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 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, intended for humanitarian decision support rather than formal statistical inference. The framework produces three complementary metrics scaled to population with uncertainty bounds: displacement rates, origin-destination flows, and return dynamics. An Aparri case study following Super Typhoon Nando (2025, Philippines) applies the framework to vendor-provided daily locations from Globe Telecom. Context-aware detection reduced estimated between-municipality displacement by 1.6-2.7 percentage points on weekdays versus naive methods, attributable to the commuter exception but not independently validated. The method captures between-municipality displacement only. Within-municipality evacuation falls outside scope. The single-case demonstration establishes proof of concept. External validity requires application across multiple events and locations. The framework provides humanitarian actors with operational displacement information while preserving individual privacy through aggregation.

翻译：及时的人群位移估计对于灾害期间的人道主义响应至关重要，但传统调查和实地评估速度缓慢。手机数据能实现近实时追踪，然而现有方法采用统一的位移定义而不考虑个体移动模式，导致将日常通勤者错误归类为灾后转移人口。我们提出一个方法论框架，通过三项创新解决这一问题：（1）移动档案分类，区分本地居民与通勤类型；（2）基于上下文的跨市镇位移检测，根据用户类型和星期几考虑预期位置；（3）操作不确定性界限，基于基线变异系数并含灾害调整因子，旨在为人道主义决策提供支持而非正式统计推断。该框架生成三个可扩至人口规模并含不确定性界限的互补指标：位移率、起讫点流量及返回动态。以超级台风南多（2025年，菲律宾）后的阿帕里案例研究为背景，将该框架应用于环球电信提供的供应商日常位置数据。与朴素方法相比，基于上下文的检测使工作日跨市镇位移估计降低了1.6至2.7个百分点，这一差异可归因于通勤例外规则，但未经过独立验证。该方法仅捕捉跨市镇位移，不涉及市镇内疏散。单案例验证确立了概念证明，外部有效性需在多事件与多地点中进一步应用。该框架通过聚合处理保护个人隐私，同时为人道主义行动者提供可操作的位移信息。