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Visual analytics for spatiotemporal events

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Abstract

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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Notes

  1. The website: www.timeviz.net

  2. VAST prototype can be tested online at http://staresearch.net/resource#prototypes.

  3. http://mathworld.wolfram.com/z-Score.html

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Acknowledgments

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).

Author information

Authors and Affiliations

  1. ISEL, Instituto Politécnico de Lisboa, Lisbon, Portugal

    Ricardo Almeida Silva & Nuno Datia

  2. NOVA LINCS, FCT, Universidade NOVA de Lisboa, Lisbon, Portugal

    Ricardo Almeida Silva, João Moura Pires, Nuno Datia & Fernando Birra

  3. ALGORITMI Research Centre, University of Minho, Braga, Portugal

    Maribel Yasmina Santos

  4. INESC-ID and IST, University of Lisbon, Lisbon, Portugal

    Bruno Martins

Authors
  1. Ricardo Almeida Silva
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  2. João Moura Pires
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  3. Nuno Datia
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  4. Maribel Yasmina Santos
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  5. Bruno Martins
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Correspondence to Nuno Datia.

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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

 

∙

 
 

extent

   

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

   

Normalized

    

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). https://doi.org/10.1007/s11042-019-08012-2

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