{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MRAVWJ2Q7EVAVJ2VMG6SFXXOQG","short_pith_number":"pith:MRAVWJ2Q","schema_version":"1.0","canonical_sha256":"64415b2750f92a0aa75561bd22deee819ed05a7f103714ebfe588c568017f8c1","source":{"kind":"arxiv","id":"2605.18777","version":1},"attestation_state":"computed","paper":{"title":"XFlowMap: Cross-Scale Generalization and Mapping of Massive Origin-Destination Data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.SI","authors_text":"Diansheng Guo, Hai Jin","submitted_at":"2026-04-23T07:52:46Z","abstract_excerpt":"Mapping large origin-destination (OD) datasets remains challenging because flow maps become cluttered, meaningful patterns occur at multiple spatial scales, and existing flow-mapping approaches frequently rely on predefined aggregation units or manual generalization. This paper presents XFlowMap, a framework for the cross-scale generalization and mapping of massive OD data. Specifically, the framework integrates cross-scale flow pattern (cluster) detection, automated flow map generalization, and a new cartographic representation for analyzing and visualizing complex origin-destination flow str"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.18777","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.SI","submitted_at":"2026-04-23T07:52:46Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"35edd8f110b40f244a7af72c2e3ff042164f1715ba475bd4af74fdbd945ad70b","abstract_canon_sha256":"9c96d028d50e08b4f33295b08b58f5cb4d69e2b0bcfe6c53fd46d7113c156130"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:06:21.659697Z","signature_b64":"py1acQiVISym8ps0fBgvEzgtUp2c4SwOJ0tOuC+IDyA1NwiWf72kbEYSlYWXKDTbWdqVK/HLcHiQcAyROSxZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64415b2750f92a0aa75561bd22deee819ed05a7f103714ebfe588c568017f8c1","last_reissued_at":"2026-05-20T00:06:21.658598Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:06:21.658598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XFlowMap: Cross-Scale Generalization and Mapping of Massive Origin-Destination Data","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.SI","authors_text":"Diansheng Guo, Hai Jin","submitted_at":"2026-04-23T07:52:46Z","abstract_excerpt":"Mapping large origin-destination (OD) datasets remains challenging because flow maps become cluttered, meaningful patterns occur at multiple spatial scales, and existing flow-mapping approaches frequently rely on predefined aggregation units or manual generalization. This paper presents XFlowMap, a framework for the cross-scale generalization and mapping of massive OD data. Specifically, the framework integrates cross-scale flow pattern (cluster) detection, automated flow map generalization, and a new cartographic representation for analyzing and visualizing complex origin-destination flow str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18777","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.18777/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.18777","created_at":"2026-05-20T00:06:21.658746+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18777v1","created_at":"2026-05-20T00:06:21.658746+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18777","created_at":"2026-05-20T00:06:21.658746+00:00"},{"alias_kind":"pith_short_12","alias_value":"MRAVWJ2Q7EVA","created_at":"2026-05-20T00:06:21.658746+00:00"},{"alias_kind":"pith_short_16","alias_value":"MRAVWJ2Q7EVAVJ2V","created_at":"2026-05-20T00:06:21.658746+00:00"},{"alias_kind":"pith_short_8","alias_value":"MRAVWJ2Q","created_at":"2026-05-20T00:06:21.658746+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG","json":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG.json","graph_json":"https://pith.science/api/pith-number/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/graph.json","events_json":"https://pith.science/api/pith-number/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/events.json","paper":"https://pith.science/paper/MRAVWJ2Q"},"agent_actions":{"view_html":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG","download_json":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG.json","view_paper":"https://pith.science/paper/MRAVWJ2Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18777&json=true","fetch_graph":"https://pith.science/api/pith-number/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/graph.json","fetch_events":"https://pith.science/api/pith-number/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/action/storage_attestation","attest_author":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/action/author_attestation","sign_citation":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/action/citation_signature","submit_replication":"https://pith.science/pith/MRAVWJ2Q7EVAVJ2VMG6SFXXOQG/action/replication_record"}},"created_at":"2026-05-20T00:06:21.658746+00:00","updated_at":"2026-05-20T00:06:21.658746+00:00"}