Analysis of spatial autocorrelation for traffic accident data based on spatial decision tree

Bimal Ghimire, Shrutilipi Bhattacharjee, Soumya K. Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

With rapid increase of scope, coverage and volume of geographic datasets, knowledge discovery from spatial data have drawn a lot of research interest for last few decades. Traditional analytical techniques cannot easily discover new, implicit patterns, and relationships that are hidden into geographic datasets. The principle of this work is to evaluate the performance of traditional and spatial data mining techniques for analysing spatial certainty, such as spatial autocorrelation. Analysis is done by classification technique, i.e. a Decision Tree (DT) based approach on a spatial diversity coefficient. ID3 (Iterative Dichotomiser 3) algorithm is used for building the conventional and spatial decision trees. A synthetically generated spatial accident dataset and real accident dataset are used for this purpose. The spatial DT (SDT) is found to be more significant in spatial decision making.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 4th International Conference on Computing for Geospatial Research and Application, COM.Geo 2013
Pages111-115
Number of pages5
DOIs
StatePublished - 2013
Event2013 4th International Conference on Computing for Geospatial Research and Application, COM.Geo 2013 - San Jose, CA, United States
Duration: Jul 22 2013Jul 24 2013

Publication series

NameProceedings - 2013 4th International Conference on Computing for Geospatial Research and Application, COM.Geo 2013

Conference

Conference2013 4th International Conference on Computing for Geospatial Research and Application, COM.Geo 2013
Country/TerritoryUnited States
CitySan Jose, CA
Period7/22/137/24/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

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