Dynamic Spatial Pattern Recognition in Count Data

2016 ◽  
pp. 185-202
Author(s):  
Xia Wang ◽  
Ming-Hui Chen ◽  
Rita C. Kuo ◽  
Dipak K. Dey
Keyword(s):  
Pattern Recognition ◽  
Spatial Pattern ◽  
Count Data

scholarly journals Fish Perform Spatial Pattern Recognition and Abstraction by Exclusive Use of Active Electrolocation

2004 ◽  
Vol 14 (9) ◽  
pp. 818-823 ◽  
Author(s):  
Christian Graff ◽  
Gwenaël Kaminski ◽  
Michael Gresty ◽  
Théophile Ohlmann
Keyword(s):  
Pattern Recognition ◽  
Spatial Pattern

scholarly journals Spatial pattern recognition of extreme temperature climatology: assessing HadCM3 simulations via NCEP reanalyses over Europe

2007 ◽  
Vol 22 (2) ◽  
pp. 204-217 ◽  
Author(s):  
P. S. Lucio ◽  
F. C. Conde ◽  
A. M. Ramos
Keyword(s):  
Pattern Recognition ◽  
Spatial Pattern ◽  
Model Building ◽  
Climate Model ◽  
Extreme Values ◽  
Extreme Temperature ◽  
Spatial Models ◽  
Climate Extremes ◽  
The Usa ◽  
Hadley Centre

This article attempts to quantify the spatial uncertainties associated with extreme temperature’s response, by assessing data derived from climate model. This is undertaken by a comparison of the spatial pattern of a long-term time-series aggregation (1960/61-1989/90) for extreme temperatures simulated by a particular GCM (the UK Met Office - Hadley Centre climate model, HadCM3) to that of the USA NCAR NCEP Reanalyses, which are considered as ‘truth’, over the MICE (Modelling the Impacts of Climate Extremes - EU Project) spatial domain. Since evaluation of models is crucial to assessing future scenarios, the aim of this study is to investigate whether the extreme values predicted by the HadCM3 climate model can simulate those produced by NCEP Reanalyses, assuming that the extremes of both models are realizations of the same spatial stochastic process. To get more useful information about the uncertainties surrounding spatial climate projection, one also has to analyze the pattern of temperature extremes in terms of their anomalies. A common technical issue in the assessment of numerical spatial models is based on the Principal Components Analysis and Bayesian Classification for spatial pattern recognition. These methodologies are very important and useful for guiding an evolutionary statistical model-building process. This study leads to the conclusion that the HadCM3 Simulations do not realistically reproduce the NCEP Reanalyses, despite the fact that the climatology of extremes has demonstrated very similar spatial patterns. It is likely therefore that such instability may persist in the future.

Design of a Spectral–Spatial Pattern Recognition Framework for Risk Assessments Using Landsat Data—A Case Study in Chile

2014 ◽  
Vol 7 (3) ◽  
pp. 917-928 ◽  
Author(s):  
Andreas Christian Braun ◽  
Carolina Rojas ◽  
Cristian Echeverri ◽  
Franz Rottensteiner ◽  
Hans-Peter Bahr ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Spatial Pattern ◽  
Risk Assessments ◽  
Landsat Data
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