Geography Reference
In-Depth Information
This concept is used in statistical analysis for climate extreme change detection.
However, a similar concept can be introduced to ST cluster detection where a
return period of disease outbreak can be estimated based on the probability that
an outbreak of the same extent in each time step (e.g., month). This may help the
understanding the spreading pattern of epidemic diseases.
17.6
Conclusion and Future Work
This chapter presents an interdisciplinary review of change pattern mining tech-
niques in time series analysis, spatial statistics, and remote sensing to facilitate
cross-fertilization of techniques and ideas across disciplines. A taxonomy based on
ST footprint was proposed to better address the challenges posed by terminology
differences across these disciplines. Cross-fertilization ideas such as the application
of ST cluster detection techniques on climate change study has been discussed.
More ideas might be generated when more interdisciplinary comparison of tech-
niques are made. In the future, we wish to integrate techniques from a broader range
of disciplines to enrich cross-fertilization ideas. We also plan to explore new input
data types and patterns of change pattern mining. Finally, we plan to investigate
other taxonomies to classify change pattern mining techniques.
Acknowledgement This material is based upon work supported by the National Science Foun-
dation under Grant No. 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under
Grant No. HM1582-08-1-0017 and HM0210-13-1-0005, and the University of Minnesota under
the OVPR U-Spatial.
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