{"paper":{"title":"Context-Aware Displacement Estimation from Mobile Phone Data: A Methodological Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Mobile phone location data can estimate disaster-induced population displacement more accurately by classifying users as residents or commuters and adjusting for expected daily movements.","cross_cats":["cs.SI","stat.AP"],"primary_cat":"cs.CY","authors_text":"Muhammad Rheza Muztahid, Radityo Eko Prasojo, Rajius Idzalika","submitted_at":"2026-04-23T09:14:11Z","abstract_excerpt":"Timely population displacement estimates are critical for humanitarian response during disasters, but traditional surveys and field assessments are slow. Mobile phone data enables near real-time tracking, yet existing approaches apply uniform displacement definitions regardless of individual mobility patterns, misclassifying regular commuters as displaced. We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting for expected location by user type and day of week, and (3) operational uncertainty bounds derived from baseline coefficient of variation with a disaster adjustment factor.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That pre-disaster mobile data patterns reliably define 'expected' locations for each user type and that the commuter classification correctly separates regular cross-border movement from disaster-induced displacement.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A methodological framework that classifies users into mobility types and uses day-of-week context to detect true between-municipality displacement from mobile data, producing scaled rates with uncertainty bounds.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Mobile phone location data can estimate disaster-induced population displacement more accurately by classifying users as residents or commuters and adjusting for expected daily movements.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7e33f158168cb29685e2faa48b8afe5637bc10aae0547770bef8db0bc976c266"},"source":{"id":"2604.21457","kind":"arxiv","version":2},"verdict":{"id":"36f0de3f-ee60-4891-a55f-8ea14e119b06","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T13:48:44.474138Z","strongest_claim":"We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting for expected location by user type and day of week, and (3) operational uncertainty bounds derived from baseline coefficient of variation with a disaster adjustment factor.","one_line_summary":"A methodological framework that classifies users into mobility types and uses day-of-week context to detect true between-municipality displacement from mobile data, producing scaled rates with uncertainty bounds.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That pre-disaster mobile data patterns reliably define 'expected' locations for each user type and that the commuter classification correctly separates regular cross-border movement from disaster-induced displacement.","pith_extraction_headline":"Mobile phone location data can estimate disaster-induced population displacement more accurately by classifying users as residents or commuters and adjusting for expected daily movements."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.21457/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T12:41:37.756993Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-20T00:57:24.408172Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"866ff05ebb03fb54e8735c8ec8818414f703a151eb35ee554cbbe691e795c44d"},"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"}