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DuroNet: A Dual-robust Enhanced Spatial-temporal Learning Network for Urban Crime Prediction

Published:05 January 2021Publication History
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Abstract

Urban crime is an ongoing problem in metropolitan development and attracts general concern from the international community. As an effective means of defending urban safety, crime prediction plays a crucial role in patrol force allocation and public safety. However, urban crime data is a macro result of crime patterns overlapped by various irrelevant factors that cause inhomogeneous noises—local outliers and irregular waves. These noises might obstruct the learning process of crime prediction models and result in a deviation of performance. To tackle the problem, we propose a novel paradigm of <underline>Du</underline>al-<underline>ro</underline>bust Enhanced Spatial-temporal Learning <underline>Net</underline>work (DuroNet), an encoder-decoder architecture that possesses an adaptive robustness for reducing the effect of outliers and waves. The robustness is mainly reflected on two aspects. One is a locality enhanced module that employs local temporal context information to smooth the deviation of outliers and dynamic spatial information to assist in understanding normal points. The other is a self-attention-based pattern representation module to weaken the effect of irregular waves by learning attentive weights. Finally, extensive experiments are conducted on two real-world crime datasets before and after adding Gaussian noises. The results demonstrate the superior performance of our DuroNet over the state-of-the-art methods.

References

  1. David O. Afolabi, Sheng-Uei Guan, Ka Lok Man, and Prudence W. H. Wong. 2016. Meta-learning with empirical mode decomposition for noise elimination in time series forecasting. Lect. Notes Elect. Eng. 393 (2016), 405--413.Google ScholarGoogle ScholarCross RefCross Ref
  2. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR abs/1803.01271 (2018).Google ScholarGoogle Scholar
  3. Christoph Bergmeir and José Manuel Benítez. 2012. On the use of cross-validation for time series predictor evaluation. Inf. Sci. 191 (2012), 192--213.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Spencer Chainey and Jerry Ratcliffe. 2013. GIS and Crime Mapping. John Wiley 8 Sons.Google ScholarGoogle Scholar
  5. ByoungSeon Choi. 2012. ARMA Model Identification. Springer Science 8 Business Media.Google ScholarGoogle Scholar
  6. QuanXi Dong, YongZhe Lin, Jing Bi, and Haitao Yuan. 2019. An integrated deep neural network approach for large-scale water quality time series prediction. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC’19). IEEE, 3537--3542.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. H. Dovoedo and Subha Chakraborti. 2015. Boxplot-based outlier detection for the location-scale family. Commun. Stat. Simul. Comput. 44, 6 (2015), 1492--1513.Google ScholarGoogle ScholarCross RefCross Ref
  8. Bonnie S. Fisher et al. 2010. Encyclopedia of Victimology and Crime Prevention. Vol. 1. Sage.Google ScholarGoogle Scholar
  9. Matthew S. Gerber. 2014. Predicting crime using Twitter and kernel density estimation. Decis. Supp. Syst. 61 (2014), 115--125.Google ScholarGoogle ScholarCross RefCross Ref
  10. Felix A. Gers, Jürgen Schmidhuber, and Fred A. Cummins. 2000. Learning to forget: Continual prediction with LSTM. Neural Comput. 12, 10 (2000), 2451--2471.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Brian Gray. 2002. Introduction to linear regression analysis. Technomet. 44, 2 (2002), 191--192.Google ScholarGoogle ScholarCross RefCross Ref
  12. Chaolin Gu. 2019. Urbanization: Processes and driving forces. Sci. China Earth Sci. 62, 9 (2019), 1351--1360.Google ScholarGoogle ScholarCross RefCross Ref
  13. Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Nitesh V. Chawla, and Dawei Yin. 2019. MiST: A multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting. In Proceedings of the World Wide Web Conference (WWW’19). ACM, 717--728.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Chao Huang, Junbo Zhang, Yu Zheng, and Nitesh V. Chawla. 2018. DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18). ACM, 1423--1432.Google ScholarGoogle Scholar
  15. Ákos Jakobi and Andrea Podör. 2020. GIS-based statistical analysis of detecting fear of crime with digital sketch maps: A Hungarian multicity study. ISPRS Int. J. Geo-Inf. 9, 4 (2020), 229.Google ScholarGoogle ScholarCross RefCross Ref
  16. Dulakshi Santhusitha Kumari Karunasingha and Shie-Yui Liong. 2018. Enhancement of chaotic hydrological time series prediction with real-time noise reduction using Extended Kalman Filter. J. Hydrol. 565 (2018), 737--746.Google ScholarGoogle ScholarCross RefCross Ref
  17. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR’15).Google ScholarGoogle Scholar
  18. Ned Levine. 2006. Crime mapping and the Crimestat program. Geog. Anal. 38, 1 (2006), 41--56.Google ScholarGoogle ScholarCross RefCross Ref
  19. Longyuan Li, Junchi Yan, Xiaokang Yang, and Yaohui Jin. 2019. Learning interpretable deep state space model for probabilistic time series forecasting. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 2901--2908.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NIPS’19). 5244--5254.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Andy Liaw, Matthew Wiener, et al. 2002. Classification and regression by randomForest. R News 2, 3 (2002), 18--22.Google ScholarGoogle Scholar
  22. Jianquan Liu, Shoji Nishimura, and Takuya Araki. 2016. AntiLoiter: A loitering discovery system for longtime videos across multiple surveillance cameras. In Proceedings of the ACM Conference on Multimedia (MM’16). 675--679.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Jianquan Liu, Shoji Nishimura, Takuya Araki, and Yuichi Nakamura. 2017. A loitering discovery system using efficient similarity search based on similarity hierarchy. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A, 2 (2017), 367--375.Google ScholarGoogle Scholar
