{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:20:14Z","timestamp":1790072414267,"version":"4.0.1"},"reference-count":180,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2017-05950"],"award-info":[{"award-number":["RGPIN-2017-05950"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative\/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields\u2014bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.<\/jats:p>","DOI":"10.3390\/ijgi12010012","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T02:12:48Z","timestamp":1672625568000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Spatial Decision Support Systems with Automated Machine Learning: A Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8904-8152","authenticated-orcid":false,"given":"Richard","family":"Wen","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8244-5681","authenticated-orcid":false,"given":"Songnian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"04020007","DOI":"10.1061\/(ASCE)UP.1943-5444.0000566","article-title":"Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods","volume":"146","author":"Niu","year":"2020","journal-title":"J. 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