Cluster analysis for the derivation of agents for ABMs in the context of an ageing , super-diverse population : a mixed-methods approach
Haacke H, Enssle F, Helbrecht I, Lakes T, Walker B (2018)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2018
Event location: Lund
Abstract
Across Europe,the population is simultaneously ageing andbecoming more socially diverse. However, the intersection between super-diversityand ageing has been largely absent from the literature, despite its importancefor urban and social planning. Agent-Based Modelling (ABM)comprises a commonly used method to simulateand analyse urban development in the context of demographic change, but suffers from high sensitivity to parameterisation.We therefore propose a mixed-methods approach to developing agents for the purpose of ABM, using hierarchical cluster analysis,expert interviews, and focus groups. Thequantitativeand qualitative results are compared and contrasted to derive empirical agents. A geospatial dataset is then used to explore the feasibility of assigning multimethod-derived agents to a ‘home location’. Ourstatistical results exhibited a high level of agreement with the qualitative analysis, while the derivation of agent locations presented some unique methodological challenges meriting further research. We conclude by asserting the utility of mixed-methods for deriving agents to be used in population modelling.
Authors with CRIS profile
Involved external institutions
Germany (DE)How to cite
APA:
Haacke, H., Enssle, F., Helbrecht, I., Lakes, T., & Walker, B. (2018). Cluster analysis for the derivation of agents for ABMs in the context of an ageing , super-diverse population : a mixed-methods approach. In Proceedings of the AGILE 2018. Lund, SE.
MLA:
Haacke, Hannah, et al. "Cluster analysis for the derivation of agents for ABMs in the context of an ageing , super-diverse population : a mixed-methods approach." Proceedings of the AGILE 2018, Lund 2018.
BibTeX: Download