Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter

Panteras, George; Wise, Sarah; Xu, Lian-Zeng-Ji; Croitoru, Arie; Crooks, Andrew; Stefanidis, Anthony

doi:10.1111/tgis.12122

Cited by 53 publications

(34 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When using social media, biases may also be present, for example, from a lack of digital engagement within certain demographics of the populations of particular areas (Xiao et al 2015). Panteras et al (2014) cross-reference geotagged points of images mined from Flickr and tweets to estimate spatial footprints events. The authors extract toponyms expressed in tweets to aid in estimating the viewing direction of Flickr images depicting a wildfire event, leading to a more accurate delineation of the event.…”

Section: Social Mediamentioning

confidence: 99%

Rapid flood inundation mapping using social media, remote sensing and topographic data

2017

View full text Add to dashboard Cite

Flood events cause substantial damage to urban and rural areas. Monitoring water extent during large-scale flooding is crucial in order to identify the area affected and to evaluate damage. During such events, spatial assessments of floodwater may be derived from satellite or airborne sensing platforms. Meanwhile, an increasing availability of smartphones is leading to documentation of flood events directly by individuals, with information shared in real-time using social media. Topographic data, which can be used to determine where floodwater can accumulate, are now often available from national mapping or governmental repositories. In this work, we present and evaluate a method for rapidly estimating flood inundation extent based on a model that fuses remote sensing, social media and topographic data sources. Using geotagged photographs sourced from social media, optical remote sensing and high-resolution terrain mapping, we develop a Bayesian statistical model to estimate the probability of flood inundation through weights-of-evidence analysis. Our experiments were conducted using data collected during the 2014 UK flood event and focus on the Oxford city and surrounding areas. Using the proposed technique, predictions of inundation were evaluated against ground-truth flood extent. The results report on the quantitative accuracy of the multisource mapping process, which obtained area under receiver operating curve values of 0.95 and 0.93 for model fitting and testing, respectively.

show abstract

Section: Social Mediamentioning

confidence: 99%

Rapid flood inundation mapping using social media, remote sensing and topographic data

2017

View full text Add to dashboard Cite

show abstract

“…Recently, a number of machine-learning approaches have been used to investigate electoral predictions (Gayo-Avello, 2013), stock market flows (Zhang, Fuehres, & Gloor, 2011), flu trends (Culotta, 2010;Ritterman, Osborne, & Klein, 2009), natural disasters (Fraustino, Liu, & Jin, 2012;Resch, Usländer, & Havas, 2017), or to detect large events (Lee & Sumiya, 2010;Li, Lei, Khadiwala, & Chang, 2012;Weng & Lee, 2011), even in near real-time (Zhao, Zhong, Wickramasuriya, & Vasudevan, 2011) and with respect to their impacts (Panteras et al, 2015).…”

Section: Urban Planning Social Media and Planned Large Eventsmentioning

confidence: 99%

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Kovács-Györi

Ristea

Havas

et al. 2018

View full text Add to dashboard Cite

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

show abstract

“…A Hot Spot Analysis tool is available in ArcMap (ESRI Products, Redlands, CA) for calculating Getis-Ord Gi* statistic, and this study used ArcMap 10.1. Getis-Ord Gi* was adopted because of its ability to test the statistical significance of the results [56]. Following Bruce et al [57], a symmetric one/zero spatial weight matrix (i.e., the spatial weight between a given feature and each of its surrounding features is one if the distance between them is within an assigned distance band, and is zero if otherwise) was applied to generating the Gi* statistics using fixed distance band weighting.…”

Section: Discussionmentioning

confidence: 99%

Towards Enhancing Integrated Pest Management Based on Volunteered Geographic Information

Yan

Feng

Chang

2017

IJGI

View full text Add to dashboard Cite

Integrated pest management (IPM) involves integrating multiple pest control methods based on site information obtained through inspection, monitoring, and reports. IPM has been deployed to achieve the judicious use of pesticides and has become one of the most important methods of securing agricultural productivity. Despite the efforts made to strengthen IPM during the past decades, overuse as well as indiscriminate use of pesticides is still common. This problem is particularly serious in underserved farming communities which suffer from ineffectiveness with respect to pest management information collection and dissemination. The recent development of volunteered geographic information (VGI) offers an opportunity to the general public to create and receive ubiquitous, cost-effective, and timely geospatial information. Therefore, this study proposes to enhance IPM through establishing a VGI-based IPM. As a starting point of this line of research, this study explored how such geospatial information can contribute to IPM enhancement. Based on this, a conceptual framework of VGI interaction was built to guide the establishment of VGI-based IPM. To implement VGI-based IPM, a mobile phone platform was developed. In addition, a case study was conducted in the town of Shuibian in Jiangxi province of China to demonstrate the effectiveness of the proposed approach. In the case study, by analyzing infestation incidents of an overwintering outbreak of striped rice stem borers voluntarily reported by farmers through mobile phones, spatiotemporal infestation patterns of the borers throughout the study area were revealed and disseminated to the farmers. These patterns include the dates and degree-days the pest infestations intensified, and the orientation or spatial structural variations of the clustering of the infestations. This case study showcased the unique merit of VGI in enhancing IPM, namely the acquisition of previously unrecorded spatial data in a cost-effective and real-time manner for discovering and disseminating previously unknown pest management knowledge.

show abstract

Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter

Cited by 53 publications

References 60 publications

Rapid flood inundation mapping using social media, remote sensing and topographic data

Rapid flood inundation mapping using social media, remote sensing and topographic data

#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data

Towards Enhancing Integrated Pest Management Based on Volunteered Geographic Information

Contact Info

Product

Resources

About