{"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/-maps-from-spatio-temporal-data-to-a-weighted","title":"δ-MAPS: From spatio-temporal data to a weighted and lagged network between functional domains","arxiv_id":"1602.07249","date":"2016-02-23","proceeding":null,"authors":["Ilias Fountalis","Annalisa Bracco","Bistra Dilkina","Constantine Dovrolis","Shella Keilholz"],"abstract":"We propose {\\delta}-MAPS, a method that analyzes spatio-temporal data to first identify the distinct spatial components of the underlying system, referred to as \"domains\", and second to infer the connections between them. A domain is a spatially contiguous region of highly correlated temporal activity. The core of a domain is a point or subregion at which a metric of local homogeneity is maximum across the entire domain. We compute a domain as the maximum-sized set of spatially contiguous cells that include the detected core and satisfy a homogeneity constraint, expressed in terms of the average pairwise cross-correlation across all cells in the domain. Domains may be spatially overlapping. Different domains may have correlated activity, potentially at a lag, because of direct or indirect interactions. The proposed edge inference method examines the statistical significance of each lagged cross-correlation between two domains, infers a range of lag values for each edge, and assigns a weight to each edge based on the covariance of the two domains. We illustrate the application of {\\delta}-MAPS on data from two domains: climate science and neuroscience.","url_abs":"http://arxiv.org/abs/1602.07249v3","url_pdf":"http://arxiv.org/pdf/1602.07249v3.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"-maps-from-spatio-temporal-data-to-a-weighted","repo_url":"https://github.com/FabriFalasca/delta-MAPS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}