Using opportunities in big data analytics to more accurately predict societal consequences of natural disasters.

Jessica Boakye, Paolo Gardoni, Colleen Murphy

Research output: Contribution to journalArticle

Abstract

The availability of data sources has greatly increased due to advances in technology and data sharing. With these new data sources and significantly larger volume of data, engineers have been presented with a unique opportunity to create more realistic and informative models that can be used in real world applications. This paper presents a probabilistic framework for using big data to assess and predict the well-being of individuals before and in the aftermath of a hazard. Data are used to inform a Capability Approach (CA) where capabilities are defined as important dimensions of well-being reflecting what individuals have a genuine opportunity to do or become. The paper also addresses three of the grand challenges presented by big data: privacy, source validity, and accuracy. As an example, the probabilistic framework is used to study the ability of households in a coastal community to be sheltered in the aftermath of a hypothetical earthquake.
Original languageEnglish (US)
Pages (from-to)100-114
Number of pages15
JournalCivil Engineering and Environmental Systems
Volume36
Issue number1
DOIs
StatePublished - Mar 1 2019

Fingerprint

Disasters
Data privacy
Earthquakes
Hazards
Availability
Engineers
Big data

Keywords

  • big data analytics
  • Hazard management
  • spatial capability approach

Cite this

@article{7de9b832b25a4d5d86bc262caac832a6,
title = "Using opportunities in big data analytics to more accurately predict societal consequences of natural disasters.",
abstract = "The availability of data sources has greatly increased due to advances in technology and data sharing. With these new data sources and significantly larger volume of data, engineers have been presented with a unique opportunity to create more realistic and informative models that can be used in real world applications. This paper presents a probabilistic framework for using big data to assess and predict the well-being of individuals before and in the aftermath of a hazard. Data are used to inform a Capability Approach (CA) where capabilities are defined as important dimensions of well-being reflecting what individuals have a genuine opportunity to do or become. The paper also addresses three of the grand challenges presented by big data: privacy, source validity, and accuracy. As an example, the probabilistic framework is used to study the ability of households in a coastal community to be sheltered in the aftermath of a hypothetical earthquake.",
keywords = "big data analytics, Hazard management, spatial capability approach",
author = "Jessica Boakye and Paolo Gardoni and Colleen Murphy",
year = "2019",
month = "3",
day = "1",
doi = "10.1080/10286608.2019.1615480",
language = "English (US)",
volume = "36",
pages = "100--114",
journal = "Civil Engineering and Environmental Systems",
issn = "1028-6608",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

TY - JOUR

T1 - Using opportunities in big data analytics to more accurately predict societal consequences of natural disasters.

AU - Boakye, Jessica

AU - Gardoni, Paolo

AU - Murphy, Colleen

PY - 2019/3/1

Y1 - 2019/3/1

N2 - The availability of data sources has greatly increased due to advances in technology and data sharing. With these new data sources and significantly larger volume of data, engineers have been presented with a unique opportunity to create more realistic and informative models that can be used in real world applications. This paper presents a probabilistic framework for using big data to assess and predict the well-being of individuals before and in the aftermath of a hazard. Data are used to inform a Capability Approach (CA) where capabilities are defined as important dimensions of well-being reflecting what individuals have a genuine opportunity to do or become. The paper also addresses three of the grand challenges presented by big data: privacy, source validity, and accuracy. As an example, the probabilistic framework is used to study the ability of households in a coastal community to be sheltered in the aftermath of a hypothetical earthquake.

AB - The availability of data sources has greatly increased due to advances in technology and data sharing. With these new data sources and significantly larger volume of data, engineers have been presented with a unique opportunity to create more realistic and informative models that can be used in real world applications. This paper presents a probabilistic framework for using big data to assess and predict the well-being of individuals before and in the aftermath of a hazard. Data are used to inform a Capability Approach (CA) where capabilities are defined as important dimensions of well-being reflecting what individuals have a genuine opportunity to do or become. The paper also addresses three of the grand challenges presented by big data: privacy, source validity, and accuracy. As an example, the probabilistic framework is used to study the ability of households in a coastal community to be sheltered in the aftermath of a hypothetical earthquake.

KW - big data analytics

KW - Hazard management

KW - spatial capability approach

U2 - 10.1080/10286608.2019.1615480

DO - 10.1080/10286608.2019.1615480

M3 - Article

VL - 36

SP - 100

EP - 114

JO - Civil Engineering and Environmental Systems

JF - Civil Engineering and Environmental Systems

SN - 1028-6608

IS - 1

ER -