Generating Synthetic Population

TL;DR

This paper presents a machine learning-based method to generate realistic synthetic populations at multiple administrative levels in India, aiding demographic analysis and policy planning.

Contribution

It introduces a novel approach combining machine learning and statistical techniques to create detailed synthetic populations from survey data.

Findings

Synthetic populations closely match source data metrics.

Method successfully applied to multiple districts in India.

Generated data captures key demographic characteristics.

Abstract

In this paper, we provide a method to generate synthetic population at various administrative levels for a country like India. This synthetic population is created using machine learning and statistical methods applied to survey data such as Census of India 2011, IHDS-II, NSS-68th round, GPW etc. The synthetic population defines individuals in the population with characteristics such as age, gender, height, weight, home and work location, household structure, preexisting health conditions, socio-economical status, and employment. We used the proposed method to generate the synthetic population for various districts of India. We also compare this synthetic population with source data using various metrics. The experiment results show that the synthetic data can realistically simulate the population for various districts of India.