{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:SUVOFEQ6KHLPB4TRTMNNNH7Y3M","short_pith_number":"pith:SUVOFEQ6","canonical_record":{"source":{"id":"1507.00363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-07-01T20:19:26Z","cross_cats_sorted":[],"title_canon_sha256":"8c4d63c5c56333725344db2e746bdb25cc3d6a7f2debdb4caeef743106e350ca","abstract_canon_sha256":"2be417eb92dee4a7539049c290112e3f1ff88cdcb3962dc88de90d9be701245f"},"schema_version":"1.0"},"canonical_sha256":"952ae2921e51d6f0f2719b1ad69ff8db2dc1ecd750280364be5fa50f31df7760","source":{"kind":"arxiv","id":"1507.00363","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.00363","created_at":"2026-05-18T01:37:26Z"},{"alias_kind":"arxiv_version","alias_value":"1507.00363v1","created_at":"2026-05-18T01:37:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00363","created_at":"2026-05-18T01:37:26Z"},{"alias_kind":"pith_short_12","alias_value":"SUVOFEQ6KHLP","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"SUVOFEQ6KHLPB4TR","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"SUVOFEQ6","created_at":"2026-05-18T12:29:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:SUVOFEQ6KHLPB4TRTMNNNH7Y3M","target":"record","payload":{"canonical_record":{"source":{"id":"1507.00363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-07-01T20:19:26Z","cross_cats_sorted":[],"title_canon_sha256":"8c4d63c5c56333725344db2e746bdb25cc3d6a7f2debdb4caeef743106e350ca","abstract_canon_sha256":"2be417eb92dee4a7539049c290112e3f1ff88cdcb3962dc88de90d9be701245f"},"schema_version":"1.0"},"canonical_sha256":"952ae2921e51d6f0f2719b1ad69ff8db2dc1ecd750280364be5fa50f31df7760","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:26.823074Z","signature_b64":"HubG7PFB+O9Vce+Foc7+0gf+KijtNRla/woZZXFHB5f0aFq8lLEyJ+J4Tb4LtAJZ0I5NzDWnxTc32W1hsPhsAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"952ae2921e51d6f0f2719b1ad69ff8db2dc1ecd750280364be5fa50f31df7760","last_reissued_at":"2026-05-18T01:37:26.822452Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:26.822452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.00363","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:37:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TIBia65FFIdrUq8R6Z3XU4++L/MFZEwMoxrSMtvhleGGkVGSuPSDDlfIuYb1mvU1XIBcvxG6wSNLkxznM96NDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:59:51.498020Z"},"content_sha256":"e5c759dfb6c984a543c2a26b7b704567b1f88ce2737a925c9bef7dd4a3199714","schema_version":"1.0","event_id":"sha256:e5c759dfb6c984a543c2a26b7b704567b1f88ce2737a925c9bef7dd4a3199714"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:SUVOFEQ6KHLPB4TRTMNNNH7Y3M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting Melbourne Ambulance Demand using Kernel Warping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"David S. Matteson, Zhengyi Zhou","submitted_at":"2015-07-01T20:19:26Z","abstract_excerpt":"Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data sparsity at high resolutions and the need to respect complex urban spatial domains. To provide spatial density predictions for ambulance demand in Melbourne, Australia as it varies over hourly intervals, we propose a predictive spatio-temporal kernel warping method. To predict for each hour, we build a kernel density estimator on a sparse set of the most similar data from relevant past time periods (labeled data), but wa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00363","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:37:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UvQgfFDivWsGd0iqtnBURnU+0bObhT6EQRgGgFWfGtpv/3NLe7JnCqQ/KYGSTFeLmtGfAmElMZbBhvYXTimADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T15:59:51.498382Z"},"content_sha256":"6abb96612eb8771ce80dc1cb3752bca9d8e610d1f681541755627b8ad5562568","schema_version":"1.0","event_id":"sha256:6abb96612eb8771ce80dc1cb3752bca9d8e610d1f681541755627b8ad5562568"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M/bundle.json","state_url":"https://pith.science/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T15:59:51Z","links":{"resolver":"https://pith.science/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M","bundle":"https://pith.science/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M/bundle.json","state":"https://pith.science/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SUVOFEQ6KHLPB4TRTMNNNH7Y3M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:SUVOFEQ6KHLPB4TRTMNNNH7Y3M","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2be417eb92dee4a7539049c290112e3f1ff88cdcb3962dc88de90d9be701245f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-07-01T20:19:26Z","title_canon_sha256":"8c4d63c5c56333725344db2e746bdb25cc3d6a7f2debdb4caeef743106e350ca"},"schema_version":"1.0","source":{"id":"1507.00363","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.00363","created_at":"2026-05-18T01:37:26Z"},{"alias_kind":"arxiv_version","alias_value":"1507.00363v1","created_at":"2026-05-18T01:37:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00363","created_at":"2026-05-18T01:37:26Z"},{"alias_kind":"pith_short_12","alias_value":"SUVOFEQ6KHLP","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"SUVOFEQ6KHLPB4TR","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"SUVOFEQ6","created_at":"2026-05-18T12:29:42Z"}],"graph_snapshots":[{"event_id":"sha256:6abb96612eb8771ce80dc1cb3752bca9d8e610d1f681541755627b8ad5562568","target":"graph","created_at":"2026-05-18T01:37:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data sparsity at high resolutions and the need to respect complex urban spatial domains. To provide spatial density predictions for ambulance demand in Melbourne, Australia as it varies over hourly intervals, we propose a predictive spatio-temporal kernel warping method. To predict for each hour, we build a kernel density estimator on a sparse set of the most similar data from relevant past time periods (labeled data), but wa","authors_text":"David S. Matteson, Zhengyi Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-07-01T20:19:26Z","title":"Predicting Melbourne Ambulance Demand using Kernel Warping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00363","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e5c759dfb6c984a543c2a26b7b704567b1f88ce2737a925c9bef7dd4a3199714","target":"record","created_at":"2026-05-18T01:37:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2be417eb92dee4a7539049c290112e3f1ff88cdcb3962dc88de90d9be701245f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-07-01T20:19:26Z","title_canon_sha256":"8c4d63c5c56333725344db2e746bdb25cc3d6a7f2debdb4caeef743106e350ca"},"schema_version":"1.0","source":{"id":"1507.00363","kind":"arxiv","version":1}},"canonical_sha256":"952ae2921e51d6f0f2719b1ad69ff8db2dc1ecd750280364be5fa50f31df7760","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"952ae2921e51d6f0f2719b1ad69ff8db2dc1ecd750280364be5fa50f31df7760","first_computed_at":"2026-05-18T01:37:26.822452Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:37:26.822452Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HubG7PFB+O9Vce+Foc7+0gf+KijtNRla/woZZXFHB5f0aFq8lLEyJ+J4Tb4LtAJZ0I5NzDWnxTc32W1hsPhsAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:37:26.823074Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.00363","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5c759dfb6c984a543c2a26b7b704567b1f88ce2737a925c9bef7dd4a3199714","sha256:6abb96612eb8771ce80dc1cb3752bca9d8e610d1f681541755627b8ad5562568"],"state_sha256":"0f9b3241c9ff12f2827943399a339fd357111625d0b33300a405280cb57ec68f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NTh9PBnTMRcHJOnZUk3trFlcN3DSduyPRbQtxbfxiTLLHO7vXjyoxa/lhbodEkvRs9ykHtjwOoWB3pUYSGa+Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T15:59:51.500705Z","bundle_sha256":"1046860a3577113300aea1ca9f12ee8ebdc8070106c59fdc9b2bdfb94b0b0a80"}}