Adepeju, MO orcid.org/0000-0002-9006-4934 and Evans, A orcid.org/0000-0002-3524-1571 (2017) Investigating the impacts of training data set length (T) and the aggregation unit size (M) on the accuracy of the self-exciting point process (SEPP) hotspot method. In: Proceedings of the 2017 International Conference on GeoComputation. International Conference on GeoComputation, 04-07 Sep 2017, Leeds, UK. Centre for Computational Geography, University of Leeds .
Abstract
This study examines the impacts of two variables; the training data lengths (T) and the aggregation unit sizes (M); on the accuracy of the self-exciting point process (SEPP) model during crime prediction. A case study of three crime types in the South Chicago area is presented, in which different combinations of values of T and M are used for 100 daily consecutive crime predictions. The results showed two important points regarding the SEPP model: first is that large values of T are likely to improve the accuracy of the SEPP model and second is that, a small aggregation unit, such as a 50m x 50m grid, is better in terms of capturing local repeat and near-repeat patterns of crimes.
Metadata
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Copyright, Publisher and Additional Information: | This is an author produced version of a paper presented at the 2017 International Conference on GeoComputation. | ||||
Keywords: | self-exciting; point process; crime prediction; temporal; aggregation | ||||
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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) | ||||
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Depositing User: | Symplectic Publications | ||||
Date Deposited: | 16 Nov 2017 16:35 | ||||
Last Modified: | 29 Jan 2018 11:17 | ||||
Published Version: | http://www.geocomputation.org/2017/papers/5.pdf | ||||
Status: | Published | ||||
Publisher: | Centre for Computational Geography, University of Leeds |