Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior

Yabe, Takahiro; Tsubouchi, Kota; Shimizu, Toru; Sekimoto, Yoshihide; Ukkusuri, Satish V.

doi:10.1145/3292500.3330697

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Often, such measurements obtained from big data sources are fed into state-of-the-art machine-learning and artificial intelligence (AI) models to predict future outcome trends [e.g., predicting disaster evacuation dynamics using long short-term memory (LSTM) models ( 21 )]. Although studies have shown the high predictability of postdisaster dynamics with such models using selected case studies, such models are “black box” and often lack interpretability that is crucial for informing policy making.…”

Section: Measuring Recovery Trajectories Using Big Datamentioning

confidence: 99%

Toward data-driven, dynamical complex systems approaches to disaster resilience

Yabe

Rao

Cutter

2022

Proc. Natl. Acad. Sci. U.S.A.

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With rapid urbanization and increasing climate risks, enhancing the resilience of urban systems has never been more important. Despite the availability of massive datasets of human behavior (e.g., mobile phone data, satellite imagery), studies on disaster resilience have been limited to using static measures as proxies for resilience. However, static metrics have significant drawbacks such as their inability to capture the effects of compounding and accumulating disaster shocks; dynamic interdependencies of social, economic, and infrastructure systems; and critical transitions and regime shifts, which are essential components of the complex disaster resilience process. In this article, we argue that the disaster resilience literature needs to take the opportunities of big data and move toward a different research direction, which is to develop data-driven, dynamical complex systems models of disaster resilience. Data-driven complex systems modeling approaches could overcome the drawbacks of static measures and allow us to quantitatively model the dynamic recovery trajectories and intrinsic resilience characteristics of communities in a generic manner by leveraging large-scale and granular observations. This approach brings a paradigm shift in modeling the disaster resilience process and its linkage with the recovery process, paving the way to answering important questions for policy applications via counterfactual analysis and simulations.

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Section: Measuring Recovery Trajectories Using Big Datamentioning

confidence: 99%

Toward data-driven, dynamical complex systems approaches to disaster resilience

Yabe

Rao

Cutter

2022

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…Mining of web search query data has attracted the attention of researchers and practitioners ever since search engines were introduced to the world [30]. Web search data has been utilized for various applications, for example, to predict users' demographics [31] and mobility decisions during crisis [32]. During the COVID-19 pandemic, many studies have utilized web search query data to understand information seeking behavior and the occurrence of infodemics [33,34,35].…”

Section: Applications Of Web Search Data Analysismentioning

confidence: 99%

Early warning of COVID-19 hotspots using human mobility and web search query data

Yabe

Tsubouchi²,

Sekimoto

et al. 2022

Computers, Environment and Urban Systems

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COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1–2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.

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“…Therefore, we need an approach with a more user-friendly method to spatial analysis that offers single tasks for the user, and it may be an "app-based" web application [8]. As the internet becomes the main media in delivering any information, the design of effective website becomes increasingly essential [9]. The importance of putting the user needs in building a web-app to be used by anyone universally is more realistic and practical.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a User-Centered Web-App using Open Source Platform: Indonesia Disaster Data (InDITA)

Rudiastuti

Suryanegara

Wirawan

et al. 2020

JOIV : Int. J. Inform. Visualization

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The availability of updated and easy access to geospatial data and information is the main thing in disaster mitigation and sustainable spatial planning. This needs to be supported by the rapid development of technology in the aspect of spreading data and spatial information via the internet. The Spatial Data Infrastructure (SDI) is realized as an implementation of spatial data distribution. SDI is an integrated mechanism to make it easier for spatial data users to access, search, share, and use spatial data and information. Built upon the background statements, the research does intend to develop a web-app which is functioned as a data repository, viewer, and also simple processing (web GIS). To design and develop a disaster geospatial online application web-app that has an integrated function (repository-web GIS) following the directions and input from user feedback becomes the fold of the research. . The importance of putting the user needs in building a web-app to be used by anyone universally is more realistic and practical. The user interface is very essential in bridging communication between users and web mapping systems. User-centred design (UCD) implemented by gathering input and feedback from users. UCD describes the process of ensuring interface success throughout the design and development of the interface. GeoNode has chosen as the platform since it is open-sourced, and commonly used for managing and publishing geospatial data. Based on respondents' assessment of the quality of content/information that important to be developed in a disaster website, it is recognized that providing reliable and up-to-date data and information (69%) and the accuracy of data and information (74%) are the most important things to develop in the InDITA application. the most important features to respondents are the spatial data display feature/map viewer (89%). The design of InDITA was built interactive and user-friendly which designed as an application that accommodates simple viewer, data repository, and web GIS functions. The interactive design emerged as the demand for app users.

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Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior

Cited by 9 publications

References 30 publications

Toward data-driven, dynamical complex systems approaches to disaster resilience

Toward data-driven, dynamical complex systems approaches to disaster resilience

Early warning of COVID-19 hotspots using human mobility and web search query data

Design and Implementation of a User-Centered Web-App using Open Source Platform: Indonesia Disaster Data (InDITA)

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