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

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

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Authors/Creators:
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:
  • Published: 4 September 2017
  • Accepted: 15 May 2017
  • Published (online): 4 September 2017
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:
FunderGrant number
Home OfficeNo 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

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