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Short-term forecasting of crime

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  • Gorr, Wilpen
  • Olligschlaeger, Andreas
  • Thompson, Yvonne

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  • Gorr, Wilpen & Olligschlaeger, Andreas & Thompson, Yvonne, 2003. "Short-term forecasting of crime," International Journal of Forecasting, Elsevier, vol. 19(4), pages 579-594.
    • Handle: RePEc:eee:intfor:v:19:y:2003:i:4:p:579-594
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    References listed on IDEAS

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    1. Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October.
    2. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552.
    3. George Duncan & Wilpen Gorr & Janusz Szczypula, 1993. "Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting," Management Science, INFORMS, vol. 39(3), pages 275-293, March.
    4. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    5. A. Hirschfield & K.J. Bowers, 1997. "The Effect of Social Cohesion on Levels of Recorded Crime in Disadvantaged Areas," Urban Studies, Urban Studies Journal Limited, vol. 34(8), pages 1275-1295, July.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Gorr, Wilpen & Harries, Richard, 2003. "Introduction to crime forecasting," International Journal of Forecasting, Elsevier, vol. 19(4), pages 551-555.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.

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