Adepeju, MO orcid.org/0000-0002-9006-4934 and Evans, A orcid.org/0000-0002-3524-1571 (2017) Comparative analysis of two variants of the Knox test: Inferences from space-time pattern analysis. In: Gervasi, O, Murgante, B, Misra, S, Borruso, G, Torre, CM, Rocha, AMAC, Taniar, D, Apduhan, BO, Stankova, E and Cuzzocrea, A, (eds.) Lecture Notes in Computer Science. 17th International Conference on Computer Science and Its Applications (ICCSA 2017), 03-06 Jul 2017, Trieste, Italy. Springer Nature , pp. 770-778. ISBN 978-3-319-62407-5
Abstract
This paper compares two variants of the Knox test in relation to space-time crime pattern analysis. A case study of burglary and ‘stolen-vehicle’ crime data sets of San Francisco city is presented. The comparative analysis shows that while one variant is designed to detect the sizes of the spatio-temporal neighbourhoods at which clustering (hotspots) is prominent within a data set, the other variant is able to reveal the spatial and temporal windows/bands at which crime events are frequently repeated to form clusters (hotspots) across an area.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | (c) 2017, Springer International Publishing AG. This is an author produced version of a paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-62407-5_59 |
Keywords: | Knox test; Space-time; Crime; Repeat patterns; Critical distances |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Nov 2017 17:06 |
Last Modified: | 25 Sep 2019 11:24 |
Status: | Published |
Publisher: | Springer Nature |
Identification Number: | 10.1007/978-3-319-62407-5_59 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124149 |