[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119312-en":3,"doc-seo-119312-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119312,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Forecasting First-Year Student Mobility Using Explainable Machine Learning Techniques","Forecasting spatial student mobility in regional science and migration studies often relies on gravity and radiation models embedded in regression frameworks with covariates. Non-linear correlations, complex spatial interactions, and multicollinearity make model interpretation and prediction challenging. The study applies XGBoost to capture spatial interactions, providing statistics and visualizations for evaluation and for interpreting independent variables. Results for Germany’s county-level high-school to university transitions show explainable machine learning performance comparable to traditional regression, with SHAP revealing differentiated non-linear variable effects, including mixed impacts of local labor-market academic employment rates.","Review of Regional Research (2024) 44:119–140 [https://doi.org/10.1007/s10037-024-00207-x](https://doi.org/10.1007/s10037-024-00207-x)  \nORIGINAL PAPER  \nForecasting first-year student mobility using explainable machine learning techniques  \nMarie-Louise Litmeyer1  · Stefan Hennemann1  \nAccepted: 9 February 2024 / Published online: 21 February 2024 © The Author(s) 2024  \nAbstract In the context of regional sciences and migration studies, gravity and radiation models are typically used to estimate human spatial mobility of all kinds. These formal models are incorporated as part of regression models along with co-variates, to better represent regional specific aspects. Often, the correlations between dependent and independent variables are of non-linear type and follow complex spatial interactions and multicollinearity. To address some of the model-related obstacles and to arrive at better predictions, we introduce machine learning algorithm class XGBoost to the estimation of spatial interactions and provide useful statistics and visual representations for the model evaluation and the evaluation and interpretation of the independent variables. The methods suggested are used to study the case of the spatial mobility of high-school graduates to the enrolment in higher education institutions in Germany at the county-level. We show that machine learning techniques can deliver explainable results that compare to traditional regression modeling. In addition to typically high model fits, variable-based indicators such as the Shapley Additive Explanations value (SHAP) provide significant additional information on the differentiated and non-linear effect of the variable values. For instance, we provide evidence that the initial study location choice is not related to the quality of local labor-markets in general, as there are both, strong positive and strong negative effects of the local academic employment rates on the migration decision. When  \n􀀂 Marie-Louise Litmeyer [Marie-Louise.Litmeyer@geogr.uni-giessen.de](Marie-Louise.Litmeyer@geogr.uni-giessen.de)  \nStefan Hennemann  \n[Stefan.Hennemann@geogr.uni-giessen.de](Stefan.Hennemann@geogr.uni-giessen.de)  \n1 Economic Geography, Department of Geography, Justus Liebig University Giessen, Senckenbergstr. 1, 35390 Gießen, Germany  \nK  \ncontrolling for about 28 co-variates, the attractiveness of the study location itself is the most important single factor of influence, followed by the classical distancerelated variables travel time (gravitation) and regional opportunities (radiation) . We show that machine learning methods can be transparent, interpretable, and explainable, when employed with adequate domain-knowledge and flanked by additional calculations and visualizations related to the model evaluation.  \nKeywords Spatial Mobility · High School-to-University Transition · Machine Learning · Gravitation Model · Radiation Model  \n1 Introduction  \nSince the early 2000s, the number of students in Germany has increased significantly more than predicted in forecasts (see, Nutz 1991; KMK 2005; Gösta and von Stuckrad 2007; Wissenschaftliche Dienste des Deutschen Bundestages 2006 ; Multrus et al. 2017) . Reasons for this are the politically desired expansion of higher education offerings, the rising high school graduation rate, the introduction of the bachelor’s/master’s system, the abolition of compulsory military service, and double high school graduation cohorts. While the deviations of the total predictions are often hard to comprehend due to the effect size of such non-predictable political decisions, it is of great importance for decision makers to forecast spatial patterns of student mobility, since the basic funding is strongly related to the enrolment (HMWK 2015) .  \nGravity models are typically used to forecast student migration (Sá et al. 2004 ; Alm and Winters 2009; Cooke and Boyle 2011; Faggian and Franklin 2014) . However, these models have some weaknesses, for example, empirical data are ","cbCail27NzgsH4cM","https://ap.wps.com/l/cbCail27NzgsH4cM","pdf",1413335,1,22,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Gravity and radiation models in student mobility forecasting\n## Machine learning and explainable modeling\n# Methods and evaluation approach","[{\"question\":\"Why are gravity and radiation models used for forecasting student mobility?\",\"answer\":\"They estimate spatial human mobility and are integrated into regression models with covariates to capture regional aspects of migration patterns.\"},{\"question\":\"What machine learning technique is proposed to address modeling obstacles?\",\"answer\":\"The study introduces XGBoost to estimate spatial interactions and improve prediction while enabling evaluation and interpretation.\"},{\"question\":\"How do explainability tools contribute to the findings?\",\"answer\":\"SHAP provides variable-based indicators that expose differentiated, non-linear effects, helping interpret drivers of migration decisions beyond overall model fit.\"}]","Forecasting First-Year Student Mobility Using Explainable Machine Learning Techniques | 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are gravity and radiation models used for forecasting student mobility?","Question",{"text":75,"@type":76},"They estimate spatial human mobility and are integrated into regression models with covariates to capture regional aspects of migration patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning technique is proposed to address modeling obstacles?",{"text":80,"@type":76},"The study introduces XGBoost to estimate spatial interactions and improve prediction while enabling evaluation and interpretation.",{"name":82,"@type":73,"acceptedAnswer":83},"How do explainability tools contribute to the findings?",{"text":84,"@type":76},"SHAP provides variable-based indicators that expose differentiated, non-linear effects, helping interpret drivers of migration decisions beyond overall model 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