Volunteered Geographic Information in Natural Hazard Analysis: A Systematic Literature Review of Current Approaches with a Focus on Preparedness and Mitigation

Klonner, Carolin; Marx, Sabrina; Usón, Tomás J.; Albuquerque, João Porto de; Höfle, Bernhard

doi:10.3390/ijgi5070103

Cited by 68 publications

(44 citation statements)

References 54 publications

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“…The software is designed for quick and reliable information collection after natural disasters or in humanitarian crises. Open-source software, as a method for data collection and gaining knowledge, is increasingly becoming important within the field of natural hazards (Eckle et al, 2016;Klonner et al, 2016). For instance, OpenStreetMap (OSM) and other voluntary geographic information services help to create comprehensive databases of up-to-date geospatial data which also can be used for natural risk assessment (Schelhorn et al, 2014;Vaz and Arsanjani, 2015;Yang et al, 2016).…”

Section: Contents Of the Surveymentioning

confidence: 99%

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

Laudan

Rözer

Sieg

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Flash floods are caused by intense rainfall events and represent an insufficiently understood phenomenon in Germany. As a result of higher precipitation intensities, flash floods might occur more frequently in future. In combination with changing land use patterns and urbanisation, damage mitigation, insurance and risk management in flash-floodprone regions are becoming increasingly important. However, a better understanding of damage caused by flash floods requires ex post collection of relevant but yet sparsely available information for research. At the end of May 2016, very high and concentrated rainfall intensities led to severe flash floods in several southern German municipalities. The small town of Braunsbach stood as a prime example of the devastating potential of such events. Eight to ten days after the flash flood event, damage assessment and data collection were conducted in Braunsbach by investigating all affected buildings and their surroundings. To record and store the data on site, the open-source software bundle KoBoCollect was used as an efficient and easy way to gather information. Since the damage driving factors of flash floods are expected to differ from those of riverine flooding, a post-hoc data analysis was performed, aiming to identify the influence of flood processes and building attributes on damage grades, which reflect the extent of structural damage. Data analyses include the application of random forest, a random general linear model and multinomial logistic regression as well as the construction of a local impact map to reveal influences on the damage grades. Further, a Spearman's Rho correlation matrix was calculated. The results reveal that the damage driving factors of flash floods differ from those of riverine floods to a certain extent. The exposition of a building in flow direction shows an especially strong correlation with the damage grade and has a high predictive power within the constructed damage models. Additionally, the results suggest that building materials as well as various building aspects, such as the existence of a shop window and the surroundings, might have an effect on the resulting damage. To verify and confirm the outcomes as well as to support future mitigation strategies, risk management and planning, more comprehensive and systematic data collection is necessary.

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Section: Contents Of the Surveymentioning

confidence: 99%

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

Laudan

Rözer

Sieg

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…With the rise of Web 2.0 applications, however, there was a large boost in collaborative and fast data acquisition initiatives. Olyazadeh et al (2016) present a WebGIS Android App for fast data acquisition of landslide hazard, and Klonner et al (2016) present a review of how volunteered geographical information is collected in natural hazard analysis. Baum et al (2014) highlight the Report a landslide website operated by the USGS for engaging public in the identification of geological hazards.…”

Section: Discussionmentioning

confidence: 99%

Spatially distributed rainfall information and its potential for regional landslide early warning systems

Canli

Loigge²,

Glade

2017

Nat Hazards

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Crucial to most landslide early warning system (EWS) is the precise prediction of rainfall in space and time. Researchers are aware of the importance of the spatial variability of rainfall in landslide studies. Commonly, however, it is neglected by implementing simplified approaches (e.g. representative rain gauges for an entire area). With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on deterministic and geostatistical methods. With kriging usually being a labour-intensive, manual task, a simplified variogram modelling routine was applied for the automated processing of up-to-date point information data. Validation showed quite satisfactory results, yet it also revealed the drawbacks that are associated with univariate geostatistical interpolation techniques which solely rely on rain gauges (e.g. smoothing of data, difficulties in resolving small-scale, highly intermittent rainfall). In the perspective, the potential use of citizen scientific data is highlighted for the improvement of studies on landslide EWS.

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“…In recent years, many cases have proved that citizens on the spot are able to organize themselves promptly and begin to share the disaster information when a disaster strikes, producing massive VGI (Volunteered geographic information) about disaster situation, which is valuable for disaster response if this VGI can be exploited efficiently and properly . Many research studies on natural disasters like flood, earthquake, and forest fires utilize social media like Twitter, Flickr, or YouTube, but few studies focus on typhoon disaster based on Sina Microblog, which is the most popular social network in China even though typhoon disaster plagues Southeast China every year . In order to take advantage of this social media data, the following problems must be investigated.…”

Section: Introductionmentioning

confidence: 99%

“…2 Many research studies on natural disasters like flood, earthquake, and forest fires utilize social media like Twitter, Flickr, or YouTube, but few studies focus on typhoon disaster based on Sina Microblog, which is the most popular social network in China even though typhoon disaster plagues Southeast China every year. [3][4][5] In order to take advantage of this social media data, the following problems must be investigated. First, how to retrieve and classify the data.…”

mentioning

confidence: 99%

Extracting and classifying typhoon disaster information based on volunteered geographic information from Chinese Sina microblog

Zhao

Chen

Liu

et al. 2018

Concurrency and Computation

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Summary The notion and application of volunteered geographic information occur and rise rapidly in recent years with the thriving of social media in China such as Sina Microblog, which is one of the most active social network sites. Many researches on natural disasters like flood, earthquake, and forest fires leverage social media like Twitter, Flickr, or YouTube, but few studies focus on typhoon disaster based on Sina Microblog even though typhoon disaster batters southeast coastline of China every year. This study proposed a method to extract and classify typhoon disaster information from Sina Microblog. KNN (k‐nearest neighbors) algorithm is implemented for microblog classification in order to extract useful information about the real hazards, and an experiment is conducted to tune the parameters in KNN by comparison of outcomes of social media data analysis and the real typhoon situation. The result shows that more than 70% microblogs are classified correctly. After the classification, we carried out spatial temporal analysis to map the disaster situation. It shows that the spatial distribution of microblog message mean centers about typhoon has regular variation along with the typhoon path. It can be confidently concluded that Sina Microblog has some potential prospects for estimating the typhoon disaster situation.

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Volunteered Geographic Information in Natural Hazard Analysis: A Systematic Literature Review of Current Approaches with a Focus on Preparedness and Mitigation

Cited by 68 publications

References 54 publications

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods

Spatially distributed rainfall information and its potential for regional landslide early warning systems

Extracting and classifying typhoon disaster information based on volunteered geographic information from Chinese Sina microblog

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