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Last night in Sweden?: Using Gaussian processes to study changing demographics at the level of municipalities
Södertörn Univ, Sch Social Sci, Huddinge, Sweden; Stockholm School of Economics, Mistra Ctr Sustainable Markets, Stockholm, Sweden.ORCID iD: 0000-0003-0573-5287
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.ORCID iD: 0000-0002-1436-9103
2020 (English)In: European Journal of Crime, Criminal Law and Criminal Justice, ISSN 0928-9569, E-ISSN 1571-8174, Vol. 28, no 1, p. 46-75Article in journal (Refereed) Published
Abstract [en]

The increased immigration in Western Europe has been linked by some political parties to increased criminality rates. We study the statistical relationship between the proportion of foreign-born to three types of reported criminality - rapes, burglary, and assault. The analysis is based on Swedish municipality level data for 2002-2014, years with significant immigration. Using non-parametric Gaussian processes models, we find that while reported rape rates have increased, they are likely best explained by changes in reporting. The reported burglary rates have decreased, while reported assault rates are positively correlated to the proportion of foreign-born residents in the municipality.

Place, publisher, year, edition, pages
2020. Vol. 28, no 1, p. 46-75
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-364654DOI: 10.1163/15718174-02801003ISI: 000519778600004OAI: oai:DiVA.org:uu-364654DiVA, id: diva2:1259740
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2022-01-29Bibliographically approved
In thesis
1. Gaussian process models of social change
Open this publication in new window or tab >>Gaussian process models of social change
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Social systems produce complex and nonlinear relationships in the indicator variables that describe them. Traditional statistical regression techniques are commonly used in the social sciences to study such systems. These techniques, such as standard linear regression, can prevent the discovery of the complex underlying mechanisms and rely too much on the expertise and prior beliefs of the data analyst. In this thesis, we present two methodologies that are designed to allow the data to inform us about these complex relations and provide us with interpretable models of the dynamics.

The first methodology is a Bayesian approach to analysing the relationship between indicator variables by finding the parametric functions that best describe their interactions. The parametric functions with the highest model evidence are found by fitting a large number of potential models to the data using Bayesian linear regression and comparing their respective model evidence. The methodology is computationally fast due to the use of conjugate priors, and this allows for inference on large sets of models. The second methodology is based on a Gaussian processes framework and is designed to overcome the limitations of the first modelling approach. This approach balances the interpretability of more traditional parametric statistical methods with the predictability and flexibility of non-parametric Gaussian processes.

This thesis contains four papers where we apply the methodologies to both real-life problems in the social sciences as well as on synthetic data sets. In paper I, the first methodology (Bayesian linear regression) is applied to the classic problem of how democracy and economic development interact. In paper II and IV, we apply the second methodology (Gaussian processes) to study changes in the political landscape and demographic shifts in Sweden in the last decades. In paper III, we apply the second methodology on a synthetic data set to perform parameter estimation on complex dynamical systems.

Place, publisher, year, edition, pages
Uppsala: Department of Mathematics, 2018. p. 51
Series
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 111
Keywords
Gaussian processes, Bayesian statistics, Dynamical systems, Social sciences
National Category
Mathematics
Research subject
Applied Mathematics and Statistics
Identifiers
urn:nbn:se:uu:diva-364656 (URN)978-91-506-2734-3 (ISBN)
Public defence
2018-12-21, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2018-11-30 Created: 2018-10-30 Last updated: 2018-11-30

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Bali Swain, RanjulaBlomqvist, Björn Rune HelmerSumpter, David J. T.
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