Population synthesis for urban resident modeling using deep generative models
This addresses urban planning and real estate analysis by providing a method to predict resident demographics, though it is incremental as it applies existing models to a specific domain.
The paper tackled modeling population distribution for new real estate developments using deep generative models, finding that Conditional Variational Auto-Encoder outperformed baselines and Conditional Generative Adversarial Networks in estimating distributions.
The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: Conditional Variational Auto-Encoder (CVAE) and Conditional Generative Adversarial Networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.