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Crime against women in India: unveiling spatial patterns and temporal trends of dowry deaths in the districts of Uttar Pradesh

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  • G. Vicente
  • T. Goicoa
  • P. Fernandez‐Rasines
  • M. D. Ugarte

Abstract

Crimes against women in India have been continuously increasing lately as reported by the National Crime Records Bureau. Gender‐based violence has become a serious issue to such an extent that it has been catalogued as a high impact health problem by the World Health Organization. However, there is a lack of spatiotemporal analyses to reveal a complete picture of the geographical and temporal patterns of crimes against women. We focus on analysing how the geographical pattern of ‘dowry deaths’ changes over time in the districts of Uttar Pradesh during the period 2001–2014. The study of the geographical distribution of dowry death incidence and its evolution over time aims to identify specific regions that exhibit high risks and to hypothesize on potential risk factors. We also look into different spatial priors and their effects on final risk estimates. Various priors for the hyperparameters are also reviewed. The risk estimates seem to be robust in terms of the spatial prior and hyperprior choices and final results highlight several districts with extreme risks of dowry death incidence. Statistically significant associations are also found between dowry deaths, sex ratio and some forms of overall crime.

Suggested Citation

  • G. Vicente & T. Goicoa & P. Fernandez‐Rasines & M. D. Ugarte, 2020. "Crime against women in India: unveiling spatial patterns and temporal trends of dowry deaths in the districts of Uttar Pradesh," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 655-679, February.
  • Handle: RePEc:bla:jorssa:v:183:y:2020:i:2:p:655-679
    DOI: 10.1111/rssa.12545
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    1. Hodges, James S. & Reich, Brian J., 2010. "Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love," The American Statistician, American Statistical Association, vol. 64(4), pages 325-334.
    2. Jean Drèze & Reetika Khera, 2000. "Crime, Gender, and Society in India: Insights from Homicide Data," Population and Development Review, The Population Council, Inc., vol. 26(2), pages 335-352, June.
    3. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. Francis Bloch & Vijayendra Rao, 2002. "Terror as a Bargaining Instrument: A Case Study of Dowry Violence in Rural India," American Economic Review, American Economic Association, vol. 92(4), pages 1029-1043, September.
    7. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    8. C. B. Dean & M. D. Ugarte & A. F. Militino, 2001. "Detecting Interaction Between Random Region and Fixed Age Effects in Disease Mapping," Biometrics, The International Biometric Society, vol. 57(1), pages 197-202, March.
    9. Scott South & Katherine Trent & Sunita Bose, 2014. "Skewed Sex Ratios and Criminal Victimization in India," Demography, Springer;Population Association of America (PAA), vol. 51(3), pages 1019-1040, June.
    10. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    11. Brian J. Reich & James S. Hodges & Vesna Zadnik, 2006. "Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease-Mapping Models," Biometrics, The International Biometric Society, vol. 62(4), pages 1197-1206, December.
    12. Geetika Dang & Vani S. Kulkarni & Raghav Gaiha, 2018. "Why Dowry Deaths Have Risen in India?," ASARC Working Papers 2018-03, The Australian National University, Australia South Asia Research Centre.
    13. Ugarte, M.D. & Goicoa, T. & Militino, A.F., 2009. "Empirical Bayes and Fully Bayes procedures to detect high-risk areas in disease mapping," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2938-2949, June.
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