z-logo
open-access-imgOpen Access
An Age-Structured Population Approach for the Mathematical Modeling of Urban Burglaries
Author(s) -
Joan Saldaña,
María Aguareles,
A. Avinyó,
Marta Pellicer,
Jordi Ripoll
Publication year - 2018
Publication title -
siam journal on applied dynamical systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.218
H-Index - 61
ISSN - 1536-0040
DOI - 10.1137/17m1142090
Subject(s) - commit , variable (mathematics) , structuring , population , computer science , nonlinear system , dynamics (music) , software deployment , econometrics , mathematical economics , statistical physics , mathematics , economics , sociology , demography , physics , mathematical analysis , pedagogy , finance , quantum mechanics , database , operating system
We propose a nonlinear model for the dynamics of urban burglaries which takes into account the deterring effect of the police. The model focuses on the timing of criminal activity rather than on the spatial spreading of burglaries and it is inspired in the age-dependent population dynamics. The structuring variables are the time elapsed between two consecutive offenses committed by a burglar or suffered by a house. The main ingredients of the model are the propensity of burglars to commit a crime and the rate at which houses are being burgled. These rates are taken as general as possible to allow different scenarios, including the widely used repeat victimization pattern. The dissuasive effect of the active police deployment is introduced by means of a memory term that depends on the number of the last committed burglaries. The asymptotic behavior of the model and the existence of a globally stable equilibrium are determined thanks to a suitable change of variables that involves a continuous rescaling of ...

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom