Examine the effects of neighborhood equity on disaster situational awareness: Harness machine learning and geotagged Twitter data

Zhai, Wei; Peng, Zhong-Ren; Yuan, Faxi

doi:10.1016/j.ijdrr.2020.101611

Cited by 54 publications

(21 citation statements)

References 37 publications

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“…Previous studies have investigated protective and exposure factors with regard to mental health in the context of disasters [ 8 , 9 , 10 , 29 , 45 ]. Some of these studies have been able to offer precise insights into which exposure factors caused mental stress after Superstorm Sandy and how they were spatially spread [ 9 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

“…Another study by Gruebner and colleagues found that during Superstorm Sandy, specific negative emotions clustered over single days, highlighting perceived locally specific health risks, such as falling trees or rattling windows [ 24 ]. While other studies have also applied geo-referenced social media data in the context of disaster events [ 14 , 15 , 16 , 17 , 18 , 20 , 21 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], none to our knowledge has investigated the socio-demographic context in which emotions have been expressed. Further, as far as we know, the relationship between infrastructural damage and negative emotional expressions on Twitter before, during, or after a natural disaster has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

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Space-Time Dependence of Emotions on Twitter after a Natural Disaster

Garske¹,

Elayan

Sýkora

et al. 2021

IJERPH

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Natural disasters can have significant consequences for population mental health. Using a digital spatial epidemiologic approach, this study documents emotional changes over space and time in the context of a large-scale disaster. Our aims were to (a) explore the spatial distribution of negative emotional expressions of Twitter users before, during, and after Superstorm Sandy in New York City (NYC) in 2012 and (b) examine potential correlations between socioeconomic status and infrastructural damage with negative emotional expressions across NYC census tracts over time. A total of 984,311 geo-referenced tweets with negative basic emotions (anger, disgust, fear, sadness, shame) were collected and assigned to the census tracts within NYC boroughs between 8 October and 18 November 2012. Global and local univariate and bivariate Moran’s I statistics were used to analyze the data. We found local spatial clusters of all negative emotions over all disaster periods. Socioeconomic status and infrastructural damage were predominantly correlated with disgust, fear, and shame post-disaster. We identified spatial clusters of emotional reactions during and in the aftermath of a large-scale disaster that could help provide guidance about where immediate and long-term relief measures are needed the most, if transferred to similar events and on comparable data worldwide.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space-Time Dependence of Emotions on Twitter after a Natural Disaster

Garske¹,

Elayan

Sýkora

et al. 2021

IJERPH

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“…Several studies exist on the awareness of disaster situations, such as research that has examined the combined sociodemographic data and geotagged Twitter data to understand disaster situation awareness from a social justice perspective [9]. Another study examined disaster situation awareness through social media text classification in real time by comparing the support vector machine and logistic regression methods [10].…”

Section: Introductionmentioning

confidence: 99%

Development and Simulation of Cyberdisaster Situation Awareness Models

2022

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Cyberdisasters require an organization’s disaster team to be prepared. Disaster events are difficult to predict, but the impact of this risk on an organization is large. However, organizations sometimes struggle in being prepared for disaster situations. Here, awareness of disaster situations when analysing priority disasters (e.g., earthquakes and pandemics) and how to mitigate them can help an organization’s preparedness. Mitigation scenarios need to be determined and simulated so that a disaster team is ready to face disaster. Using Endsley’s situational awareness model and a tabletop exercise, this study aimed to help a disaster team determine cyberdisaster risk priority and assess a team’s preparedness for dealing with a cyberdisaster. The situation awareness model was divided into two stages: awareness of cyberdisaster situations and tabletop evaluations. Awareness of a disaster situation was carried out by determining the highest priority for disaster risk using the fuzzy failure modes and effects analysis (FMEA) method. The results of the first study show that the high-risk category contains ransomware attacks during pandemics and earthquakes. The second study performed a tabletop simulation questionnaire survey of earthquakes and ransomware attacks during a pandemic for several disaster teams with 152 respondents. The results of the survey evaluation of the earthquakes and ransomware attacks simulation survey show that the effect factors of cyberdisaster simulation decisions are 95% system capability (p < 0.05), 90% knowledge (p < 0.05), and 90% awareness of a disaster situation (p < 0.05); these factors show the effect of a disaster team’s decision during a tabletop simulation. The novelty of this research lies in building a model for how an organizational process determines the priority of a cyberdisaster tabletop simulation and the factors that contribute to increasing a disaster team’s awareness in dealing with cyberattacks.

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“…Another limitation is the dearth of empirical studies measuring community resilience based on data capturing the complex interactions of the nexus of populations, businesses and the built environment. Most existing studies have relied on two data types: surveys [23,[28][29][30][31][32] and social media data [33][34][35][36][37][38]. Surveys present two drawbacks for collection of community resilience data.…”

Section: Introductionmentioning

confidence: 99%

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

Podesta

Coleman

Esmalian

et al. 2021

J. R. Soc. Interface.

Self Cite

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This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics—low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics—high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.

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Examine the effects of neighborhood equity on disaster situational awareness: Harness machine learning and geotagged Twitter data

Cited by 54 publications

References 37 publications

Space-Time Dependence of Emotions on Twitter after a Natural Disaster

Space-Time Dependence of Emotions on Twitter after a Natural Disaster

Development and Simulation of Cyberdisaster Situation Awareness Models

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

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