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Abstract
Self-exciting point processes have been proposed as models for the location of criminal events in space and time. Here we consider the case where the triggering function is isotropic and takes a non-parametric form that is determined from data. We pay special attention to normalisation issues and to the choice of spatial distance measure, thereby extending the current methodology. After validating these ideas on synthetic data, we perform inference and prediction tests on public domain burglary data from Chicago. We show that the algorithmic advances that we propose lead to improved predictive accuracy.
Original language | English |
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Number of pages | 23 |
Journal | European Journal of Applied Mathematics |
Early online date | 8 Apr 2021 |
DOIs | |
Publication status | E-pub ahead of print - 8 Apr 2021 |
Keywords
- Hawkes process
- criminology
- non-parametric
- hotspots
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Dive into the research topics of 'Modelling burglary in Chicago using a self-exciting point process with isotropic triggering'. Together they form a unique fingerprint.Projects
- 1 Finished
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Doctoral Training Partnership (DTA - University of Strathclyde) | Gilmour, Craig
Higham, D. (Principal Investigator), Ramage, A. (Co-investigator) & Gilmour, C. (Research Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/12/14 → 28/08/19
Project: Research Studentship - Internally Allocated