Understanding Citizens' and Local Governments' Digital Communications During Natural Disasters

Hong, Lingzi; Fu, Cheng; Torrens, Paul M.; Frías-Martínez, Vanessa

doi:10.1145/3091478.3091502

Cited by 24 publications

(21 citation statements)

References 19 publications

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“…Of the 127,181 Twitter users, only 116 were detected as non-persons. This is probably due to the fact that related work has shown that news platforms and other official accounts that belong to government ad-ministrations and that report important information during disasters (weather, road or evacuation information) do not usually post geotagged tweets [31].…”

Section: Data Collectionmentioning

confidence: 99%

Modeling and predicting evacuation flows during hurricane Irma

Hong

Frías-Martínez

2020

EPJ Data Sci.

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Evacuations are a common practice to mitigate the potential risks and damages made by natural disasters. However, without proper coordination and management, evacuations can be inefficient and cause negative impact. Local governments and organizations need to have a better understanding of how the population responds to disasters and evacuation recommendations so as to enhance their disaster management processes. Previous studies mostly examine responses to evacuations at the individual or household level by using survey methods. However, population flows during disasters are not just the aggregation of individuals’ decisions, but a result of complex interactions with other individuals and the environment. We propose a method to model evacuation flows and reveal the patterns of evacuation flows at different spatial scales. Specifically, we gathered large-scale geotagged tweets during Hurricane Irma to conduct an empirical study. First, we present a method to characterize evacuation flows at different geographic scales: the state level, considering evacuation flows across southern states affected by Irma; the urban/rural area level, and the county level. Then we demonstrate results on the predictability of evacuation flows in the most affected state, Florida, by using the following environmental factors: the destructive force of the hurricane, the socioeconomic context, and the evacuation policy issued for counties. Feature analyses show that distance is a dominant predictive factor with counties that are geographically closer generally having larger evacuation flows. Socioeconomic levels are positively related to evacuation flows, with popular destinations associated to higher socioeconomic levels. The results presented in this paper can help decision makers to better understand population evacuation behaviors given certain environmental features, which in turn will aid in the design of efficient and informed preparedness and response strategies.

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Section: Data Collectionmentioning

confidence: 99%

Modeling and predicting evacuation flows during hurricane Irma

Hong

Frías-Martínez

2020

EPJ Data Sci.

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“…We propose using the remarkability of a particular high-tide event, as measured by the volume of tweets about flooding generated in a particular day, as a measure of flood occurrence and severity. Other scholars have used social media data to identify damage 13,14 and aid management 15,16 of severe natural disasters, such as earthquakes [17][18][19] , heat waves 20 , hurricanes 21,22 , snowstorms 23 , and wildfires 24 . Researchers have also recently examined the ability to use social media to detect public attention paid to other climatic factors 25 .…”

mentioning

confidence: 99%

Using remarkability to define coastal flooding thresholds

Moore

Obradovich

2020

Nat Commun

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Coastal flooding is increasingly common in many areas. However, the degree of inundation and associated disruption depend on local topography as well as the distribution of people, infrastructure and economic activity along the coast. Local measures of flooding that are comparable over large areas are difficult to obtain. Here we use the remarkability of flood events, measured by flood-related posts on social media, to estimate county-specific flood thresholds for shoreline counties along the east coast of the United States. While thresholds in most counties are statistically-indistinguishable from minor flood thresholds of nearby tide gauges, we find evidence that several areas experience noticeable flooding at tide heights lower than existing flood thresholds. These 22 counties include several major cities such as Miami, New York, and Boston, with a total population over 13 million. Our analysis implies that large populations might currently be exposed to nuisance flooding not identified via standard measures.

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“…During this process we consulted categories developed in prior content analyses of crisis-related social media [26,31]. While coding we noticed a diversity of information reporting forms of infrastructure damage prior studies suggest can support situational awareness during a crisis, including tweets reporting damage to buildings [26], roadways [12,33], and electrical infrastructure [4,17]. While this work informed our grounded analysis, the data we encountered revealed types of information that unpacked categories developed in prior research.…”

Section: Qualitative Content Analysismentioning

confidence: 92%

Expanding Awareness: Comparing Location, Keyword, and Network Filtering Methods to Collect Hyperlocal Social Media Data

Grace¹,

Halse²,

Aurite³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Opportunities to collect real-time social media data during a crisis remain limited to location and keyword filtering despite the sparsity of geographic metadata and the tendency of keyword-based methods to capture information posted by remote rather than local users. Here we introduce a third, network filtering method that uses social network ties to infer the location of social media users in a geographic community and collect data from networks of these users during a crisis. In this paper we compare all three methods by analysing the distribution of situational reports of infrastructure damage and service disruption across location, keyword, and network-filtered social media data during a weather emergency. We find that network filtering doubles the number of situational reports collected in real-time compared to location and keyword filtering alone, but that all three methods collect unique reports that can support situational awareness of incidents occurring across a community.

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Understanding Citizens' and Local Governments' Digital Communications During Natural Disasters

Cited by 24 publications

References 19 publications

Modeling and predicting evacuation flows during hurricane Irma

Modeling and predicting evacuation flows during hurricane Irma

Using remarkability to define coastal flooding thresholds

Expanding Awareness: Comparing Location, Keyword, and Network Filtering Methods to Collect Hyperlocal Social Media Data

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