Population synthesis for urban resident modeling using deep generative models

TL;DR

This paper introduces a deep generative modeling approach using CVAE and CGAN to synthesize population distributions for urban planning, demonstrating CVAE's superior performance in a real estate context.

Contribution

It applies deep generative models to urban population synthesis, comparing CVAE and CGAN, and shows CVAE's effectiveness in modeling population distributions for new developments.

Findings

CVAE outperforms CGAN and baseline models in accuracy.

Deep generative models effectively synthesize population data.

The approach aids urban planning and real estate development.

Abstract

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.