{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatio-temporal-data-mining-a-survey-of","title":"Spatio-Temporal Data Mining: A Survey of Problems and Methods","arxiv_id":"1711.04710","date":"2017-11-13","proceeding":null,"authors":["Gowtham Atluri","Anuj Karpatne","Vipin Kumar"],"abstract":"Large volumes of spatio-temporal data are increasingly collected and studied\nin diverse domains including, climate science, social sciences, neuroscience,\nepidemiology, transportation, mobile health, and Earth sciences.\nSpatio-temporal data differs from relational data for which computational\napproaches are developed in the data mining community for multiple decades, in\nthat both spatial and temporal attributes are available in addition to the\nactual measurements/attributes. The presence of these attributes introduces\nadditional challenges that needs to be dealt with. Approaches for mining\nspatio-temporal data have been studied for over a decade in the data mining\ncommunity. In this article we present a broad survey of this relatively young\nfield of spatio-temporal data mining. We discuss different types of\nspatio-temporal data and the relevant data mining questions that arise in the\ncontext of analyzing each of these datasets. Based on the nature of the data\nmining problem studied, we classify literature on spatio-temporal data mining\ninto six major categories: clustering, predictive learning, change detection,\nfrequent pattern mining, anomaly detection, and relationship mining. We discuss\nthe various forms of spatio-temporal data mining problems in each of these\ncategories.","url_abs":"http://arxiv.org/abs/1711.04710v2","url_pdf":"http://arxiv.org/pdf/1711.04710v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatio-temporal-data-mining-a-survey-of","repo_url":"https://github.com/devbas/ovassistant-alpha","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"epidemiology","task_name":"Epidemiology"},{"task_slug":"survey","task_name":"Survey"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}