{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JBRS25DTBJSF5ATZ4GFNSFBTMS","short_pith_number":"pith:JBRS25DT","schema_version":"1.0","canonical_sha256":"48632d74730a645e8279e18ad91433649d974b1c73035c0eb5484e6cb4999c8e","source":{"kind":"arxiv","id":"1711.05462","version":1},"attestation_state":"computed","paper":{"title":"A Machine Learning Approach to Modeling Human Migration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Bistra Dilkina, Caleb Robinson","submitted_at":"2017-11-15T09:10:02Z","abstract_excerpt":"Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications, international trade, spread of infectious diseases, conservation planning, and public policy development. Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, an"},"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":"1711.05462","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-11-15T09:10:02Z","cross_cats_sorted":["cs.LG","physics.soc-ph"],"title_canon_sha256":"352fbc7532aca949e6f5631bb178d4d168df45ec93d2b2b262a84e3ea49b331f","abstract_canon_sha256":"b72208d55f653fe058d491daa89ea7ba6525d37f27fdd4e79df1fbc5fe1fc5b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:29.745503Z","signature_b64":"UHDNfLWDoYG6NzJFznFgXm/cGDqy3jv0KcuKZRVkky3L6Fcy9w7vTyYV6oGO0E0+klBlUgNfjB1xUGljao0jDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48632d74730a645e8279e18ad91433649d974b1c73035c0eb5484e6cb4999c8e","last_reissued_at":"2026-05-18T00:30:29.744856Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:29.744856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Machine Learning Approach to Modeling Human Migration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.soc-ph"],"primary_cat":"cs.SI","authors_text":"Bistra Dilkina, Caleb Robinson","submitted_at":"2017-11-15T09:10:02Z","abstract_excerpt":"Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications, international trade, spread of infectious diseases, conservation planning, and public policy development. Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05462","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":""},"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":"1711.05462","created_at":"2026-05-18T00:30:29.744932+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.05462v1","created_at":"2026-05-18T00:30:29.744932+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05462","created_at":"2026-05-18T00:30:29.744932+00:00"},{"alias_kind":"pith_short_12","alias_value":"JBRS25DTBJSF","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"JBRS25DTBJSF5ATZ","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"JBRS25DT","created_at":"2026-05-18T12:31:21.493067+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/JBRS25DTBJSF5ATZ4GFNSFBTMS","json":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS.json","graph_json":"https://pith.science/api/pith-number/JBRS25DTBJSF5ATZ4GFNSFBTMS/graph.json","events_json":"https://pith.science/api/pith-number/JBRS25DTBJSF5ATZ4GFNSFBTMS/events.json","paper":"https://pith.science/paper/JBRS25DT"},"agent_actions":{"view_html":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS","download_json":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS.json","view_paper":"https://pith.science/paper/JBRS25DT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.05462&json=true","fetch_graph":"https://pith.science/api/pith-number/JBRS25DTBJSF5ATZ4GFNSFBTMS/graph.json","fetch_events":"https://pith.science/api/pith-number/JBRS25DTBJSF5ATZ4GFNSFBTMS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS/action/storage_attestation","attest_author":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS/action/author_attestation","sign_citation":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS/action/citation_signature","submit_replication":"https://pith.science/pith/JBRS25DTBJSF5ATZ4GFNSFBTMS/action/replication_record"}},"created_at":"2026-05-18T00:30:29.744932+00:00","updated_at":"2026-05-18T00:30:29.744932+00:00"}