{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:Z2PTCNVB4VT5ODYSOVQXGUAQSN","short_pith_number":"pith:Z2PTCNVB","schema_version":"1.0","canonical_sha256":"ce9f3136a1e567d70f127561735010936bd4560b2e8735137c15c4be65657f1a","source":{"kind":"arxiv","id":"1906.11099","version":1},"attestation_state":"computed","paper":{"title":"A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.AP","authors_text":"Daiki Shiroi, Hajime Seya","submitted_at":"2019-06-25T14:15:31Z","abstract_excerpt":"The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the ext"},"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":"1906.11099","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2019-06-25T14:15:31Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"62fbde55c21da8c94ed3b4c8bc9e055a209276f069069281293f2a465deaa055","abstract_canon_sha256":"637d9537410fcfbd48232a540eb13f9e5dd448b1ccabaa4db173364c1abea65f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:09.834499Z","signature_b64":"iJR5xFBF99lMsae8+wYvg0TCTuT+TfYuSTcagbrNaqJzVt8fd4N3WCVIlMS0jCtyvwkSWEbA9MOBg7x3VSaEAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce9f3136a1e567d70f127561735010936bd4560b2e8735137c15c4be65657f1a","last_reissued_at":"2026-05-17T23:42:09.833860Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:09.833860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.AP","authors_text":"Daiki Shiroi, Hajime Seya","submitted_at":"2019-06-25T14:15:31Z","abstract_excerpt":"The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11099","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":"1906.11099","created_at":"2026-05-17T23:42:09.833988+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.11099v1","created_at":"2026-05-17T23:42:09.833988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11099","created_at":"2026-05-17T23:42:09.833988+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z2PTCNVB4VT5","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z2PTCNVB4VT5ODYS","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z2PTCNVB","created_at":"2026-05-18T12:33:33.725879+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/Z2PTCNVB4VT5ODYSOVQXGUAQSN","json":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN.json","graph_json":"https://pith.science/api/pith-number/Z2PTCNVB4VT5ODYSOVQXGUAQSN/graph.json","events_json":"https://pith.science/api/pith-number/Z2PTCNVB4VT5ODYSOVQXGUAQSN/events.json","paper":"https://pith.science/paper/Z2PTCNVB"},"agent_actions":{"view_html":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN","download_json":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN.json","view_paper":"https://pith.science/paper/Z2PTCNVB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.11099&json=true","fetch_graph":"https://pith.science/api/pith-number/Z2PTCNVB4VT5ODYSOVQXGUAQSN/graph.json","fetch_events":"https://pith.science/api/pith-number/Z2PTCNVB4VT5ODYSOVQXGUAQSN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN/action/storage_attestation","attest_author":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN/action/author_attestation","sign_citation":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN/action/citation_signature","submit_replication":"https://pith.science/pith/Z2PTCNVB4VT5ODYSOVQXGUAQSN/action/replication_record"}},"created_at":"2026-05-17T23:42:09.833988+00:00","updated_at":"2026-05-17T23:42:09.833988+00:00"}