ERA

Download the full-sized PDF of Discovering Spatial Co-Clustering Patterns in Collision DataDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3WH50

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Discovering Spatial Co-Clustering Patterns in Collision Data Open Access

Descriptions

Other title
Subject/Keyword
non-spatial attributes
hotspots
attribute-value pairs
spatial patterns
collision data
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Li, Dapeng
Supervisor and department
Sander, Joerg (Computing Science)
Nascimento, Mario A. (Computing Science)
Examining committee member and department
Yang, Herbert (Computing Science)
Sander, Joerg (Computing Science)
Bowling, Michael (Computing Science)
Nascimento, Mario A. (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2013-08-26T15:18:34Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
Identifying spatial patterns of collisions is critical for improving the efficiency and effectiveness of traffic enforcement deployment and road safety. Recently, many studies have centred on finding locations with high collision concentration, so-called hotspots. However, most of them only focus on the location information of the collision data, without integrating the non-spatial attributes into analysis. Taking non-spatial attributes into account opens opportunities to reveal attribute-related hotspots that otherwise goes undetected, and can add valuable indicators for explaining those hotspots. In this thesis, we address this problem. We propose a method for identifying the sets of non-spatial attribute-value pairs (AVPs) that together contribute significantly to the spatial clustering of the corresponding collisions. We call such AVP sets Spatial Co-Clustering Patterns (SCCPs). By applying our method on Edmonton’s collision data, we discovered larger numbers of meaningful hotspot patterns than traditional methods did, and revealed the relevant non-spatial indicators for explaining those hotspots.
Language
English
DOI
doi:10.7939/R3WH50
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2014-04-29T21:08:16.296+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 3586404
Last modified: 2015:10:12 11:31:07-06:00
Filename: Li_Dapeng_Fall 2013.pdf
Original checksum: 4dd046923601e974a891ae469e3ac953
Well formed: false
Valid: false
Status message: No document catalog dictionary offset=0
Activity of users you follow
User Activity Date