  24. Jianquan Liu, Duncan Yung, Shoji Nishimura, and Takuya Araki. 2019. Stalker retrieval on surveillance videos using spatio-temporal coappearance. In Proceedings of the 2nd IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’19). 127--134.Google ScholarGoogle ScholarCross RefCross Ref
  25. Christopher J. Lyons. 2007. Community (dis) organization and racially motivated crime. Amer. J. Sociology 113, 3 (2007), 815--863.Google ScholarGoogle ScholarCross RefCross Ref
  26. Wentao Ma, Jiandong Duan, Weishi Man, Haiquan Zhao, and Badong Chen. 2017. Robust kernel adaptive filters based on mean p-power error for noisy chaotic time series prediction. Eng. Appl. Artif. Intell. 58 (2017), 101--110.Google ScholarGoogle ScholarCross RefCross Ref
  27. George Mohler and Michael D. Porter. 2018. Rotational grid, PAI-maximizing crime forecasts. Stat. Anal. Data Min. 11, 5 (2018), 227--236.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML’10). Omnipress, 807--814.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Xiang Niu, Amr Elsisy, Noemi Derzsy, and Boleslaw K. Szymanski. 2019. Dynamics of crime activities in the network of city community areas. Appl. Netw. Sci. 4, 1 (2019), 127.Google ScholarGoogle ScholarCross RefCross Ref
  30. Lan Qin, Weide Li, and Shijia Li. 2019. Effective passenger flow forecasting using STL and ESN based on two improvement strategies. Neurocomputing 356 (2019), 244--256.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, and Hang Li. 2008. Global ranking using continuous conditional random fields. In Proceedings of the 22nd Conference on Neural Information Processing Systems (NIPS’08). Curran Associates, Inc., 1281--1288.Google ScholarGoogle Scholar
  32. Shakila Khan Rumi, Ke Deng, and Flora Dilys Salim. 2018. Crime event prediction with dynamic features. EPJ Data Sci. 7, 1 (2018), 43.Google ScholarGoogle ScholarCross RefCross Ref
  33. Tim Salimans and Diederik P. Kingma. 2016. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS’16). 901--909.Google ScholarGoogle Scholar
  34. David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski. 2019. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36 (2019), 1181--1191.Google ScholarGoogle ScholarCross RefCross Ref
  35. Sima Siami-Namini, Neda Tavakoli, and Akbar Siami Namin. 2019. The performance of LSTM and BiLSTM in forecasting time series. In Proceedings of the IEEE International Conference on Big Data (BigData’19). IEEE, 3285--3292.Google ScholarGoogle ScholarCross RefCross Ref
  36. Alexander J. Smola and Bernhard Schölkopf. 2004. A tutorial on support vector regression. Stat. Comput. 14, 3 (2004), 199--222.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (2014), 1929--1958.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the 28th Conference on Neural Information Processing Systems (NIPS’14). 3104--3112.Google ScholarGoogle Scholar
  39. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS’17). 5998--6008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Minh Thanh Vo, Rohit Sharma, Raghvendra Kumar, Le Hoang Son, Binh Thai Pham, Dieu Tien Bui, Ishaani Priyadarshini, Manash Sarkar, and Tuong Le. 2020. Crime rate detection using social media of different crime locations and Twitter part-of-speech tagger with Brown clustering. J. Intell. Fuzzy Syst. 38, 4 (2020), 4287--4299.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Hongjian Wang, Huaxiu Yao, Daniel Kifer, Corina Graif, and Zhenhui Li. 2019. Non-stationary model for crime rate inference using modern urban data. IEEE Trans. Big Data 5, 2 (2019), 180--194.Google ScholarGoogle ScholarCross RefCross Ref
  42. Xiaozhe Wang, Kate A. Smith, and Rob J. Hyndman. 2006. Characteristic-based clustering for time series data. Data Min. Knowl. Discov. 13, 3 (2006), 335--364.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Pin Wu, Zhidan Lei, Quan Zhou, Rukang Zhu, Xuting Chang, Junwu Sun, Wenjie Zhang, and Yike Guo. 2020. Multiple premises entailment recognition based on attention and gate mechanism. Expert Syst. Appl. 147 (2020), 113214--113221.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Fei Yi, Zhiwen Yu, Fuzhen Zhuang, and Bin Guo. 2019. Neural network based continuous conditional random field for fine-grained crime prediction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 4157--4163.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Fei Yi, Zhiwen Yu, Fuzhen Zhuang, Xiao Zhang, and Hui Xiong. 2018. An integrated model for crime prediction using temporal and spatial factors. In Proceedings of the IEEE International Conference on Data Mining (ICDM’18). IEEE Computer Society, 1386--1391.Google ScholarGoogle ScholarCross RefCross Ref
  46. Xiangyu Zhao and Jiliang Tang. 2017. Modeling temporal-spatial correlations for crime prediction. In Proceedings of the ACM on Conference on Information and Knowledge Management (CIKM’17). ACM, 497--506.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. 2016. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th Meeting of the Association for Computational Linguistics (ACL’16). The Association for Computer Linguistics, 207--212.Google ScholarGoogle ScholarCross RefCross Ref

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          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 21, Issue 1
            Visions Paper, Regular Papers, SI: Blockchain in E-Commerce, and SI: Human-Centered Security, Privacy, and Trust in the Internet of Things
            February 2021
            534 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3441681
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

            Copyright © 2021 ACM

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 5 January 2021
            • Accepted: 1 October 2020
            • Revised: 1 September 2020
            • Received: 1 June 2020
            Published in toit Volume 21, Issue 1

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