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Studying crime trends in the USA over the years 2000–2012

Author

Listed:
  • Volodymyr Melnykov

    (University of Alabama)

  • Xuwen Zhu

    (University of Louisville)

Abstract

Studying crime trends and tendencies is an important problem that helps to identify socioeconomic patterns and relationships of crucial significance. Finite mixture models are famous for their flexibility in modeling heterogeneity in data. A novel approach designed for accounting for skewness in the distributions of matrix observations is proposed and applied to the United States crime data collected between 2000 and 2012 years. Then, the model is further extended by incorporating explanatory variables. A step-by-step model development demonstrates differences and improvements associated with every stage of the process. Results obtained by the final model are illustrated and thoroughly discussed. Multiple interesting conclusions have been drawn based on the developed model and obtained model-based clustering partition.

Suggested Citation

  • Volodymyr Melnykov & Xuwen Zhu, 2019. "Studying crime trends in the USA over the years 2000–2012," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 325-341, March.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:1:d:10.1007_s11634-018-0326-1
    DOI: 10.1007/s11634-018-0326-1
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    References listed on IDEAS

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    1. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    2. K. Harries, 1976. "A crime based analysis and classification of 729 American cities," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 2(4), pages 467-487, March.
    3. Brian J. Reich & Michael D. Porter, 2015. "Partially supervised spatiotemporal clustering for burglary crime series identification," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(2), pages 465-480, February.
    4. Semhar Michael & Volodymyr Melnykov, 2016. "Finite Mixture Modeling of Gaussian Regression Time Series with Application to Dendrochronology," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 412-441, October.
    5. Cabral, Celso Rômulo Barbosa & Lachos, Víctor Hugo & Prates, Marcos O., 2012. "Multivariate mixture modeling using skew-normal independent distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 126-142, January.
    6. Viroli, Cinzia, 2012. "On matrix-variate regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 296-309.
    7. Sharon Lee & Geoffrey McLachlan, 2013. "On mixtures of skew normal and skew $$t$$ -distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 241-266, September.
    8. Melnykov, Volodymyr, 2016. "Model-based biclustering of clickstream data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 31-45.
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    Cited by:

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