Encoding social values of local communities in algorithmic-driven design methods

This study tackles one important but frequently ignored aspect of the housing cri-sis in the UK. We propose a computational method to encode socio-cultural val-ues and aspirations of residents in future housing developments.
In this work, we address the question of: how resident aspirations, values, and living qualities of local communities can be encoded in algorithmic-driven design methods to ensure that the future UK housing design and developments will ad-dress resilient, net-zero and sustainable communities?
To address this, we developed a model that generates housing urban configura-tions (massing) using a combination of factors that encode a combination of (i) residents’ social aspirations, (ii) housing regulations and policy-makers agendas, and (iii) successful elements for resilient communities. We suggest a statistical lin-ear method to inform the choice of inputs and suggest weights. The model com-bines these factors using a multi-objective optimisation strategy implemented through Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find the best trade-offs among competing factors.
We present our final model with some testing and explain how the model can be used by designers (and potentially by city councils and developers) to include so-cial aspects of local communities in the planning of new schemes and evaluation of existing ones.
The presented model can be used to consider multi and diverse communities in-to data-driven approaches to ensure that future housing schemes strongly cater for resilient, net-zero and sustainable communities. This aligns with UN and UK targets for net-zero communities, housing and sustainable living.