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Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County

Received: 8 October 2019     Accepted: 29 October 2019     Published: 4 November 2019
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Abstract

Nairobi is a county in Kenya that is more prone to crime occurrence. This has made many researchers, for the past years, to study about crime occurrence in its suburbs and which factors promote crime. The theories around crime are always coupled with an attempt to predict their occurrence, for better crime analysis, and management, in case they happen, the associated covariates and their changes are analyzed. At the sub-county level, the crime occurrence is highly studied and understood. In this study, using Bayesian theory, this study builds spatial-temporal Bayesian model approach to crime to analyze its spatial-temporal patterns and determine any developing trends using data regarding robberies that occurred in Nairobi County in Kenya from January 1, 2011 to December 31, 2018. Of the diverse socio-economic variables associated with crime rate, including unemployment rate, poverty, weak law enforcement, Alcohol and drug abuse, and illiteracy, this study finds that robbery crime rate is significantly correlated with the poverty index and the unemployment rate. This finding provides a statistical reference for County safety protection. For further work, we recommend that further study can be done to determine factors associated with the dynamics and the distribution of crime in Nairobi County while accounting for measurement error that might be present in the covariates.

Published in International Journal of Data Science and Analysis (Volume 5, Issue 6)
DOI 10.11648/j.ijdsa.20190506.11
Page(s) 111-116
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Crime, Integrated Nested Laplace Approximation (INLA), Bayesian, Spatial-temporal Modeling, Hotspot, Crime Mapping

References
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  • APA Style

    George Ngogoyo Chege, Samuel Musili Mwalili, Anthony Wanjoya. (2019). Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County. International Journal of Data Science and Analysis, 5(6), 111-116. https://doi.org/10.11648/j.ijdsa.20190506.11

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    ACS Style

    George Ngogoyo Chege; Samuel Musili Mwalili; Anthony Wanjoya. Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County. Int. J. Data Sci. Anal. 2019, 5(6), 111-116. doi: 10.11648/j.ijdsa.20190506.11

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    AMA Style

    George Ngogoyo Chege, Samuel Musili Mwalili, Anthony Wanjoya. Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County. Int J Data Sci Anal. 2019;5(6):111-116. doi: 10.11648/j.ijdsa.20190506.11

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  • @article{10.11648/j.ijdsa.20190506.11,
      author = {George Ngogoyo Chege and Samuel Musili Mwalili and Anthony Wanjoya},
      title = {Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County},
      journal = {International Journal of Data Science and Analysis},
      volume = {5},
      number = {6},
      pages = {111-116},
      doi = {10.11648/j.ijdsa.20190506.11},
      url = {https://doi.org/10.11648/j.ijdsa.20190506.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20190506.11},
      abstract = {Nairobi is a county in Kenya that is more prone to crime occurrence. This has made many researchers, for the past years, to study about crime occurrence in its suburbs and which factors promote crime. The theories around crime are always coupled with an attempt to predict their occurrence, for better crime analysis, and management, in case they happen, the associated covariates and their changes are analyzed. At the sub-county level, the crime occurrence is highly studied and understood. In this study, using Bayesian theory, this study builds spatial-temporal Bayesian model approach to crime to analyze its spatial-temporal patterns and determine any developing trends using data regarding robberies that occurred in Nairobi County in Kenya from January 1, 2011 to December 31, 2018. Of the diverse socio-economic variables associated with crime rate, including unemployment rate, poverty, weak law enforcement, Alcohol and drug abuse, and illiteracy, this study finds that robbery crime rate is significantly correlated with the poverty index and the unemployment rate. This finding provides a statistical reference for County safety protection. For further work, we recommend that further study can be done to determine factors associated with the dynamics and the distribution of crime in Nairobi County while accounting for measurement error that might be present in the covariates.},
     year = {2019}
    }
    

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    AU  - George Ngogoyo Chege
    AU  - Samuel Musili Mwalili
    AU  - Anthony Wanjoya
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    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijdsa.20190506.11
    AB  - Nairobi is a county in Kenya that is more prone to crime occurrence. This has made many researchers, for the past years, to study about crime occurrence in its suburbs and which factors promote crime. The theories around crime are always coupled with an attempt to predict their occurrence, for better crime analysis, and management, in case they happen, the associated covariates and their changes are analyzed. At the sub-county level, the crime occurrence is highly studied and understood. In this study, using Bayesian theory, this study builds spatial-temporal Bayesian model approach to crime to analyze its spatial-temporal patterns and determine any developing trends using data regarding robberies that occurred in Nairobi County in Kenya from January 1, 2011 to December 31, 2018. Of the diverse socio-economic variables associated with crime rate, including unemployment rate, poverty, weak law enforcement, Alcohol and drug abuse, and illiteracy, this study finds that robbery crime rate is significantly correlated with the poverty index and the unemployment rate. This finding provides a statistical reference for County safety protection. For further work, we recommend that further study can be done to determine factors associated with the dynamics and the distribution of crime in Nairobi County while accounting for measurement error that might be present in the covariates.
    VL  - 5
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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (Jkuat), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (Jkuat), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (Jkuat), Nairobi, Kenya

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