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Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0001-7218-9082
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0003-1164-8403
2021 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 10, no 6, p. 421-, article id 421Article in journal (Refereed) Published
Abstract [en]

Under normal circumstances, people's homes and work locations are given by their addresses, and this information is used to create a disaster management plan in which there are instructions to individuals on how to evacuate. However, when a disaster strikes, some shelters are destroyed, or in some cases, distance from affected areas to the closest shelter is not reasonable, or people have no possibility to act rationally as a natural response to physical danger, and hence, the evacuation plan is not followed. In each of these situations, people tend to find alternative places to stay, and the evacuees in shelters do not represent the total number of the displaced population. Knowing the spatial distribution of total displaced people (including people in shelters and other places) is very important for the success of the response activities which, among other measures, aims to provide for the basic humanitarian needs of affected people. Traditional methods of people displacement estimation are based on population surveys in the shelters. However, conducting a survey is infeasible to perform at scale and provides low coverage, i.e., can only cover the numbers for the population that are at the shelters, and the information cannot be delivered in a timely fashion. Therefore, in this research, anonymized mobile Call Detail Records (CDRs) are proposed as a source of information to infer the spatial distribution of the displaced population 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 an individual's questionnaire survey conducted 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 CDRs and from DTM. In addition to this, CDRs derive spatial distribution of displaced populations with high coverage of people, i.e., including not only people in the shelter but everyone who used a mobile phone before and after the disaster. Moreover, results suggest that if CDRs data are available right after a disaster, population displacement can be estimated, and this information can be used for response activities and hence contribute to reducing waterborne diseases (e.g., diarrheal disease) and diseases associated with crowding (e.g., acute respiratory infections) in shelters and host communities.

Place, publisher, year, edition, pages
MDPI AG , 2021. Vol. 10, no 6, p. 421-, article id 421
Keywords [en]
disaster response, mobile Call Detail Records (CDRs), displaced population
National Category
Human Geography
Identifiers
URN: urn:nbn:se:kth:diva-299049DOI: 10.3390/ijgi10060421ISI: 000666988800001Scopus ID: 2-s2.0-85109906266OAI: oai:DiVA.org:kth-299049DiVA, id: diva2:1582358
Note

QC 20210730

Available from: 2021-07-30 Created: 2021-07-30 Last updated: 2022-06-25Bibliographically approved
In thesis
1. Population Displacement Estimation During Disasters Using Mobile Phone Data
Open this publication in new window or tab >>Population Displacement Estimation During Disasters Using Mobile Phone Data
2022 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

Naturkatastrofer leder till förödande förluster i människoliv, miljötillgångaroch personliga, regionala och nationella ekonomier. Tillgången till olika stordatasåsom satellitbilder, GPS-spår (Global Positioning System), mobila Call DetailRecords (CDR), inlägg på sociala medier, etc., i samband med framsteg inom data-analysteknik (t.ex. datautvinning och big data bearbetning, maskininlärningoch djupinlärning) kan underlätta utvinningen av geospatial information somär avgörande för snabba och effektiva katastrofhantering. Utveckling av katas-trofberedskapssystem kräver dock vanligtvis integrering av data från olika källor(strömmande datakällor och datakällor i vila) med olika egenskaper och typer, somföljaktligen har olika behandlingsbehov. Att bestämma vilket ramverk för bearbet-ning som ska användas för en specifik big data för att utföra en given uppgift är van-ligtvis en utmaning för forskare från katastrofhanteringsområdet. Även om mångasaker kan åstadkommas med befolknings- och rörelsedata, för katastrofhantering ären nyckel- och utan tvekan viktigaste uppgift att analysera befolkningens förflyt-tning under och efter en katastrof. Därför, i denna licentiatuppsats, användes kun-skapen och ramverket från en litteraturgenomgång för att välja verktyg och bear-betningsstrategier för att utföra analys av befolkningsförflyttning efter en katastrof.Detta är ett användningsfall av ramverket samt en illustration av värdet och ut-maningarna (t.ex. luckor i data på grund av strömavbrott) med att användaCDR-dataanalys för att stödja katastrofhantering.Med hjälp av CDR-data kunde man sluta sig till den förflyttna befolkningengenom att analysera variationen av hemcellstorn för varje anonymiserad mobiltele-fonabonnent före och efter en katastrof. Effektiviteten av den föreslagna metodenutvärderas med hjälp av fjärranalysbaserade byggnadsskadebedömningsdata ochDisplacement Tracking Matrix (DTM) från individers enkätsvar vid skyddslokalerefter en svår cyklon i staden Beira, centrala Mozambique, i mars 2019.Resultaten visar en uppmuntrande korrelationskoefficient (över 70 %) mellanantalet ankomster i varje område uppskattat med hjälp av CDR-data och frånDTM. Utöver detta härleder CDR-baserad analys den rumsliga fördelningen avförflyttna befolkningen med hög täckning av människor, det vill säga inklusiveinte bara människor i skyddslokaler utan alla som använde mobiltelefon före ochefter katastrof. Dessutom tyder resultaten på att om CDR-data finns tillgängligaefter en katastrof kan befolkningsförflyttning uppskattas och denna informationkan användas för responsaktiviteter och till exempel för att bidra till att minskavattenburna sjukdomar (t.ex. diarrésjukdomar) och sjukdomar associerade medträngsel (t.ex. akuta luftvägsinfektioner) i skyddslokaler och värdsamhällen

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2022. p. 54
Series
TRITA-ABE-DLT ; 2230
National Category
Computer and Information Sciences
Research subject
Geodesy and Geoinformatics, Geoinformatics
Identifiers
urn:nbn:se:kth:diva-312254 (URN)978-91-8040-286-6 (ISBN)
Presentation
2022-06-13, Ångloket, Teknikringen 10A, KTH Campus, https://kth-se.zoom.us/j/69617751419, Stockholm, 08:00 (English)
Opponent
Supervisors
Funder
Sida - Swedish International Development Cooperation Agency
Note

QC220525

Available from: 2022-05-25 Created: 2022-05-16 Last updated: 2022-06-25Bibliographically approved

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Cumbane, Silvino PedroGidofalvi, Gyözö

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