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
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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 |
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) |
Funding Information: | Funder Grant number Home Office No External Reference |
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 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124153 |