IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v182y2019i3p919-942.html
   My bibliography  Save this article

A semiparametric spatiotemporal Hawkes‐type point process model with periodic background for crime data

Author

Listed:
  • Jiancang Zhuang
  • Jorge Mateu

Abstract

Past studies have shown that crime events are often clustered. This study proposes a spatiotemporal Hawkes‐type point process model, which includes a background component with daily and weekly periodization, and a clustering component that is triggered by previous events. We generalize the non‐parametric stochastic reconstruction method so that we can estimate each component in the background rate and the triggering response that appears in the model conditional intensity: the background rate includes a daily and a weekly periodicity, a separable spatial component and a long‐term background trend. Two relaxation coefficients are introduced to stabilize and secure the estimation process. This model is used to describe the occurrences of violence or robbery cases in Castellón, Spain, during 2 years. The results show that robbery crime is highly influenced by daily life rhythms, revealed by its daily and weekly periodicity, and that about 3% of such crimes can be explained by clustering. Further diagnostic analysis shows that the model could be improved by considering the following ingredients: the daily occurrence patterns are different between weekends and working days; in the city centre, robbery activity shows different temporal patterns, in both weekly periodicity and long‐term trend, from other suburb areas.

Suggested Citation

  • Jiancang Zhuang & Jorge Mateu, 2019. "A semiparametric spatiotemporal Hawkes‐type point process model with periodic background for crime data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 919-942, June.
  • Handle: RePEc:bla:jorssa:v:182:y:2019:i:3:p:919-942
    DOI: 10.1111/rssa.12429
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12429
    Download Restriction: no

    File URL: https://libkey.io/10.1111/rssa.12429?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chenlong Li & Kaiyan Cui, 2024. "Multivariate Hawkes processes with spatial covariates for spatiotemporal event data analysis," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(4), pages 535-578, August.
    2. Kieran Kalair & Colm Connaughton & Pierfrancesco Alaimo Di Loro, 2021. "A non‐parametric Hawkes process model of primary and secondary accidents on a UK smart motorway," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 80-97, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:182:y:2019:i:3:p:919-942. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.