Compară metode

	Forced Migration Needs Assessment	Mobile-Phone Mobility Estimation
Domeniu	Migration Studies	Migration Studies
Familie	Process / pipeline	Process / pipeline
Anul apariției	2015	2014
Autorul original	Inter-Agency Standing Committee (MIRA framework)	Pierre Deville, Catherine Linard, Andrew J. Tatem, et al.
Tip	Rapid multi-sector field assessment protocol for displacement crises	Computational pipeline for population and migration inference from mobile data
Sursa seminală	Inter-Agency Standing Committee (2015). Multi-Sector Initial Rapid Assessment (MIRA) Guidance. Geneva: IASC.	Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., Blondel, V. D., & Tatem, A. J. (2014). Dynamic Population Mapping Using Mobile Phone Data. PNAS, 111(45), 15888-15893.
Denumiri alternative	Displacement Needs Assessment, Multi-Sector Rapid Needs Assessment, MIRA-Style Assessment, Humanitarian Needs Assessment	CDR Mobility Estimation, Call-Detail-Record Migration Inference, Mobile Big Data Population Mapping, Phone-Based Displacement Tracking
Înrudite	3	3
Rezumat	A forced-migration needs assessment is the structured, rapid process humanitarian actors use to understand what a displaced population urgently needs in the chaotic first days and weeks of a refugee or displacement crisis. Its reference standard is the Inter-Agency Standing Committee's Multi-Sector Initial Rapid Assessment (MIRA) framework, codified in 2015, which coordinates many agencies behind a single, comparable picture of needs rather than a scatter of overlapping, sector-specific surveys. The method is deliberately a pipeline: it begins with a secondary-data review that mines everything already known — pre-crisis baselines and early situation reports — to define what is still unknown; it then collects rapid primary data across sectors such as food, water, shelter, health, and protection through key-informant interviews, direct site observation, and reports from the affected people themselves; it converts these into standardized severity scores; and it ranks needs to prioritize the response. Because conditions are fluid and access is constrained, the assessment trades statistical precision for speed, coordination, and decision-relevance, producing a shared analytical basis for an inter-agency humanitarian appeal and response plan.	Mobile-phone mobility estimation uses the digital traces left by ordinary phone use — call detail records, or CDRs — to map where people are, how their numbers shift over time, and how they move between places. Deville and colleagues' 2014 study in PNAS demonstrated that the locations of cell towers handling each call, aggregated across millions of subscribers, can produce dynamic population maps that track seasonal and daily changes far more finely than a decennial census ever could. Because a CDR records which tower served a user and when, the method can infer each person's habitual home location, count how many people 'live' in each area, and detect when those homes shift — the signature of internal migration or displacement. The approach turns a byproduct of telecom billing into a near-real-time demographic sensor, especially valuable where censuses are infrequent and crises move people faster than official statistics can follow. Crucially, the estimates are calibrated and validated against census or survey ground truth, so the phone-derived figures are anchored to known totals rather than taken at face value. The result is a powerful, if ethically fraught, way to observe human mobility at scale.