GeoVisuals: a visual analytics approach to leverage the potential of spatial videos and associated geonarratives

Jamonnak, Suphanut; Zhao, Ye; Curtis, Andrew; Al-Dohuki, Shamal; Ye, Xinyue; Kamw, Farah; Yang, Jing

doi:10.1080/13658816.2020.1737700

Cited by 20 publications

(11 citation statements)

References 54 publications

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“…While as mentioned there have been various visual analytic methods and tools developed (e.g., [ 6 , 26 ]) to fully leverage geo-spatial data, spatial videos combined with geonarratives provides both a new data direction and associated challenges. The team had previously built GeoVisuals [ 10 ] to interactively manage, visualize, and analyze spatial video and narratives using a set of visualization widgets and interaction functions. This offered a variety of data investigations based on keywords extracted from geo-narratives tied to images and locations on the videos.…”

Section: Related Workmentioning

confidence: 99%

“…Second, when the existing VA system, GeoVisuals [ 10 ], was used, domain experts could perform visual analytic over visual+semantic information. However, they identified several limitations: (1) GeoVisuals did not support data capture and processing.…”

Section: Overviewmentioning

confidence: 99%

“…Existing visual analytic (VA) tools for analyzing vehicle/human trajectories (e.g., [ 6 ]), geo-spatial events (e.g., [ 7 ]), geo-tagged social media (e.g., [ 8 ]), and multimedia data (e.g., [ 9 ]) cannot be directly applied to these types of geo-coded video datasets. Recently, a GeoVisuals system [ 10 ] was developed to visually explore similar spatially encoded datasets. However, this system is not easily applied by a community user (see “ Motivation and tasks ” scetion) due to (1) the lack of processing functions and system integration, and (2) the complexity restricting non-technical users.…”

Section: Introductionmentioning

confidence: 99%

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VisualCommunity: a platform for archiving and studying communities

et al. 2022

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VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific advances through use case examples including COVID-19 related scenarios.

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Section: Related Workmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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VisualCommunity: a platform for archiving and studying communities

et al. 2022

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“…Jamonnak et al. ( 2020 ) combined location information associated with videos and the narrations of those videos to show their locations on the map and the topics and sentiment discussed at those locations. Xu et al.…”

Section: Related Workmentioning

confidence: 99%

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

Pezanowski

Mitra

MacEachren

2022

KN J. Cartogr. Geogr. Inf.

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Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable. Supplementary Information The online version contains supplementary material available at 10.1007/s42489-022-00098-3.

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“…With the increasing popularity of smartphones, users have captured massive quantities of video data, which they have uploaded to internet platforms, such as YouTube and TikTok. A geotagged video contains location information and is rich in spatiotemporal semantics (Borko, 2008;Jamonnak et al, 2020). If the video camera is installed on a moving platform, its shooting path becomes a moving trajectory (Han, Cui, Kong, Qin, & Fu, 2016;Lewis, Fotheringham, & Winstanley, 2011;Milosavljević, Rančić, Dimitrijević, Predić, & Mihajlović, 2016).…”

Section: Tigrid For Geotagged Video Retrievalmentioning

confidence: 99%

TiGrid: A raster‐based method for time‐interval information representation with geotagged videos as an example

Han

Kong

Cui

et al. 2021

Transactions in GIS

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Spatiotemporal representation is one of the critical areas receiving ongoing interest in GIScience. The ever-growing spatiotemporal big data, however, pose considerable challenges to temporal representation as existing methods predominantly use one-dimensional chronological timeline representations. Aiming to represent the lifespans of spatiotemporal objects, a temporal interval grid (TiGrid) is proposed based on a raster structure in two-dimensional space.The TiGrid is the lower-triangular part of a square matrix of order N, in which the column and row represent the start time and end time, respectively. Using the graphical transformation method, the lower-triangular matrix of the TiGrid is transformed to a rectangle matrix. The cells in a TiGrid can be converted into absolute world time using metadata such as the time resolution and start time. The TiGrid generating algorithm, the temporal topological relations, and the unary or binary operations in a TiGrid are defined. Furthermore, spatiotemporal constraint and snapshot retrieval methods are introduced based on the spatial semantics in the TiGrid.A prototype performed evaluation with the case of geotagged video retrieval. The experimental results show that a TiGrid with spatial indexing can efficiently retrieve mass objects with time-interval information. The TiGrid has the potential to be a new way for spatiotemporal modeling and geovisualization.

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GeoVisuals: a visual analytics approach to leverage the potential of spatial videos and associated geonarratives

Cited by 20 publications

References 54 publications

VisualCommunity: a platform for archiving and studying communities

VisualCommunity: a platform for archiving and studying communities

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

TiGrid: A raster‐based method for time‐interval information representation with geotagged videos as an example

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