Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data

Qiang, Yi; Xu, Jinwen

doi:10.1080/13658816.2019.1694681

Cited by 33 publications

(14 citation statements)

References 41 publications

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“…Figure 3 shows an example of the recovery trajectory where a functional capacity suddenly declines after the disaster and gradually recovers subsequently. The conceptual framework of recovery trajectories has been widely used to measure the resilience of social-ecological and infrastructural systems [49][50][51][52][53]. The variation of recovery trajectories among different places can indicate resilience.…”

Section: Conceptual Framework Of Recovery Trajectoriesmentioning

confidence: 99%

“…Also, resampling the NTL data to the same scale of the exploratory variables may result in potential data loss or bias, thus affecting the model fitting. Future work is needed to ground-truthing the detected NTL patterns from other data sources, such as real-time traffic data [51], mobile phone tracking data [71], and survey data.…”

Section: Interpretation Of Ntl Spatial Patternmentioning

confidence: 99%

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Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

Qiang

2021

Remote Sensing

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Quantitative assessment of community resilience is a challenge due to the lack of empirical data about human dynamics in disasters. To fill the data gap, this study explores the utility of nighttime lights (NTL) remote sensing images in assessing community recovery and resilience in natural disasters. Specifically, this study utilized the newly-released NASA moonlight-adjusted SNPP-VIIRS daily images to analyze spatiotemporal changes of NTL radiance in Hurricane Sandy (2012). Based on the conceptual framework of recovery trajectory, NTL disturbance and recovery during the hurricane were calculated at different spatial units and analyzed using spatial analysis tools. Regression analysis was applied to explore relations between the observed NTL changes and explanatory variables, such as wind speed, housing damage, land cover, and Twitter keywords. The result indicates potential factors of NTL changes and urban-rural disparities of disaster impacts and recovery. This study shows that NTL remote sensing images are a low-cost instrument to collect near-real-time, large-scale, and high-resolution human dynamics data in disasters, which provide a novel insight into community recovery and resilience. The uncovered spatial disparities of community recovery help improve disaster awareness and preparation of local communities and promote resilience against future disasters. The systematical documentation of the analysis workflow provides a reference for future research in the application of SNPP-VIIRS daily images.

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Section: Conceptual Framework Of Recovery Trajectoriesmentioning

confidence: 99%

Section: Interpretation Of Ntl Spatial Patternmentioning

confidence: 99%

Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

Qiang

2021

Remote Sensing

Self Cite

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“…With the rapid development and widespread use of spatial awareness and mobile positioning technologies, locationbased big data is closely linked to the development of society and penetrates into the daily lives of people (Li et al, 2021;Zheng et al, 2020). The wave of big data has provided new insights into the value of data and significantly changed the way they are used (Mudigonda et al, 2019;Qiang & Xu, 2020;Zheng et al, 2020). Users of web navigation platforms become data producers through real-time movement trajectories (Xu et al, 2020).…”

Section: Spatial Accessibilitymentioning

confidence: 99%

“…At the same time, online platforms expose data to users through application programming interfaces (APIs). Researchers can use the APIs provided by Web GIS platforms such as Google Map, Yahoo Map, and online maps such as Baidu Map and Gaode Map in China to obtain optimal travel routes and time costs for different traffic conditions with precision (Mudigonda et al, 2019;Qiang & Xu, 2020;Zheng et al,…”

Section: Spatial Accessibilitymentioning

confidence: 99%

The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry

Qian

Lin

Shi

et al. 2021

Preprint

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Abstract. Underlying the impact of global warming and rapid growth of the tourism industry, the increasing frequency of flood post threats to the sustainable development of the coastal cities in China. The article proposes a methodological approach to evaluate the emergence response capability. This approach combines the flood simulation scenario method, traffic big data with the path navigation interface of the web. This article provides an empirical study to evaluate the emergency response from Fire & Rescue Service (FRS) to the tourist hotel in Shanghai from spatial accessibility perspective. The findings show that (1). The emergency response from FRS has significant relationships with the situation of transportation, the location of hotels, the intensity of flood inundation and the number, location of the urban FRS. (2). The emergency accessibility of a city caused by floods depends on the prevailing traffic conditions. The more severe traffic congestion has a significant impact on the spatial accessibility. (3) Flooding and real-time traffic conditions can change the fastest path from FRS to tourist hotels, resulting in delays in emergency response times, and the selection of the most appropriate travel routes is critical to improving the emergency response capability of cities. The results proved the validity of this proposed approach. Consequently, the approach contributes to the enhancement of the level of emergence response ability of urban tourism when they encounter disasters.

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“…Specifically, this special section was proposed as part of a pre-conference workshop on Analysis of Movement Data (AMD 2018) at the GIScience 2018 meeting, 28 August 2018, Melbourne, Australia. The focus of this special section is on three aspects of CMA: (1) representation and modeling of movement (Buchin et al 2019, Graser et al 2020; (2) urban mobility analytics (Qiang andXu 2019, Li et al 2020) and (3) movement analytics using social media data (Ma et al 2020, Xin andMacEachren 2020). With the papers presented in the special section, we highlight recent advancements in CMA with the development of methods and techniques for big movement data analytics and utilization of trajectories constructed using user-generated crowdsourced contents such as geo-tagged social media posts.…”

Section: Introductionmentioning

confidence: 99%

Progress in computational movement analysis – towards movement data science

Dodge

Gao

Tomko

et al. 2020

International Journal of Geographical Information Science

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Empirical assessment of road network resilience in natural hazards using crowdsourced traffic data

Cited by 33 publications

References 41 publications

Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light

The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry

Progress in computational movement analysis – towards movement data science

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