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Population Displacement Estimation During Disasters Using Mobile Phone Data

Time: Mon 2022-06-13 08.00

Location: Ångloket, Teknikringen 10A, Campus

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Language: English

Subject area: Geodesy and Geoinformatics, Geoinformatics

Doctoral student: Silvino Pedro Cumbane , Geoinformatik, Eduardo Mondlane University

Opponent: Professor John Östh, Oslo Metropolitan University

Supervisor: Professor Yifang Ban, Geoinformatik; Associate Professor Gyözö Gidofalvi, ; Assistant Professor Zeferino Saugene, Eduardo Mondlane University

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Natural disasters result in devastating losses in human life, environmental assets, and personal-, regional-, and national economies. The availability of different big data such as satellite images, Global Positioning System (GPS)traces, mobile Call Detail Records (CDR), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response system development usually requires the integration of data from different sources (streaming data sources and data sources at rest) with different characteristics and types, which consequently have different processing needs. Deciding which processing framework to use for specific big data to perform a given task is usually a challenge for researchers from the disaster management field. While many things can be accomplished with population and movement data, for disaster management key, and arguably most important task is to analyze the displacement of the population during and after a disaster. Therefore, in this Licentiate, the knowledge and framework resulting from a literature review were used to select tools, and processing strategies to perform population displacement analysis after a disaster. This is a use case of the framework as well as an illustration of the value and challenges (e.g., gaps in data due to power outages) of using CDR data analysis to support disaster management.

Using CDR data, the displaced population was inferred by analyzing the variation of home cell-tower for each anonymized mobile phone subscriber before and after a disaster. The effectiveness of the proposed method is evaluated using remote sensing-based building damage assessment data and Displacement Tracking Matrix (DTM) from individuals’ survey responses at shelters after a severe cyclone in Beira city, central Mozambique, in March 2019.

The results show an encouraging correlation coefficient (over 70%) between the number of arrivals in each neighborhood estimated using CDR data and from DTM. In addition to this, CDR-based analysis derives the spatial distribution of displaced populations with high coverage of people, i.e., including not only people in shelters but everyone who used a mobile phone before and after a disaster. Moreover, results suggest that if CDR data are available after a disaster, population displacement can be estimated and this information can be used for response activities and for example to contribute to reducing waterborne diseases (e.g., diarrheal disease) and diseases associated with crowding (e.g., acute respiratory infections) in shelters and host communities.