Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756374
Title: Mathematical modelling of social systems
Author: Prieto Curiel, Rafael
ISNI:       0000 0004 7429 3272
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Mobility and migration patterns, the concentration of crime and opinion dynamics observed on the fear of crime are all examples of social systems in which complex patterns emerge that subsequently feed back into the overall system. This thesis describes new methods established to analyse such patterns which appear in social systems. The main application area is in the field of crime science, but the methods developed here have wider applications within other social systems, some of which are also explored in the thesis, such as migration or road accidents. Based on new assessments of data, by utilising novel techniques of analysis and visualisation, models are also developed to determine how the perception of security is affected by particular crimes. The new metrics and models developed here consider different types of situation. Firstly, for events which have low frequency and yet a high degree of concentration; secondly, the distribution of such events which allows them to be simulated under different conditions; and then finally, understanding the impact of different degrees of concentration. An individual's fear of crime is the result of a mixture of factors which go beyond merely the actual crime experienced by that person, such as fear shared by others, memory of past events and of previous perceptions, crime reported in the media and more. This thesis quantifies fear of crime in a way that may prove useful to identify factors which increase fear of crime besides crime itself, explain why fear of crime emerges in a population and suggests policies for controlling fear.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.756374  DOI: Not available
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