Publicação
Dashboards for Urban Incident Reporting: Improving Decision Support with Power BI Using Na Minha Rua Lx Data
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| datacite.subject.sdg | 16:Paz, Justiça e Instituições Eficazes | |
| dc.contributor.advisor | Neves, Maria de Fátima dos Santos Trindade | |
| dc.contributor.author | Ramos, João Pedro Brito | |
| dc.date.accessioned | 2026-04-21T11:06:08Z | |
| dc.date.available | 2026-04-21T11:06:08Z | |
| dc.date.issued | 2026-04-14 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence | |
| dc.description.abstract | This dissertation addresses the translation gap between raw citizen-reported urban incident data and structured decision support in municipal governance. Focusing on Lisbon’s Na Minha Rua LX platform, it investigates how Business Intelligence dashboards, grounded in disciplined dimensional modelling, can enhance decision support for managing urban incident reporting. Guided by the Design Science Research paradigm and informed by the Kimball methodology, the study designs and implements a dimensional data warehouse and a governed Power BI semantic layer. A declared analytical grain and a clear bus matrix with conformed dimensions (Date, Location, Incident Type, and Channel) ensure semantic consistency across dashboards. Data pipelines implemented in Microsoft Fabric follow a Medallion (Bronze–Silver–Gold) architecture to guarantee lineage, data quality, and traceability. At the core, a star schema supports interpretable measures for daily incident volumes, seasonal baselines, anomaly detection, territorial pressure, and weather sensitivity. Empirical analysis reveals structured regularities in Lisbon’s incident reporting dynamics, including stable seasonal cycles, persistent territorial concentration, category-level asymmetries, and directional associations with rainfall in selected domains. These patterns are interpreted descriptively rather than causally and illustrate how dimensional discipline enables consistent detection and contextualisation of analytical signals. Evaluation combines scenario-based walkthroughs and artefact assessment criteria to examine business coverage, interpretability, traceability, semantic consistency, and usability. Findings indicate that improvements in decision support arise primarily from structural clarity rather than analytical complexity. The study demonstrates that a governed semantic layer and a conformant dimensional architecture can transform open citizen-reporting data into reproducible, interpretable, and action-oriented dashboards without relying on opaque predictive models. The dissertation contributes a reusable dimensional representation of urban incident reporting, documented implementation patterns that connect governance principles to analytical design, and empirical evidence that semantic discipline is a foundational enabler of reliable decision support in smart-city contexts. | eng |
| dc.identifier.tid | 204297281 | |
| dc.identifier.uri | http://hdl.handle.net/10362/202410 | |
| dc.language.iso | eng | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Business Intelligence (BI) | |
| dc.subject | Dimensional Data Warehousing | |
| dc.subject | Urban Incident Reporting | |
| dc.subject | Decision Support Systems (DSS) | |
| dc.subject | Smart Cities | |
| dc.subject | Microsoft Power BI | |
| dc.title | Dashboards for Urban Incident Reporting: Improving Decision Support with Power BI Using Na Minha Rua Lx Data | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Business Intelligence |