Population Synthesis via k-Nearest Neighbor Crossover Kernel
This addresses the need for accurate population synthesis in multi-agent simulations, which is crucial for urban planning and policy analysis, though it appears incremental as an extension of classical estimators.
The paper tackles the problem of reconstructing entire populations from limited survey samples (e.g., 1%) for multi-agent simulations by introducing a new kernel density estimator. It demonstrates the method's effectiveness through real and synthetic datasets, including a household synthesis task for an urban micro-simulator.
The recent development of multi-agent simulations brings about a need for population synthesis. It is a task of reconstructing the entire population from a sampling survey of limited size (1% or so), supplying the initial conditions from which simulations begin. This paper presents a new kernel density estimator for this task. Our method is an analogue of the classical Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the huge degree of freedom which is required to model high-dimensional nonlinearly correlated datasets: the crossover kernel, the k-nearest neighbor restriction of the kernel construction set and the bagging of kernels. The performance as a statistical estimator is examined through real and synthetic datasets. We provide an "optimization-free" parameter selection rule for our method, a theory of how our method works and a computational cost analysis. To demonstrate the usefulness as a population synthesizer, our method is applied to a household synthesis task for an urban micro-simulator.