Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), 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 systems 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 a specific big data to perform a given task is usually a challenge for researchers from the disaster management field. Therefore, this paper contributes in four aspects. Firstly, potential big data sources are described and characterized. Secondly, the big data processing frameworks are characterized and grouped based on the sources of data they handle. Then, a short description of each big data processing framework is provided and a comparison of processing frameworks in each group is carried out considering the main aspects such as computing cluster architecture, data flow, data processing model, fault-tolerance, scalability, latency, back-pressure mechanism, programming languages, and support for machine learning libraries, which are related to specific processing needs. Finally, a link between big data and processing frameworks is established, based on the processing provisioning for essential tasks in the response phase of disaster management.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.