Spatial Multi-Criteria Decision Analysis for Rehabilitation Priority Ranking: A Collaborative Application to Heritage Workforce Housing Sites

ABSTRACT

This work presents a methodology to rank heritage sites regarding rehabilitation, considering both the characteristics of building sites and of the urban environment in the surrounding area. The objective is to aid the decision process of building rehabilitation by ranking the sites according to their potential for re-emergence in the affordable housing rental market. The developed methodology is based on a combination of multi-criteria decision analysis (MCDA) and spatial analysis of geographical data, in order to construct an index, the “rehabilitation potential”, which is understandable by rehabilitation technicians and land managers and is applicable to support a list of priorities of building rehabilitation interventions. The methodology was applied to a case study consisting of a set of 33 heritage sites of the workforce housing typology in Lisbon. These were built in the early industrial age in Portugal and are owned by the city municipality. The application of MCDA was a collaborative process that brought together the expertise of the academy and of the public administration. The results included a sensitivity analysis and gave form to a recommendation of five sites, selected from the total workforce housing set, to be rehabilitated in the near future.

KEYWORDS:

• geographical information systems
• heritage rehabilitation and management
• rehabilitation potential index
• spatial multi-criteria decision analysis
• workforce housing

Highlights

• Methodology for ranking heritage sites for rehabilitation according to market potential.

• MCDA was applied to assess each site from Building and Urban Context points of view.

• Descriptors of many of the sub-criteria are spatial indicators calculated with GIS.

• Case-study includes 33 sites of Lisbon municipality-managed historic workforce housing estate.

• Five sites were recommended to be rehabilitated in the near future.

Acknowledgements

This project was funded by the CERIS research centre of Instituto Superior Técnico, Universidade de Lisboa. The authors are grateful to André Sousa for his valuable help in defining the value functions and Arch. João Gomes-Teixeira, Eng. Isabel Genro and Eng. Marta Cardoso from Lisbon Municipality (Câmara Municipal de Lisboa) for providing data and sharing their expert knowledge in this study. From Instituto Superior Técnico, the authors thank Professors Rui Oliveira for the assistance on multi-criteria modelling and João Abreu e Silva for the discussions on transport accessibility. The authors would also like to thank the thorough reviews that helped to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the CERIS [BL177/2016].