Spatiotemporal Data Mining: A Computational Perspective
Abstract
:1. Introduction
2. Input: Spatial and Spatiotemporal Data
2.1. Types of Spatial and Spatiotemporal Data
Spatial Data  Temporal Snapshots (Time Series)  Temporal Change (Delta/Derivative)  Events/Processes  

object model  point(s) 
 displacement/motion (e.g., Brownian motion, random walk), speed/acceleration  spatial/spatiotemporal point process: Poisson, Cox, or Cluster process 
line(s)  line trajectories  motion/extension/rotation, deformation, split/merge  line process  
polygon(s)  polygon trajectories  motion/expansion/rotation/ deformation, split/merge  flat process  
field model  regular, irregular  raster time series  change across raster snapshots  cellular automation 
spatial network model  graph  spatiotemporal network:
 addition or removal of nodes and edges 

2.2. Data Attributes and Relationships
Attributes  Categories  Relationships 

nonspatial 
 Explicit

spatial 
 Often implicit

Spatial Data  Temporal Snapshots (Time Series)  Change (Delta/Derivative)  Event/Process  

object model  point(s), line(s), polygon(s) 



field model  regular irregular 
 local, focal, zonal change across snapshots [29]  cellular automation [55] 
spatial network  graph 
 change in centrality, connectivity  spatiotemporal coupling of network events 
3. Statistical Foundations
3.1. Spatial Statistics for Different Types of Spatial Data
Spatial Model  Spatial Statistics  Spatiotemporal Statistics  

object model  point(s)  Geostatistics (point reference data)
 Statistics for spatial time series

Spatial Point Processes
 Spatiotemporal Point Processes
 
line(s)  line process  
polygon(s)  flat process  
field model  regular, irregular  Lattice Statistics (areal data model)
 Statistics for raster time series

spatial network  graph  Spatial Network Statistics

3.2. Spatiotemporal Statistics
4. Output Pattern Families
4.1. Spatiotemporal Outlier
4.1.1. What are Spatiotemporal Outliers?
4.1.2. Application Domains
4.1.3. Statistical Foundation
4.1.4. Common Approaches
4.2. Spatiotemporal Couplings and TeleCouplings
4.2.1. What are Spatiotemporal Couplings and TeleCouplings?
4.2.2. Application Domains
4.2.3. Statistical Foundation
4.2.4. Common Approaches
4.3. Spatiotemporal Prediction
4.3.1. What is Spatiotemporal Prediction?
4.3.2. Application Domains
4.3.3. Statistical Foundation
4.3.4. Common Approaches
4.4. Spatiotemporal Partitioning and Summarization
4.4.1. What is Spatiotemporal Partitioning and Summarization?
4.4.2. Application Domains
4.4.3. Statistical Foundation
4.4.4. Common Approaches
Data Types  Partition Definition  Summarization 

classical data  partition of rows of records  aggregate statistics: sum, count, mean, etc. 
spatial data  partition of Euclidean space  representatives: centroids, medoids, etc. 
partition of spatial network  representatives: K main routes, etc.  
spatiotemporal data  partition of trajectories on a spatial or spatiotemprol network  representatives: K primary corridors, etc. 
4.5. Spatiotemporal Hotspots
4.5.1. What are Spatiotemporal Hotspots?
4.5.2. Application Domains
4.5.3. Statistical Foundation
4.5.4. Common Approaches
4.6. Spatiotemporal Change
4.6.1. What are Spatiotemporal Changes and Change Footprints
4.6.2. Common Approaches
5. Spatial and Spatiotemporal Analysis Tools
6. Research Trend and Future Research Needs
7. Summary
Acknowledgments
Author Contributions
Conflicts of Interest
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Shekhar, S.; Jiang, Z.; Ali, R.Y.; Eftelioglu, E.; Tang, X.; Gunturi, V.M.V.; Zhou, X. Spatiotemporal Data Mining: A Computational Perspective. ISPRS Int. J. GeoInf. 2015, 4, 23062338. https://doi.org/10.3390/ijgi4042306
Shekhar S, Jiang Z, Ali RY, Eftelioglu E, Tang X, Gunturi VMV, Zhou X. Spatiotemporal Data Mining: A Computational Perspective. ISPRS International Journal of GeoInformation. 2015; 4(4):23062338. https://doi.org/10.3390/ijgi4042306
Chicago/Turabian StyleShekhar, Shashi, Zhe Jiang, Reem Y. Ali, Emre Eftelioglu, Xun Tang, Venkata M. V. Gunturi, and Xun Zhou. 2015. "Spatiotemporal Data Mining: A Computational Perspective" ISPRS International Journal of GeoInformation 4, no. 4: 23062338. https://doi.org/10.3390/ijgi4042306