{"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/clustering-to-reduce-spatial-data-set-size","title":"Clustering to Reduce Spatial Data Set Size","arxiv_id":"1803.08101","date":"2018-03-21","proceeding":null,"authors":["Geoff Boeing"],"abstract":"Traditionally it had been a problem that researchers did not have access to\nenough spatial data to answer pressing research questions or build compelling\nvisualizations. Today, however, the problem is often that we have too much\ndata. Spatially redundant or approximately redundant points may refer to a\nsingle feature (plus noise) rather than many distinct spatial features. We use\na machine learning approach with density-based clustering to compress such\nspatial data into a set of representative features.","url_abs":"http://arxiv.org/abs/1803.08101v1","url_pdf":"http://arxiv.org/pdf/1803.08101v1.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":"clustering-to-reduce-spatial-data-set-size","repo_url":"https://github.com/gboeing/urban-data-science","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}