Visual analytics for spatiotemporal events

Silva, Ricardo Almeida; Pires, João Moura; Datia, Nuno; Santos, Maribel Yasmina; Martins, Bruno; Birra, Fernando

doi:10.1007/s11042-019-08012-2

Cited by 10 publications

(3 citation statements)

References 59 publications

(82 reference statements)

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“…Problem 1 Level of detail -Multidimensional datasets present features in multiple levels of detail (LoD). However, discovering new hidden patterns can be difficult depending on the level of detail presented to the user Silva et al [2019]. To analysts, visualization tools need to incorporate AI and ML algorithms that automatically choose, in the background, the proper LoD to start with the interactive analysis.…”

Section: Multidimensional Visualizations and Artificial Intelligencementioning

confidence: 99%

Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

Kovalerchuk¹,

Andonie²,

Datia³

et al. 2022

Preprint

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This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/Visual Analytics. Chapters included are extended versions of the selected AI and Visual Analytics papers and related symposia at the recent International Information Visualization Conferences (IV2019 and IV2020). AI/ML face a long-standing challenge of explaining models to humans. Models explanation is fundamentally human activity, not only an algorithmic one. In this chapter we aim to present challenges and future directions within the field of Visual Analytics, Visual Knowledge Discovery and AI/ML, and to discuss the role of visualization in visual AI/ML. In addition, we describe progress in emerging Full 2D ML, natural language processing, and AI/ML in multidimensional data aided by visual means.

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Section: Multidimensional Visualizations and Artificial Intelligencementioning

confidence: 99%

Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

Kovalerchuk¹,

Andonie²,

Datia³

et al. 2022

Preprint

Self Cite

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“…The visualisation and visual analytics of events, which forms the second objective of the paper, also remain an active field of study. Interesting research topics in visualisation and visual analytics of spatiotemporal events have emerged in literature over time, including those that focus on visual analytics at multiple levels of details (Silva et al 2019), temporal evolution of events (Lukasczyk et al 2015), and frameworks (Andrienko and Andrienko 2016;Robinson et al 2017). Since the importance of user input and inclusion of domain knowledge in pattern discovery and spatiotemporal analysis has been highlighted in recent studies, this can be fully exploited with the ability to visualise the results and make meaningful deductions.…”

Section: Visual Analytics Of Clustering Eventsmentioning

confidence: 99%

Determining Real-Time Patterns of Lightning Strikes from Sensor Observations

2021

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Transient spatiotemporal events occur within a short interval of time, in a particular location. If such events occur unexpectedly with varying durations, frequencies, and intensities, they pose a challenge for near-real-time monitoring. Lightning strikes are examples of such events and they can have severe negative consequences, such as fires, or they precede sudden flash storms, which can result in damage to infrastructure, loss of Internet connectivity, interruption of electrical power supply, and loss of life or property. Furthermore, they are unexpected, momentary in occurrence, sometimes with high frequency and then again with long intervals between them, their intensity varies considerably, and they are difficult to trace once they have occurred. Despite their unpredictable and irregular nature, timely analysis of lightning events is crucial for understanding their patterns and behaviour so that any adverse effects can be mitigated. However, near-real-time monitoring of unexpected and irregular transient events presents technical challenges for their analysis and visualisation. This paper demonstrates an approach for overcoming some of the challenges by clustering and visualising data streams with information about lightning events during thunderstorms, in real time. The contribution is twofold. Firstly, we detect clusters in dynamic spatiotemporal lightning events based on space, time, and attributes, using graph theory, that is adaptive and does not prescribe number and size of clusters beforehand, and allows for use of multiple clustering criteria and thresholds, and formation of different cluster shapes. Secondly, we demonstrate how the space time cube can be used to visualise unexpected and irregular transient events. Along with the visualisation, we identify the interactive elements required to counter challenges related to visualising unexpected and irregular transient events through space time cubes.

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“…Finding spatio-temporal patterns of crime help us to explain the association among crimes, space, and time. Machine learning approaches focus on the analysis of a single attributes such as robberies or on a specific pattern such as crime hotspots [ 29 ]. Spatio-temporal outliers, hotspots, coupling, and partitioning are very crucial for crime prediction.…”

Section: Introductionmentioning

confidence: 99%

Crime Prediction Using Spatio-Temporal Data

Hossain

Abtahee

Kashem

et al. 2020

Communications in Computer and Information Science

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A crime is an action which constitutes a punishable offence by law. It is harmful for society so as to prevent the criminal activity , it is important to understand crime. Data driven researches are useful to prevent and solve crime. Recent research shows that 50% of the crimes are committed by only handful of offenders. The law enforcement officers need early information about the criminal activity to response and solve the spatio-temporal criminal activity. In this research, supervised learning algorithms are used to predict criminal activity . The proposed data driven system predicts crimes by analyzing San Francisco city criminal activity data set for 12 years. Decision tree and k-nearest neighbor (KNN) algorithms are applied to predict crime. But these two algorithms provide low accuracy in prediction. Then, random forest is applied as an ensemble methods and adaboost is used as a boosting method to increase the accuracy of prediction. However, log-loss is used to measure the performance of classifiers by penalizing false classifications. As the dataset contains highly class imbalance problems, a random undersampling method for random forest algorithm gives the best accuracy. The final accuracy is 99.16% with 0.17% log loss.

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Visual analytics for spatiotemporal events

Cited by 10 publications

References 59 publications

Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions

Determining Real-Time Patterns of Lightning Strikes from Sensor Observations

Crime Prediction Using Spatio-Temporal Data

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