimization. This framework transforms transactional data into strategic insights, enabling municipalities and businesses to mitigate seasonal risks, enhance infrastructure planning, and advance the Sustainable Development Goals (SDG 8–9). This solution offers a replicable blueprint for data-driven urban planning in tourism economies globally. | pt_PT |
| dc.identifier.tid | 204071950 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190125 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | smart tourism | pt_PT |
| dc.subject | smart region | pt_PT |
| dc.subject | forecasting | pt_PT |
| dc.subject | microeconomic indicators | pt_PT |
| dc.subject | business intelligence | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.title | Analysis of Monetary Transactions in Oeste CIM: Insights into Tourism Spending Patterns and Economic Impact | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Business Analytics | pt_PT |