Regional Population Forecast and Analysis Based on Machine Learning Strategy

Abstract

Regional population forecast and analysis is of essence to urban and regional planning, and a well-designed plan can effectively construct a sound national infrastructure and stabilize positive population growth. Traditionally, either urban or regional planning relies on the opinions of demographers in terms of how the population of a city or a region will grow. Multi-regional population forecast is currently possible, carried out mainly on the basis of the Interregional Cohort-Component model. While this model has its unique advantages, several demographic rates are determined based on the decisions made by primary planners. Hence, the only drawback for cohort-component type population forecasting is allowing the analyst to specify the demographic rates of the future, and it goes without saying that this tends to introduce a biased result in forecasting accuracy. To effectively avoid this problem, this work proposes a machine learning-based method to forecast multi-regional population growth objectively. Thus, this work, drawing upon the newly developed machine learning technology, attempts to analyze and forecast the population growth of major cities in Taiwan. By effectively using the advantage of the XGBoost algorithm, the evaluation of feature importance and the forecast of multi-regional population growth between the present and the near future can be observed objectively, and it can further provide an objective reference to the urban planning of regional population.

Building a predictive machine learning model of gentrification in Sydney

“…Whereas previous studies have employed RFs to model gentrification (Reades et al, 2019;Palafox & Monasterio, 2020), these results demonstrate the ability of boosting methods to further improve the accuracy of predictive neighbourhood change models. These findings may have applicability to other fields of urban studies using ML modelling, such population forecasting, property price prediction, and demographic modelling (e.g., Wang & Lee, 2021;Cesare et al, 2017).…”

“…Firstly, the paper employs more powerful tree-based models than previous ML gentrification studies (Reades et al, 2019;Palafox & Monasterio, 2020). The improved performance of boosting methods over random forests enables more accurate and effective neighbourhood change predictions, while also having application in wider urban land use change problems (e.g., Wang & Lee, 2021). Furthermore, the study incorporates a novel model explanation tool that delineates the influence of each variable on the model output for specific model predictions.…”

Development of a Prediction Model on Demographic Indicators based on Machine Learning Methods: Azerbaijan Example

“…In [20] has been applied three machine learning methods such as Linear Regression model, LSTM model, and XGBoost Regression model for analyzing and forecasting the population growth of large cities in Taiwan. The prediction result of each model is compared based on MAPE.…”

Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

“…Rappaport 20 and Rodríguez‐Pose and Ketterer 21 highlight that improved infrastructure conditions facilitate attracting more people to relocate. Additionally, Wei et al 10 and Wang and Lee 22 respectively identified urbanization rate and local public expenditure as crucial factors influencing population dynamics. Secondly, the economic level provides a comprehensive reflection of factors such as GDP, income, and employment, which serve as the primary internal drivers of population growth 23 , 24 .…”