Publicação
The Spillover Effects of Lisbon’s Gentrification up to 2041: Assessing Demographic Trends in the Setúbal Peninsula Through a Business Intelligence Approach
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Rodrigues, Teresa Maria Ferreira | |
| dc.contributor.author | Scherer, Bernardo Moraes | |
| dc.date.accessioned | 2025-11-07T16:18:49Z | |
| dc.date.available | 2025-11-07T16:18:49Z | |
| dc.date.issued | 2025-10-28 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence | pt_PT |
| dc.description.abstract | In the past years, Lisbon has been undergoing an intensive process of gentrification, contributing to the displacement of long-term residents and increased living costs, generating a sub-process of the phenomena into the suburban areas, in a cascading effect. This research aims to understand to what extent are the Lisbon’s gentrification-driven pressures shaping and influencing the demographic dynamics of the Setúbal Peninsula, and how to strategically measure and display the region’s key demographic indicators to foresight upcoming infrastructural pressures. Thus, the research focuses on (1) assessing the current demographic structure and evolution of the Setúbal Peninsula, analyzing key indicators, (2) defining and developing KPIs to quantitatively measure structural changes potentially driven by spillover pressures and (3) conducting a foresight analysis to project the region’s population up to 2041, considering three different scenarios through the application of the Cohort-Component Method, aiming to compare possible future demographic shifts and their implications for regional planning and urban livability. Aiming to accomplish the established goals, the research follows the Design Science Research Methodology (DSRM), in which an end-to-end BI solution is designed and developed within the Microsoft Fabric environment, in order to serve as an analytical tool for supporting data-driven urban planning, displaying meaningful KPIs regarding the demographic trends in the Setúbal Peninsula, and generating high value by providing accurate and straightforward answers to the research objectives while also enabling truthful data-driven decision making in the core of the demographic field. Research findings suggest that the demographic trends in the Setúbal Peninsula will be showing signs of deceleration after 2036, representing a saturation of absorptive capacity and growing infrastructural pressures, highlighting the symptoms of a cascading gentrification effect, with the Peninsula inheriting the same issues once dominant in the city of Lisbon. It reinforces the importance of regional urban planning and sustainable urban policies, in order to anticipate and mitigate these negative and inevitable externalities, ensuring an acceptable level of urban livability in the affected areas. | pt_PT |
| dc.identifier.tid | 204074126 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190292 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Gentrification | pt_PT |
| dc.subject | Suburbanization | pt_PT |
| dc.subject | Setúbal Peninsula | pt_PT |
| dc.subject | Demographic Trends | pt_PT |
| dc.subject | Business Intelligence | pt_PT |
| dc.subject | Cohort-Component Method | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 10 - Reduced inequalities | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.title | The Spillover Effects of Lisbon’s Gentrification up to 2041: Assessing Demographic Trends in the Setúbal Peninsula Through a Business Intelligence Approach | 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 Gestão de Informação, especialização em Business Intelligence | pt_PT |