Robust Spatial-Temporal Incident Prediction

Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, Yevgeniy Vorobeychik
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:360-369, 2020.

Abstract

Spatio-temporal incident prediction is a central issue in law enforcement, with applications in fighting crimes like poaching, human trafficking, illegal fishing, burglaries and smuggling. However, state of the art approaches fail to account for evasion in response to predictive models, a common form of which is spatial shift in incident occurrence. We present a general approach for incident forecasting that is robust to spatial shifts. We propose two techniques for solving the resulting robust optimization problem: first, a constraint generation method guaranteed to yield an optimal solution, and second, a more scalable gradient-based approach. We then apply these techniques to both discrete-time and continuous-time robust incident forecasting. We evaluate our algorithms on two different real-world datasets, demonstrating that our approach is significantly more robust than conventional methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-mukhopadhyay20a, title = {Robust Spatial-Temporal Incident Prediction}, author = {Mukhopadhyay, Ayan and Wang, Kai and Perrault, Andrew and Kochenderfer, Mykel and Tambe, Milind and Vorobeychik, Yevgeniy}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {360--369}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/mukhopadhyay20a/mukhopadhyay20a.pdf}, url = {https://proceedings.mlr.press/v124/mukhopadhyay20a.html}, abstract = {Spatio-temporal incident prediction is a central issue in law enforcement, with applications in fighting crimes like poaching, human trafficking, illegal fishing, burglaries and smuggling. However, state of the art approaches fail to account for evasion in response to predictive models, a common form of which is spatial shift in incident occurrence. We present a general approach for incident forecasting that is robust to spatial shifts. We propose two techniques for solving the resulting robust optimization problem: first, a constraint generation method guaranteed to yield an optimal solution, and second, a more scalable gradient-based approach. We then apply these techniques to both discrete-time and continuous-time robust incident forecasting. We evaluate our algorithms on two different real-world datasets, demonstrating that our approach is significantly more robust than conventional methods. } }
Endnote
%0 Conference Paper %T Robust Spatial-Temporal Incident Prediction %A Ayan Mukhopadhyay %A Kai Wang %A Andrew Perrault %A Mykel Kochenderfer %A Milind Tambe %A Yevgeniy Vorobeychik %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-mukhopadhyay20a %I PMLR %P 360--369 %U https://proceedings.mlr.press/v124/mukhopadhyay20a.html %V 124 %X Spatio-temporal incident prediction is a central issue in law enforcement, with applications in fighting crimes like poaching, human trafficking, illegal fishing, burglaries and smuggling. However, state of the art approaches fail to account for evasion in response to predictive models, a common form of which is spatial shift in incident occurrence. We present a general approach for incident forecasting that is robust to spatial shifts. We propose two techniques for solving the resulting robust optimization problem: first, a constraint generation method guaranteed to yield an optimal solution, and second, a more scalable gradient-based approach. We then apply these techniques to both discrete-time and continuous-time robust incident forecasting. We evaluate our algorithms on two different real-world datasets, demonstrating that our approach is significantly more robust than conventional methods.
APA
Mukhopadhyay, A., Wang, K., Perrault, A., Kochenderfer, M., Tambe, M. & Vorobeychik, Y.. (2020). Robust Spatial-Temporal Incident Prediction. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:360-369 Available from https://proceedings.mlr.press/v124/mukhopadhyay20a.html.

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