Visual analytics for spatiotemporal events


Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user’ perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts’ perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.

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This work has been supported by FCT - Fundação para a Ciência e Tecnologia MCTES, UID/CEC/04516/2013 (NOVA LINCS), UID/CEC/00319/2019 (ALGORITMI), and UID/CEC/50021/2019 (INESC-ID).

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Appendix : Abstracts implemented

Appendix : Abstracts implemented

Measure Description Global Spatial Temporal
Bray-Curtis Calculates the similarity   
Similarity for based on the number of    
Synthesis granular synthesis, between    
  consecutive temporal grains    
Correlation Index Correlation between the   
For Atoms number of atoms of    
  consecutive temporal grains    
Correlation Index Correlation between the   
For Synthesis number of granular    
  synthesis of consecutive    
  temporal grains    
Dice Similarity Dice index (event / no   
(Binary) event) between conse-    
  cutive temporal grains    
Jaccard Similarity Jaccard index (event / no   
(Binary) event) between conse-    
  cutive temporal grains    
Gower Similarity Similarity (event / no   
(Binary) event) between conse-    
  cutive temporal grains    
Moran’s I Calculates the spatial   
  autocorrelation among    
  nearby locations, given    
  a domain specific variable    
Nearest Neighbor Measures the level of  
(NN) clustering    
Z-score Nearest Measures the z-score  
Neighbor (z-NN) of the level of NN    
Spatial Scope Measures the spatial   
Spatial Consecutive Measures the distance   
Distance between between consecutive    
Centers of Mass centers of mass    
Center’s Mass Measures the position  
Positioning of the centers of mass    
Reduction Rate (%) Measures the reduction   
  of atoms used    
Average Atoms in Measures the average of   
spatiotemporal atoms indexed by spatio-    
granules (%) temporal granules    
Collision Rate (%) Percentage of spatiotemporal
  granules with events, where    
  atom collisions exists    
Occupation Rate (%) Percentage of granules
  with events    
Granular Mantel Measures the spatio-   
Bounded and temporal interaction    
Frequency Rate (%) Percentage of events  
  happened in granules    
Bray-Curtis Calculates the similarity   
Similarity based on the counts of    
for Atoms atoms, between conse-    
  cutive temporal grains    

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Silva, R.A., Pires, J.M., Datia, N. et al. Visual analytics for spatiotemporal events. Multimed Tools Appl 78, 32805–32847 (2019).

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  • Data visualization
  • Spatiotemporal patterns
  • Multiple levels of detail
  • Visual analytics