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Article

Detecting region outliers in meteorological data

Authors:

Yufeng KouAuthors Info & Claims

GIS '03: Proceedings of the 11th ACM international symposium on Advances in geographic information systems

Pages 49 - 55

https://doi.org/10.1145/956676.956683

Published: 07 November 2003 Publication History

Abstract

Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal important and valuable information from large spatial data sets. In the field of meteorology, for example, spatial outliers can be associated with disastrous natural events such as tornadoes, hurricane, and forest fires. Previous study of spatial outlier mainly focuses on point data. However, in the meteorological data or other applications, spatial outliers are frequently represented in region, i.e., a group of points, with two dimensions or even three dimensions, and the previous point-based approaches may not be appropriate to be used. As region outliers are commonly multi-scale objects, wavelet analysis is an effective tool to study them. In this paper, we propose a wavelet analysis based approach to detect region outliers. We discuss the region outlier detection problem and design a suite of algorithms to effectively discover them. The algorithms were implemented and evaluated with a real-world meteorological data set.

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Cited By

Sadatrazavi AMotlagh MNoorpoor AEhsani A(2022)Predicting Wildfires Occurrences Using Meteorological ParametersInternational Journal of Environmental Research10.1007/s41742-022-00460-316:6Online publication date: 17-Oct-2022
https://doi.org/10.1007/s41742-022-00460-3
Zhang ZZhu MQiu JLiu CZhang DQi J(2019)Outlier Detection Based on Cluster Outlier Factor and Mutual DensityComputational Intelligence and Intelligent Systems10.1007/978-981-13-6473-0_28(319-329)Online publication date: 8-Feb-2019
https://doi.org/10.1007/978-981-13-6473-0_28
Sheng HXiao JCheng YNi QWang S(2018)Short-Term Solar Power Forecasting Based on Weighted Gaussian Process RegressionIEEE Transactions on Industrial Electronics10.1109/TIE.2017.271412765:1(300-308)Online publication date: Jan-2018
https://doi.org/10.1109/TIE.2017.2714127
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Index Terms

Detecting region outliers in meteorological data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

GIS '03: Proceedings of the 11th ACM international symposium on Advances in geographic information systems

November 2003

180 pages

ISBN:1581137303

DOI:10.1145/956676

Program Chairs:
Erik Hoel
ESRI, USA
,
Philippe Rigaux
LRI, University Paris-Sud Orsay, France

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 07 November 2003

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CIKM03

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CIKM03: 12th International Conference on Information and Knowledge Management

November 7 - 8, 2003

Louisiana, New Orleans, USA

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Sadatrazavi AMotlagh MNoorpoor AEhsani A(2022)Predicting Wildfires Occurrences Using Meteorological ParametersInternational Journal of Environmental Research10.1007/s41742-022-00460-316:6Online publication date: 17-Oct-2022
https://doi.org/10.1007/s41742-022-00460-3
Zhang ZZhu MQiu JLiu CZhang DQi J(2019)Outlier Detection Based on Cluster Outlier Factor and Mutual DensityComputational Intelligence and Intelligent Systems10.1007/978-981-13-6473-0_28(319-329)Online publication date: 8-Feb-2019
https://doi.org/10.1007/978-981-13-6473-0_28
Sheng HXiao JCheng YNi QWang S(2018)Short-Term Solar Power Forecasting Based on Weighted Gaussian Process RegressionIEEE Transactions on Industrial Electronics10.1109/TIE.2017.271412765:1(300-308)Online publication date: Jan-2018
https://doi.org/10.1109/TIE.2017.2714127
Cipolla EManiscalco URizzo RStabile DVella F(2016)Analysis and visualization of meteorological emergenciesJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-016-0351-x8:1(57-68)Online publication date: 23-Feb-2016
https://doi.org/10.1007/s12652-016-0351-x
Cipolla EVella F(2014)Identification of Spatio-Temporal Outliers through Minimum Spanning TreeProceedings of the 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems10.1109/SITIS.2014.25(248-255)Online publication date: 23-Nov-2014
https://dl.acm.org/doi/10.1109/SITIS.2014.25
Rahmani AAfra SZarour OAddam OKoochakzadeh NKianmehr KAlhajj RRokne J(2014)Graph-based approach for outlier detection in sequential data and its application on stock market and weather dataKnowledge-Based Systems10.1016/j.knosys.2014.02.00861:1(89-97)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1016/j.knosys.2014.02.008
Liu XChen FLu C(2014)On detecting spatial categorical outliersGeoinformatica10.1007/s10707-013-0188-918:3(501-536)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1007/s10707-013-0188-9
Das KBhaduri KVotava P(2011)Distributed anomaly detection using 1-class SVM for vertically partitioned dataStatistical Analysis and Data Mining10.1002/sam.101254:4(393-406)Online publication date: 1-Aug-2011
https://dl.acm.org/doi/10.1002/sam.10125
Junghans CGertz M(2010)Modeling and prediction of moving region trajectoriesProceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming10.1145/1878500.1878507(23-30)Online publication date: 2-Nov-2010
https://dl.acm.org/doi/10.1145/1878500.1878507
Liu XLu CChen FAgrawal DZhang PAbbadi AMokbel M(2010)Spatial outlier detectionProceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/1869790.1869841(370-379)Online publication date: 2-Nov-2010
https://dl.acm.org/doi/10.1145/1869790.1869841
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