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Crime patterns in Delhi: a Bayesian spatio-temporal assessment

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Abstract

The present research is aimed at crime modelling in the union territory of Delhi using Bayesian spatio-temporal methodology. Objective of conducting the present study is to identify crime risk and crime propensity for property crime incidents, based on the compounded data-prior belief approach, in the districts of the union territory of Delhi. Appropriate subjective priors are formulated using to represent both vague and available information from the geographically adjacent neighboring units. Suitability of intrinsic conditional autoregressive prior for investigating spatially structured random effects (\(s_{i}\)) and spatio-temporal interaction term (\(\delta_{i}\)) for crime counts is justified through caterpillar plots. Crime mapping using Bayesian tool kit helps in demarcation of hot and cold spots. Northern and Eastern regions of Delhi are found to have more crime propensity. Posterior predictive analysis reaffirms choice of Poisson model for district wise crime patterns. Areal literacy rate and proportion of non-workers are established as significant influencers in occurrence of crime. Superiority of Bayesian spatio-temporal methods over the classical methodologies as well as over the classical spatio-temporal studies on crime is asserted.

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Notes

  1. www.data.gov.in/24-05-2019.

  2. A person aged seven and above, who can both read and write with understanding in any language, is treated as literate. Literacy rate is calculated taking denominator as population of 7 years and above.

  3. Workers (female and male) include cultivators, agricultural labourers workers in household industry and other workers.

    Both definitions are taken from https://censusindia.gov.in/2011census/dchb/0700_PART_B_DCHB_NCT%20OF%20DELHI.pdf.

  4. www.data.gov.in/24-05-2019

  5. In the present study regressors are stochastic, hence the number of parameters would be multiplied by the total number of areas for independent random effects, for example there are three random effects in Model (4) (si, ui and \(\delta\)t ), so the total number of parameters contributed by these will be 3 \(\times\) 9.

  6. Purely Spatial and purely temporal model.

  7. Purely Spatial and purely temporal model with spatio-temporal interaction.

  8. Purely Spatial and purely temporal model with spatio-temporal interaction and literacy rate as a causal factor.

  9. Purely Spatial and purely temporal model with spatio-temporal interaction and percentage of non-workers as a causal factor.

  10. Purely Spatial and purely temporal model with spatio-temporal interaction term and both percentage of non-workers and literacy rate as causal factors.

  11. The above models are fitted by using OpenBUGS (A freeware from http://www.mrc-bsu.cam.ac. uk/bugs/winbugs/contents.html).

  12. Brooks and Gelman (1998). Width of the central 80% interval of the pooled runs is green, the average width of the 80% intervals within the individual runs is blue, and their ratio R (= pooled / within) is red, for plotting purpose the pooled and within interval widths are normalised to have an overall maximum of one.

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  1. Department of Statistics, Faculty of Mathematical Sciences, University of Delhi, Delhi, India

    Ranjita Pandey & Himanshu Tolani

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  1. Ranjita Pandey
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  2. Himanshu Tolani
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Pandey, R., Tolani, H. Crime patterns in Delhi: a Bayesian spatio-temporal assessment. Int J Syst Assur Eng Manag 13, 2971–2980 (2022). https://doi.org/10.1007/s13198-022-01768-1

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