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Simulation Study using Average Difference Algorithm to Analyze the Outlierness Degree of Spatial Observations

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Attribute values are the main elements in calculating degree of outlierness of spatial objects. The problem arises when the spatial outliers with extreme values are the nearest neighbors of a central object. In this study, several scenarios are simulated to verify the effect of spatial outliers' extreme values to the degree of outlierness of its nearest neighbors, based on Average Difference Algorithm. The results confirmed the effect can lead to falsely detected spatial outliers. The algorithm detect the true spatial outliers correctly if their values are three sigma away from the mean attribute values of its nearest neighbors.

Original languageEnglish
Title of host publicationICICoS 2020 - Proceeding
Subtitle of host publication4th International Conference on Informatics and Computational Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195261
DOIs
Publication statusPublished - 10 Nov 2020
Event4th International Conference on Informatics and Computational Sciences, ICICoS 2020 - Semarang, Indonesia
Duration: 10 Nov 202011 Nov 2020

Publication series

NameICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences

Conference

Conference4th International Conference on Informatics and Computational Sciences, ICICoS 2020
Country/TerritoryIndonesia
CitySemarang
Period10/11/2011/11/20

Keywords

  • Average Difference algorithm
  • economic growth
  • spatial outliers

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