The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Self-Exciting Point Process Modeling of Crime
Abstract
Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space–time clustering is modeled in seismology by self-exciting point processes and the focus of this article is to show that these methods are well suited for criminological applications. We first review self-exciting point processes in the context of seismology. Next, using residential burglary data provided by the Los Angeles Police Department, we illustrate the implementation of self-exciting point process models in the context of urban crime. For this purpose we use a fully nonparametric estimation methodology to gain insight into the form of the space–time triggering function and temporal trends in the background rate of burglary.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,796
Multivariate Density Estimation: Theory, Practice, and Visualization
Scott, D. W. (1992) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316849 · Cited by 3,696
Space-Time Point-Process Models for Earthquake Occurrences
Ogata, Y. (1998) · Annals of the Institute of Statistical Mathematics · doi:10.1023/a:1003403601725 · Cited by 1,260
Stochastic Declustering of Space-Time Earthquake Occurrences
Zhuang, J., Ogata, Y., & Vere-Jones, D. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502760046925 · Cited by 753
The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime
Chainey, S., Tompson, L., & Uhlig, S. (2008) · Security Journal · doi:10.1057/palgrave.sj.8350066 · Cited by 629
Extending Earthquakes' Reach Through Cascading
Marsan, D., & Lengliné, O. (2008) · Science · doi:10.1126/science.1148783 · Cited by 389
How Do Residential Burglars Select Target Areas?
Bernasco, W., & Nieuwbeerta, P. (2005) · The British Journal of Criminology · doi:10.1093/bjc/azh070 · Cited by 346
Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization
Johnson, S. D., Bernasco, W., Bowers, K. J., Elffers, H., Ratcliffe, J., Rengert, G., & Townsley, M. (2007) · Journal of Quantitative Criminology · doi:10.1007/s10940-007-9025-3 · Cited by 334
Prospective Hot-Spotting: The Future of Crime Mapping?
Bowers, K. J. (2004) · British Journal of Criminology · doi:10.1093/bjc/azh036 · Cited by 315
Diffusion in Homicide: Exploring a General Method for Detecting Spatial Diffusion Processes
Cohen, J., & Tita, G. (1999) · Journal of Quantitative Criminology · doi:10.1023/a:1007596225550 · Cited by 267
Self-citation
Cited by
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567
“(Mohler et al., 2011), and the tailoring of general tools for”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
Zhao, Q., Erdogdu, M. A., He, H. Y., Rajaraman, A., & Leskovec, J. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783401 · Cited by 478
Randomized Controlled Field Trials of Predictive Policing
Mohler, G. O., Short, M. B., Malinowski, S., Johnson, M., Tita, G. E., Bertozzi, A. L., & Brantingham, P. J. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1077710 · Cited by 381
Self-citationAutomatic Crime Prediction Using Events Extracted from Twitter Posts
Wang, X., Gerber, M. S., & Brown, D. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-29047-3_28 · Cited by 335
Green, B., Horel, T., & Papachristos, A. V. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.8245 · Cited by 228
Metadata
- Authors
- Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., & Tita, G. E.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2011-03
- Volume
- 106
- Issue
- 493
- Pages
- 100-108
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2123906784
- Cited by
- 892
- References
- 14
- FWCI
- 57.25
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Self-Exciting Point Process Modeling of Crime. https://app.cochat.ai/papers/10.1198/jasa.2011.ap09546
- The paper
- Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., & Tita, G. E. (2011). Self-Exciting Point Process Modeling of Crime. Journal of the American Statistical Association. https://doi.org/10.1198/jasa.2011.ap09546
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
