[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119444-en":3,"doc-seo-119444-105":30,"detail-sidebar-cat-0-en-105":95},{"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},119444,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Forecasting first-year student mobility using explainable machine learning techniques - Original paper","The study addresses forecasting first-year student mobility in the context of regional sciences and migration research, where gravity and radiation models are commonly used but can struggle with non-linear relationships, spatial interactions, and multicollinearity. It applies XGBoost to estimate spatial interactions and adds evaluation and interpretation tools, including SHAP values. Using high-school graduates’ transition to higher education in Germany at county level, the approach yields explainable results comparable to traditional regression while revealing variable-specific, differentiated effects. The location choice emerges as the dominant factor, alongside travel time and regional opportunities, supported by transparent model evaluation.","Litmeyer, Marie-Louise; Hennemann, Stefan  \nArticle — Published Version  \nForecasting first-year student mobility using explainable machine learning techniques  \nReview of Regional Research  \nProvided in Cooperation with:  \nSpringer Nature  \nSuggested Citation: Litmeyer, Marie-Louise; Hennemann, Stefan (2024) : Forecasting first-year student mobility using explainable machine learning techniques, Review of Regional Research, ISSN 1613-9836, Springer, Berlin, Heidelberg, Vol. 44, Iss. 1, pp. 119-140, [https://doi.org/10.1007/s10037-024-00207-x](https://doi.org/10.1007/s10037-024-00207-x)  \nThis Version is available at:  \n[https://hdl.handle.net/10419/315058](https://hdl.handle.net/10419/315058)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \n[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)  \nReview 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@","cbCaib0mbkPfoGFB","https://ap.wps.com/l/cbCaib0mbkPfoGFB","pdf",1533328,1,23,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What problem does the study address?\",\"answer\":\"It addresses how to forecast first-year student mobility in regional science and migration settings where traditional gravity and radiation regression models may not capture complex non-linear spatial relationships well.\"},{\"question\":\"Which machine learning method and explainability approach are used?\",\"answer\":\"The study uses XGBoost and relies on SHAP (Shapley Additive Explanations) to provide variable-based indicators and interpretable, differentiated effects.\"},{\"question\":\"How is the method tested in the paper?\",\"answer\":\"It investigates spatial mobility of high-school graduates to enrollment in higher education institutions in Germany, using county-level data.\"},{\"question\":\"What do the results suggest about drivers of migration decisions?\",\"answer\":\"They indicate that the initial study location choice is a key influence, with travel time and regional opportunities also playing roles, and with effects that can be both strongly positive and strongly negative for local academic employment rates.\"}]","Forecasting first-year student mobility using explainable machine learning techniques - 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