Abstract
Rural abandonment is a critical demographic issue that has impacted various countries across the world, including Iran, and specifically Khorasan Razavi Province. This research aims to pinpoint factors affecting rural population growth rates within this region. To overcome the limitations of the GWR model, its advanced form, known as Multiscale Geographically Weighted Regression, has been introduced. In this study, the average rural population growth rate of the province's districts between 2006 to 2016 was used as the dependent variable, and 38 variables across socio-demographic, environmental, and infrastructural sectors as independent variables. Results indicate a significant spatial autocorrelation within the rural population growth, suggesting that local regression models are more apt for examining spatial variable relationships. In analyzing local growth factors through MGWR, influencing variables included literacy rates, the average population of villages per district, and the percentage of rural health houses have had a positive impact, while variables included the percentage of the working-age population, the percentage of villages with fewer than 100 residents per district and distance from Wells have had a negative impact on rural population growth rate in the province.