Abstract
Nowadays, with the expanding population and city size, urban crime rate control will be a very important direction for the integration of artificial intelligence and urban police governance, and the prediction of the number of regional hotspots is an effective crime prevention method. Based on the real dataset of a city, we hope to improve the prediction effect of crime hotspots and make analysis and prediction feasibility judgments from the perspective of data analysis. First, the dataset was pre-processed and filtered, and then analyzed from the spatial and temporal perspectives to further judge the feasibility of prediction. From a non-spatial perspective, the effect of adding covariates on the prediction of urban crime hotspots was explored. First, a map was drawn based on the distribution of hotspots, the map was divided into a grid, and the grid information was classified into four categories by clustering, and then selected covariates were added to the model for experiments. This study extends the prediction range in terms of temporal characteristics. The differential integrated moving average autoregressive model (ARIMA) is commonly used for time series forecasting, but it is more suitable for dealing with linear data. The long short-term memory neural network (LSTM) has a strong advantage in dealing with nonlinear data. We construct a combined ARIMA-LSTM model. It can fully exploit the data information and improve prediction accuracy. The results show that the combined ARIMA-LSTM model can predict the property crime in a district of the city better than the single ARIMA model and LSTM model, and the combined model can better fit the actual trend of the cases.
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References
ToppiReddy, H.K.R., Saini, B., Mahajan, G.: Crime prediction & monitoring framework based on spatial analysis. Procedia Comput. Sci. 132, 696–705 (2018)
Browning, C.R., Byron, R.A., Calder, C.A., et al.: Commercial density, residential concentration, and crime: land use patterns and violence in neighborhood context. J. Res. Crime Delinq.Delinq. 47(3), 329–357 (2015)
Mohler, G.O., Short, M.B., Malinowski, S., et al.: Randomized controlled field trials of predictive policing. J. Am. Stat. Assoc. 110(512), 00 (2015)
Priya, S.S., Gupta, L.: Predicting the future in time series using auto regressive linear regression modelling. In: Twelfth International Conference on Wireless and Optical Communications Networks, pp. 1–4 (2015)
Wang, Y., Ge, L., Li, S., Chang, F.: Deep temporal multi-graph convolutional network for crime prediction. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 525–538. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_39
Yi, F., Yu, Z., Zhuang, F., et al.: Neural network based continuous conditional random field for fine-grained crime prediction. In: IJCAI, pp. 4157–4163 (2019)
Dash, S.K., Safro, I., Srinivasamurthy, R.S.: Spatio-temporal prediction of crimes using network analytic approach. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1912–1917. IEEE (2018)
Lim, S., Kim, S.J., Park, Y.J., Kwon, N.: A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic. Expert Syst. Appl. 184 (2021)
Manojkumar, G., Suresh Kumar, G.: Numerical investigations on a geothermal reservoir using fully coupled thermo-hydro-geomechanics with integrated RSM-machine learning and ARIMA models. Geothermics 96 (2021)
Im, C.-K., Youn, S.-K.: The generation of 3D trimmed elements for NURBS-based isogeometric analysis. Int. J. Comput. Methods 15(7) (2018)
Desai Prathamesh, S.: News sentiment informed time-series analyzing AI (SITALA) to curb the spread of COVID-19 in Houston. Expert Syst. Appl. 180 (2021)
Lu, M., Xu, P., Chen, W., Yang, J., Zhao, X.: SRSF signal perception matrix based on autocorrelation function optimization method. J. Radio Sci. J. 4(4), 539–546 (2021). https://doi.org/10.13443/j.carolcarrolljors.2020040805
Hemachandran, K., Shubham, T., Preetha, M.G., Parveen, S., Utku, K.: Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. CRC Press, Hoboken, 01 Nov 2021
PérezSánchez, B., González, M., Perea, C., LópezEspÃn, J.J.: A new computational method for estimating simultaneous equations models using entropy as a parameter criteria. Mathematics 9(7) (2021)
Zhang, X.: Research on improved GM (1, 1) Load forecasting model based on numerical analysis. Taiyuan University of Technology (2012)
Boppuru, P.R., Ramesha, K.: Spatio-temporal crime analysis using KDE and ARIMA models in the indian context. Int. J. Digit. Crime Forensics (IJDCF) 12(4), 1–19 (2020)
Peng, Z., Dang, J., Unoki, M., Akagi, M.: Multi-resolution modulation-filtered cochleagram feature for LSTM-based dimensional emotion recognition from speech. Neural Netw. 140 (2021)
VÃctor, S.: Computing the expected Markov reward rates with stationarity detection and relative error control. Methodol. Comput. Appl. Probab. 19(2) (2017)
Ilham, U., Aina, M., Anny, K.S.: Optimization of ARIMA forecasting model using firefly algorithm. IJCCS (Indonesian J. Comput. Cybern. Syst. 13(2) (2019)
Jones, H.F.: Comment on Solvable model of bound states in the continuum (BIC) in on dimension. Physica Scripta 96(8) (2021)
Tibbs, J., et al.: KERA: analysis tool for multi-process, multi-state single-molecule data. Nucleic Acids Res. 49(9) (2021)
Inthiyaz, S., Muzammil, P.M., Siva Kumar, M., Sri Sai Srija, J., Tarun, S.M., Amruth, V.V.: Facial expression recognition using KERAS. J. Phys. Conf. Ser. 1804(1) (2021). Juan, M., Salvador, P.: An exact dynamic programming approach to segmented isotonic regression. Omega (2021, prepublish)
Arvind Kumar, T.: Deep Learning and Its Applications. Nova Science Publishers, Inc. (2021)
Kim, K.S., Choi, Y.S.: HyAdamC: a new Adam-based hybrid optimization algorithm for convolution neural networks. Sensors 21(12) (2021)
Yijun, W., Pengyu, Z., Wenya, Z.: An optimization strategy based on hybrid algorithm of Adam and SGD. In: Proceedings of 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018), pp. 630–633 (2018)
Geng, X., Xu, W., Yin, Y.: Research on database parameters tuning method based on embedded device. J. Phys. Conf. Ser. 1873(1) (2021)
Jing, X., Xu, J.: Improved protein model quality assessment by integrating sequential and pairwise features using deep learning. Bioinformatics (Oxford, England) (2020)
Laura, M.S., Tessa, L.J.: models to examine the validity of cluster-level factor structure using individual-level data. Adv. Methods Pract. Psychol. Sci. 2(3) (2019)
Gu, C., et al.: Transformer bushing temperature measurement model based on infrared temperature measurement. In: Proceedings of 2019 2nd International Conference on Intelligent Systems Research and Mechatronics Engineering (ISRME 2019), pp. 24–30. Francis Academic Press (2019)
Stavelin, A.K., Madhu, B., Balasubramanian, S., Sahana, C.: A review on the comparison of box Jenkins ARIMA and LSTM of deep learning. J. Trend Sci. Res. Dev. 5(3) (2021)
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Tong, Q., Zheng, J., Zhao, C. (2024). Hotspot Prediction Based on Temporal Characteristics. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Computer Science and Technology. ICCSE 2023. Communications in Computer and Information Science, vol 2023. Springer, Singapore. https://doi.org/10.1007/978-981-97-0730-0_31
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DOI: https://doi.org/10.1007/978-981-97-0730-0_31
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