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Short-term forecasting of crime

Citations

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Cited by:

  1. Gorr, Wilpen & Harries, Richard, 2003. "Introduction to crime forecasting," International Journal of Forecasting, Elsevier, vol. 19(4), pages 551-555.
  2. Corcoran, Jonathan J. & Wilson, Ian D. & Ware, J. Andrew, 2003. "Predicting the geo-temporal variations of crime and disorder," International Journal of Forecasting, Elsevier, vol. 19(4), pages 623-634.
  3. David McDowall & Colin Loftin & Matthew Pate, 2012. "Seasonal Cycles in Crime, and Their Variability," Journal of Quantitative Criminology, Springer, vol. 28(3), pages 389-410, September.
  4. Huddleston, Samuel H. & Porter, John H. & Brown, Donald E., 2015. "Improving forecasts for noisy geographic time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1810-1818.
  5. Hyeon-Woo Kang & Hang-Bong Kang, 2017. "Prediction of crime occurrence from multi-modal data using deep learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
  6. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
  7. Daniel Ekwall & Björn Lantz, 2022. "Seasonality of incident types in transport crime – Analysis of TAPA statistics," Journal of Transportation Security, Springer, vol. 15(3), pages 193-222, December.
  8. Panagiotis Stalidis & Theodoros Semertzidis & Petros Daras, 2021. "Examining Deep Learning Architectures for Crime Classification and Prediction," Forecasting, MDPI, vol. 3(4), pages 1-22, October.
  9. Camacho-Collados, M. & Liberatore, F. & Angulo, J.M., 2015. "A multi-criteria Police Districting Problem for the efficient and effective design of patrol sector," European Journal of Operational Research, Elsevier, vol. 246(2), pages 674-684.
  10. Obubu Maxwell* & Ikediuwa Udoka Chinedu & Anabike Charles Ifeanyi & Nwokike Chukwudike C., 2019. "On Modeling Murder Crimes in Nigeria," Scientific Review, Academic Research Publishing Group, vol. 5(8), pages 157-162, 08-2019.
  11. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
  12. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
  13. Stephanie Glaser & Robert C. Jung & Karsten Schweikert, 2022. "Spatial panel count data: modeling and forecasting of urban crimes," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-29, December.
  14. Jean-François Richard, 2015. "Likelihood Based Inference and Prediction in Spatio-temporal Panel Count Models for Urban Crimes," Working Paper 5657, Department of Economics, University of Pittsburgh.
  15. Roman Liesenfeld & Jean‐François Richard & Jan Vogler, 2017. "Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 600-620, April.
  16. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
  17. Mohler, George & Carter, Jeremy & Raje, Rajeev, 2018. "Improving social harm indices with a modulated Hawkes process," International Journal of Forecasting, Elsevier, vol. 34(3), pages 431-439.
  18. Cohen, Jacqueline & Garman, Samuel & Gorr, Wilpen, 2009. "Empirical calibration of time series monitoring methods using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(3), pages 484-497, July.
  19. Temidayo James Aransiola & Marcelo Justus & Vania Ceccato, 2023. "Space-time dynamics of cargo theft: evidence from São Paulo, Brazil," Journal of Transportation Security, Springer, vol. 16(1), pages 1-28, December.
  20. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
  21. Daniel Ekwall & Björn Lantz, 2018. "The use of violence in cargo theft – a supply chain disruption case," Journal of Transportation Security, Springer, vol. 11(1), pages 3-21, June.
  22. Gorr, Wilpen L., 2009. "Forecast accuracy measures for exception reporting using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(1), pages 48-61.
  23. Chen, Huijing & Boylan, John E., 2008. "Empirical evidence on individual, group and shrinkage seasonal indices," International Journal of Forecasting, Elsevier, vol. 24(3), pages 525-534.
  24. Usman Ghani & Peter Toth & Fekete David, 2023. "Predictive Choropleth Maps Using ARIMA Time Series Forecasting for Crime Rates in Visegrád Group Countries," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
  25. Tao Hu & Xinyan Zhu & Lian Duan & Wei Guo, 2018. "Urban crime prediction based on spatio-temporal Bayesian model," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
  26. Grant Duwe & Nathan E. Sanders & Michael Rocque & James Alan Fox, 2022. "Forecasting the Severity of Mass Public Shootings in the United States," Journal of Quantitative Criminology, Springer, vol. 38(2), pages 385-423, June.
  27. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
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