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non-spatial attributes, hotspots, attribute-value pairs, spatial patterns, collision data

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.





Autor: Li, Dapeng

Fuente: https://era.library.ualberta.ca/


Introducción



“Planning for the long-term safety of a community is one of the most meaningful things a municipal government can do.
I am determined to make road safety a priority, now and for the future.” – Stephen Mandel, Mayor of Edmonton University of Alberta D ISCOVERING S PATIAL C O -C LUSTERING PATTERNS IN C OLLISION DATA by Dapeng Li A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science c Dapeng Li Fall 2013 Edmonton, Alberta 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 whatever without the author’s prior written permission. Dedication The thesis is dedicated to my dear father Dacheng Li, to my loving mother Xiaoai Niu, and to my beloved brother Min Li.
Their support, encouragement, and constant love have sustained me throughout my life. 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, socalled 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 attributerelated hotspots that otherwise ...





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