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An entropy-based framework for efficient post-disaster assessment based on crowdsourced data

Authors:

Sasan Tavakkol,

Patrick Lynett,

Cyrus ShahabiAuthors Info & Claims

EM-GIS '16: Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management

Article No.: 13, Pages 1 - 8

https://doi.org/10.1145/3017611.3017624

Published: 31 October 2016 Publication History

Abstract

After a disaster, authorities need to efficiently collect and analyze data from the disaster area in order to increase their situational awareness and make informed decisions. The conventional data acquisition methods such as dispatching inspection teams are often time-consuming. With the widespread availability of mobile devices, crowdsourcing has become an effective alternative means for data acquisition. However, the large amount of crowdsourced data is often overwhelming and requires triage on the collected data. In this paper, we introduce a framework to crowdsource post-disaster data and a new prioritization strategy based on the expected value of the information contained in the collected data (entropy) and their significance. We propose a multi-objective problem to analyze a portion of the collected data such that the entropy retrieved from the disaster area and the significance of analyzed data are maximized. We solve this problem using Pareto optimization that strikes a balance between both objectives. We evaluate our framework by applying it on bridges inspection after the 2001 Nisqually earthquake as a case study. We also investigate the feasibility of sending the crowdsourced data to the crowd for reviewing. The results demonstrate the effectiveness and feasibility of the proposed framework.

References

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Digital Library

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To, H., Tavakkol, S., Kim, S.H. and Shahabi, C. 2016. On Acquisition and Analysis of Visual Data for Crowdsourcing Disaster Response. Technical Report, University of Southern California. http://infolab.usc.edu/DocsDemos/bdr.pdf

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Cited By

Mudassir GDi Marco A(2021)Social-based City Reconstruction Planning in case of natural disasters: a Reinforcement Learning Approach2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00074(493-503)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00074
Mudassir GHoward EPasquini LArbib CClementini EMarco AStilo G(2021)Toward Effective Response to Natural Disasters: A Data Science ApproachIEEE Access10.1109/ACCESS.2021.31350549(167827-167844)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3135054
Mudassir G(2020)Social-Based Physical Reconstruction Planning in Case of Natural Disaster: A Machine Learning ApproachResearch Challenges in Information Science10.1007/978-3-030-50316-1_44(604-612)Online publication date: 25-Jun-2020
https://doi.org/10.1007/978-3-030-50316-1_44
Show More Cited By

Index Terms

An entropy-based framework for efficient post-disaster assessment based on crowdsourced data
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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cover image ACM Conferences

EM-GIS '16: Proceedings of the Second ACM SIGSPATIALInternational Workshop on the Use of GIS in Emergency Management

October 2016

101 pages

ISBN:9781450345804

DOI:10.1145/3017611

Conference Chairs:
Hui Zhang,
Yan Huang,
Jean-Claude Thill

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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SIGSPATIAL'16

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SIGSPATIAL

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31, 2016

California, Burlingame

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EM-GIS '16 Paper Acceptance Rate 16 of 26 submissions, 62%;

Overall Acceptance Rate 30 of 54 submissions, 56%

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Cited By

Mudassir GDi Marco A(2021)Social-based City Reconstruction Planning in case of natural disasters: a Reinforcement Learning Approach2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00074(493-503)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00074
Mudassir GHoward EPasquini LArbib CClementini EMarco AStilo G(2021)Toward Effective Response to Natural Disasters: A Data Science ApproachIEEE Access10.1109/ACCESS.2021.31350549(167827-167844)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3135054
Mudassir G(2020)Social-Based Physical Reconstruction Planning in Case of Natural Disaster: A Machine Learning ApproachResearch Challenges in Information Science10.1007/978-3-030-50316-1_44(604-612)Online publication date: 25-Jun-2020
https://doi.org/10.1007/978-3-030-50316-1_44
Kim SAlfarrarjeh AConstantinou GShahabi C(2019)TVDP: Translational Visual Data Platform for Smart Cities2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2019.00-36(45-52)Online publication date: Apr-2019
https://doi.org/10.1109/ICDEW.2019.00-36
Alialy RTavakkol STavakkol EGhorbani-Aghbologhi AGhaffarieh AKim SShahabi C(2018)A review on the applications of crowdsourcing in human pathologyJournal of Pathology Informatics10.4103/jpi.jpi_65_179:1(2)Online publication date: 2018
https://doi.org/10.4103/jpi.jpi_65_17
Alfarrarjeh AKim SDeshmukh ARajan SLu YShahabi C(2018)Spatial Coverage Measurement of Geo- Tagged Visual Data: A Database Approach2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM.2018.8499062(1-8)Online publication date: Sep-2018
https://doi.org/10.1109/BigMM.2018.8499062

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