[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123362-en":3,"doc-seo-123362-105":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":4,"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},123362,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Introduction to the special issue on spatial machine learning","Spatial machine learning applies machine learning and artificial intelligence methods to spatial data and spatially explicit research questions, extending a recent wave of attention in GIScience and related fields. This editorial sets an inclusive definition for spatial ML, summarizes each of the six papers included in the special issue, and clarifies how spatial ML connects to or differs from non-spatial ML and GeoAI. It concludes by outlining several promising directions for future research.","Journal of Geographical Systems (2024) 26:451–460 [https://doi.org/10.1007/s10109-024-00452-1](https://doi.org/10.1007/s10109-024-00452-1)  \nORIGINAL ARTICLE  \nIntroduction to the special issue on spatial machine learning  \nKevin Credit1  \nReceived: 30 October 2024 / Accepted: 30 October 2024 / Published online: 15 November 2024  \n© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024  \nAbstract  \nWhile, many of the machine learning (ML) and artificial intelligence (AI) methods that are now commonly being used to answer questions across scientific disciplines have been around for some time, their widespread application to spatial data and spatially-explicit research questions is much more recent. The large number of excellent review papers and special issues in leading journals published in the last few years—which this issue of the Journal of Geographical Systems takes its place among—attest to the growing interest in the application and development of cuttingedge methodologies for spatial data. This editorial begins by proposing a new inclusive definition for spatial ML, then provides a brief overview of each of the six papers in this special issue, and ends with a suggestion of several possible directions for future research in spatial ML.  \nKeywords Spatial machine learning · Spatial data · Spatially-explicit models · GeoAI · Random forest  \nJEL Classification C14 · C18 · C21 · C45  \n1 Background  \nWhile many of the machine learning (ML) and artificial intelligence (AI) methods that are now commonly being used to answer questions across scientific disciplines have been around for some time (Rosenblatt 1958 ; Amari 1967 ; Openshaw and Openshaw 1997 ; Breiman 2001), their widespread application to spatial data and spatially-explicit research questions is much more recent. The large number of excellent review papers and special issues in leading GIScience journals published in the last few years (e.g., Janowicz et al. 2019 ; Nikparvar and Thill 2021 ; Kopczewska 2022; Papadakis et al. 2022)—which this issue of the Journal of Geographical  \n* Kevin Credit [kevin.credit@mu.ie](kevin.credit@mu.ie)  \n1 Maynooth University, National University of Ireland Maynooth, Maynooth, Ireland  \nSystems takes its place among—attest to the growing interest in the application and development of cutting-edge methodologies for spatial data. This is of course in part due to the increasing volume, velocity, and variety of spatial data available for analysis, which require more powerful computation tools to analyse (Kitchin and McArdle 2016); it is also likely due to the demonstrated improvement in predictive performance for these methods compared to traditional statistical techniques (Hagenaueret al. 2019 ; Yoshida and Seya 2021; Credit 2022) .  \n2 Defining spatial machine learning  \nHowever, despite (or, perhaps, due to) the increasing attention paid to new methods in the literature, we lack a coherent conceptual paradigm for discussing or defining what we mean when we talk about spatial machine learning: in other words, what methods and domains are included (and excluded)? How is spatial machine learning different than (or the same as) non-spatial ML or geographic artificial intelligence (GeoAI)? While, others have already effectively traced the history of the development of AI and ML methods in a spatial context (Janowicz et al. 2019 ; Hu et al. 2024) and classified and categorised the use of existing ML and AI methods for analysing spatial data (Nikparvar and Thill 2021 ; Kopczewska 2022), there is still a need to develop a cohesive and inclusive definition for “spatial machine learning”that provides a framework for describing the wide range of new methodological work in GIScience and allied fields, and, more specifically, the content and goals of this special issue.  \nTo provide a useful definition of spatial ML, several terms need to be disentangled. First, machine learning vs. artificial intell","cbCaihueeI62j1qJ","https://ap.wps.com/l/cbCaihueeI62j1qJ","pdf",545661,1,10,"English","en",105,"# Introduction\n## Background\n## Defining spatial machine learning\n## Machine learning vs. artificial intelligence\n## Spatial ML as a cohesive definition","[{\"question\":\"What makes spatial machine learning different from non-spatial machine learning?\",\"answer\":\"Spatial machine learning focuses on methods applied to spatial data and spatially explicit questions, requiring a conceptual framework that distinguishes which methods and domains are included or excluded.\"},{\"question\":\"How does the editorial distinguish machine learning from artificial intelligence?\",\"answer\":\"AI is described as the broad field of developing computational systems that achieve novel goals, while machine learning is treated as a subset dealing with algorithms that identify patterns, make decisions, and improve with data and experience.\"},{\"question\":\"What does the editorial do besides defining spatial machine learning?\",\"answer\":\"It provides a brief overview of the six papers in the special issue and ends with suggested directions for future research in spatial ML.\"}]","Introduction to the special issue on spatial machine learning | 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makes spatial machine learning different from non-spatial machine learning?","Question",{"text":75,"@type":76},"Spatial machine learning focuses on methods applied to spatial data and spatially explicit questions, requiring a conceptual framework that distinguishes which methods and domains are included or excluded.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the editorial distinguish machine learning from artificial intelligence?",{"text":80,"@type":76},"AI is described as the broad field of developing computational systems that achieve novel goals, while machine learning is treated as a subset dealing with algorithms that identify patterns, make decisions, and improve with data and experience.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the editorial do besides defining spatial machine learning?",{"text":84,"@type":76},"It provides a brief overview of the six papers in the special issue and ends with suggested directions for future research in spatial 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