Probabilistic flood extent estimates from social media flood observations

Brouwer, Tom; Eilander, Dirk; Loenen, Arnejan van; Booij, Martijn J.; Wijnberg, Kathelijne Mariken; Verkade, Jan; Wagemaker, Jurjen

doi:10.5194/nhess-17-735-2017

Cited by 68 publications

(52 citation statements)

References 28 publications

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“…Data obtained from analyzing social media databases and image/video repositories can also be used to assess the impact of natural disasters. This can include determination of the spatial extent (Brouwer et al, ; Cervone et al, ; Jongman et al, ; Rosser et al, ) and impact/damage (de Albuquerque et al, ; Jongman et al, ; Kryvasheyeu et al, ; Vieweg et al, ) of floods, as well as the damage/injury arising from fires (Vieweg et al, ), hurricanes (Kryvasheyeu et al, ; Middleton et al, ; Yuan & Liu, ), tornadoes (Kryvasheyeu et al, ; Middleton et al, ), earthquakes (Kryvasheyeu et al, ), and mudslides (Kryvasheyeu et al, ).…”

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

100

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Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

100

View full text Add to dashboard Cite

show abstract

“…Several studies (e.g., [6], [7], [8], [9]) have proposed methods of creating near real-time flood maps from social media data, but not all of them have followed the same pathway for determining the location of floods. There is school of thought that believes flood locations can be determined based on reference to place names or physical locations as contained within social media contents [7], [9], [10]. For example, Jongman et al [9] combined satellite observations of water coverage with tweets containing textual information about flood locations in order to produce daily flood impact maps in the Philippines and Pakistan.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Jongman et al [9] combined satellite observations of water coverage with tweets containing textual information about flood locations in order to produce daily flood impact maps in the Philippines and Pakistan. Brouwer et al [10] used Twitter messages that mention locations of flooding during the December 2015 flood in the city of York (UK) to demonstrate how to generate probabilistic and deterministic flood maps, taking uncertainties in the data into consideration. A related work focused on creating and evaluating flood maps for the city of Jakarta, based on Twitter messages containing reference to flood locations [6].…”

Section: Introductionmentioning

confidence: 99%

Investigating the accuracy of georeferenced social media data for flood mapping: The PetaJakarta.org case study

Ogie

Forehead

2017

2017 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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Georeferenced social media data are gaining increased application in creating near real-time flood maps needed to improve situational awareness in data-starved regions. However, there is growing concern that the georeferenced locations of flood-related social media contents do not always correspond to the actual locations of the flooding event. But to what extent is this true? Without this knowledge, it is difficult to ascertain the accuracy of flood maps created using georeferenced social media contents. This study aims to improve understanding of the extent to which georeferenced locations of social media flood reports deviate from the actual locations of floods. The study analyses flood-related tweets acquired as part of the PetaJakarta.org project implemented in the coastal mega-city of Jakarta and provides insight into the level of accuracy expected with using georeferenced social media data for flood mapping. Importantly, the results reveal that the accuracy of flood maps generated with georeferenced social media data reduces with increase in the size of the minimum mapping unit of the flood map. Finally, an approach is recommended for creating more accurate real time flood maps from crowdsourced social media data.Abstract-Georeferenced social media data are gaining increased application in creating near real-time flood maps needed to improve situational awareness in data-starved regions. However, there is growing concern that the georeferenced locations of flood-related social media contents do not always correspond to the actual locations of the flooding event. But to what extent is this true? Without this knowledge, it is difficult to ascertain the accuracy of flood maps created using georeferenced social media contents. This study aims to improve understanding of the extent to which georeferenced locations of social media flood reports deviate from the actual locations of floods. The study analyses flood-related tweets acquired as part of the PetaJakarta.org project implemented in the coastal mega-city of Jakarta and provides insight into the level of accuracy expected with using georeferenced social media data for flood mapping. Importantly, the results reveal that the accuracy of flood maps generated with georeferenced social media data reduces with increase in the size of the minimum mapping unit of the flood map. Finally, an approach is recommended for creating more accurate real time flood maps from crowdsourced social media data.

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“…Various sources of big data have already been useful for informing disaster risk management and climate adaptation planning. Kusumo et al (2017) used volunteered geographic information through SMD as a source for assessing the desired location and capacity of flood evacuation shelters, while Brouwer et al (2017) used SMD sourced observations of flooding to develop a method for estimating flood extent in Jakarta, Indonesia. In New York City, following the devastating impact of Hurricane Sandy, researchers used Twitter SMD to reveal the geographies of a range of social processes and practices that occurred immediately after the event (Shelton et al, 2014).…”

Section: Big Data Approaches At City-scalementioning

confidence: 99%

Upscaling urban data science for global climate solutions

Creutzig

Lohrey²,

Bai

et al. 2019

Glob. Sustain.

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Non-technical summaryManhattan, Berlin and New Delhi all need to take action to adapt to climate change and to reduce greenhouse gas emissions. While case studies on these cities provide valuable insights, comparability and scalability remain sidelined. It is therefore timely to review the state-of-the-art in data infrastructures, including earth observations, social media data, and how they could be better integrated to advance climate change science in cities and urban areas. We present three routes for expanding knowledge on global urban areas: mainstreaming data collections, amplifying the use of big data and taking further advantage of computational methods to analyse qualitative data to gain new insights. These data-based approaches have the potential to upscale urban climate solutions and effect change at the global scale.

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Probabilistic flood extent estimates from social media flood observations

Cited by 68 publications

References 28 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Investigating the accuracy of georeferenced social media data for flood mapping: The PetaJakarta.org case study

Upscaling urban data science for global climate solutions

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