Review of Big Data and Processing Frameworks for Disaster Response Applications

Cumbane, Silvino Pedro; Gidófalvi, Gyözö

doi:10.3390/ijgi8090387

Cited by 31 publications

(25 citation statements)

References 51 publications

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“…The proposed method involves six main steps, namely (1) Voronoi tesselation of the study area, (2) estimation of mobile phone users home location (at cell-tower level) before and after disaster, (3) neighborhood home location assignment before and after disaster, (4) estimation of displaced mobile phone users, (5) scaling up the displaced mobile phone users to actual population flow, and (6) validation process. The data processing was mainly carried out using Python API of Spark processing Framework (PySpark) as suggested by Cumbane and Gidófalvi [39].…”

Section: Methodsmentioning

confidence: 99%

Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities

Cumbane

Gidófalvi

2021

IJGI

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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.

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Section: Methodsmentioning

confidence: 99%

Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities

Cumbane

Gidófalvi

2021

IJGI

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“…In [20] authors focused state management techniques in big data systems such as Flink, Heron, Samza, Spark, and Storm. Cumbane [21] reviewed big data frameworks for disaster response applications. This research also discussed the similarities and differences of big data frameworks.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

Khalid

Yousaf

2021

Applied Sciences

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The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.

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“…Finally, crowdsourced data are the data generated actively by the population (suppliers of it are frequently known as digital humanitarians [45]), who participate in a network of volunteers to support disaster management. Depending on the different data source characteristics, spatial and non-spatial, they require different batch and stream big data processing frameworks, as detailed by Cumbane and Gidófalvi [46].…”

Section: Big Data Technologies In Emergency Managementmentioning

confidence: 99%

“…Several authors have summarized existing research in the application of big data systems to emergency management [2,43,46,[51][52][53][54][55][56][57]. The availability of resilient communication networks is one of the challenges for the application of big data technologies during emergencies, since large-scale disasters can result in massive blackouts.…”

Section: Big Data Technologies In Emergency Managementmentioning

confidence: 99%

A Big Data Reference Architecture for Emergency Management

2020

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Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely improve the decision-making process. On the other hand, digital humanitarianism has shown to be very beneficial for providing support during emergencies. Despite this, the full potential of combining automatic big data processing and digital humanitarianism approaches has not been fully realized, though there is an initial body of research. This paper aims to provide a reference architecture for emergency management that instantiates the NIST Big Data Reference Architecture to provide a common language and enable the comparison of solutions for solving similar problems.

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Review of Big Data and Processing Frameworks for Disaster Response Applications

Cited by 31 publications

References 51 publications

Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities

Spatial Distribution of Displaced Population Estimated Using Mobile Phone Data to Support Disaster Response Activities

A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

A Big Data Reference Architecture for Emergency Management

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