Location Inference of Social Media Posts at Hyper-Local Scale

McClanahan, Brian; Gokhale, Swapna S.

doi:10.1109/ficloud.2015.71

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Locational information of tweets, for example, can be uncertain because geotags are available for only a very small number of tweets and may deviate from the actual location of the observation (Hahmann et al, 2014). McClanahan and Gokhale (2015), who derived locations from the text in tweets, indicate that the locations they derived from messages in New York City had an average error of 1.72 km. Eilander et al (2016) were the first to give an estimate of the likelihood of areas being flooded by harvesting tweets.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic flood extent estimates from social media flood observations

Brouwer¹,

Eilander²,

Loenen³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, create a growing need for accurate and timely flood maps. In this paper we present and evaluate a method to create deterministic and probabilistic flood maps from Twitter messages that mention locations of flooding. A deterministic flood map created for the December 2015 flood in the city of York (UK) showed good performance (F (2) = 0.69; a statistic ranging from 0 to 1, with 1 expressing a perfect fit with validation data). The probabilistic flood maps we created showed that, in the York case study, the uncertainty in flood extent was mainly induced by errors in the precise locations of flood observations as derived from Twitter data. Errors in the terrain elevation data or in the parameters of the applied algorithm contributed less to flood extent uncertainty. Although these maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.

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Section: Introductionmentioning

confidence: 99%

Probabilistic flood extent estimates from social media flood observations

Brouwer¹,

Eilander²,

Loenen³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…If the VGI is a picture, even if the geotagged position is in a wrong place, the image could provide landmarks to place the correct position of the observation, whose location error can be lower than the resolution of the large-scale hydraulic model, thus negligible. If the VGI is a text message from a social platform or it is an image without any recognizable landmark, the geotagged position of the VGI can vary considerably depending on its type (McClanahan and Gokhale 2015;Brouwer et al 2017). The perturbation of the VGI observation given by the positioning error for the i-element of the ensemble can be expressed as a noise error normally distributed with zero mean and variance R VGI loc :…”

Section: Observation Errors In Crowdsourced Datamentioning

confidence: 99%

Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping

Annis

Nardi

2019

Geo-spatial Information Science

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Crowdsourced data can effectively observe environmental and urban ecosystem processes. The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems (EWS) to better perform while support decision-making to reduce the fatalities and economic losses due to inundation hazard. In this work, we develop a Data Assimilation (DA) method integrating Volunteered Geographic Information (VGI) and a 2D hydraulic model and we test its performances. The proposed framework seeks to extend the capabilities and performances of standard DA works, based on the use of traditional in situ sensors, by assimilating VGI while managing and taking into account the uncertainties related to the quality, and the location and timing of the entire set of observational data. The November 2012 flood in the Italian Tiber River basin was selected as the case study. Results show improvements of the model in terms of uncertainty with a significant persistence of the model updating after the integration of the VGI, even in the case of use of few-selected observations gathered from social media. This will encourage further research in the use of VGI for EWS considering the exponential increase of quality and quantity of smartphone and social media user worldwide.

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Section: Introductionmentioning

confidence: 99%

response to RC2

Eilander¹

2017

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The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, create a growing need for accurate and timely flood maps. In this paper we present and evaluate a method to create deterministic and probabilistic flood maps from Twitter messages that mention locations of flooding. A deterministic flood map created for the December 2015 flood in the city of York (UK) showed good performance (F (2) = 0.69; a statistic ranging from 0 to 1, with 1 expressing a perfect fit with validation data). The probabilistic flood maps we created showed that, in the York case study, the uncertainty in flood extent was mainly induced by errors in the precise locations of flood observations as derived from Twitter data. Errors in the terrain elevation data or in the parameters of the applied algorithm contributed less to flood extent uncertainty. Although these maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Location Inference of Social Media Posts at Hyper-Local Scale

Cited by 7 publications

References 10 publications

Probabilistic flood extent estimates from social media flood observations

Probabilistic flood extent estimates from social media flood observations

Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping

response to RC2

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