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Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

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Page 1: Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

Interesting Interval Discovery on Spatiotemporal Datasets

Csci 8715 Fall 2013

Page 2: Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

Sub-path of Abrupt Change

• Spatial sub-path of Abrupt Change[1]– Sharp change in vegetation cover– Transition between ecological zones (ecotones)– Vulnerable to climate change– Moves in response to climate change

The change is persistent and rapid

W1=[12N, 17N]

A snapshot of vegetation cover in Africa [6]

Page 3: Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

Sub-path of Abrupt Change

• Temporal sub-path (interval) of Abrupt Change[1]৹ Abrupt shift in precipitation, temperature, etc. ৹ Climate change detection.

Raw Sahel precipitation anomaly (JJASO) Smoothed Sahel precipitation anomaly (JJASO)

Page 4: Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

Case Study Output

AVG{∆}AVG≥α{∆}

• Data: NDVI by GIMMS, Africa, 1981 August. Resolution: 8km. Smoothed within 1x1 degree.• Path: along each longitude (south north)• Interest measure: (Slope) Sameness degree

– ∆ : unit slope

• Thresholds: α= 20% percentile, SD ≥0.5

Page 5: Interesting Interval Discovery on Spatiotemporal Datasets Csci 8715 Fall 2013

Potential Project ideas

• Explore other interest measures to find interesting interval patterns– High correlations time periods between two time series– High variation periods of time series

• Implement an R software[2] package and enable user defined interest measure

[1]. Xun Zhou, Shashi Shekhar, Pradeep Mohan, Stefan Liess, and Peter K. Snyder. "Discovering interesting sub-paths in spatiotemporal datasets: A summary of results." In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 44-53. ACM, 2011.

[2]. The R project for statistical computing: http://www.r-project.org/