Abstract
This paper aims to analyze the data of different crime types in the London area for the past three years. The data is from data.police.uk provided by City of London Police. The R language and ARIMA model were applied to forecast different crime types in London. Seven sets of stable non-white sequences were obtained by preprocessing the original data, stationary test, and non-stationary sequence differential processing. The time series analysis method was used to realize the ARIMA model modelling. After comparison, the prediction was performed with the optimal ARIMA model. In terms of anti-social behaviour (i1), public order (i9), robbery (i10), shoplifting (i11), theft from the person (i12), the predicted results show a small fluctuation. And it shows a large fluctuation trend in bicycle theft (i2), but the overall number is lower than the previous months. Violence and sexual offences (i14) show a downtrend.
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Appendix
Appendix
1.1 A1. Autocorrelation Graph and Partial Autocorrelation Graph
(See Figs. 13, 14, 15, 16, 17, 18, 19, 20, 21 and 22).
Public order (i9) Autocorrelation graph
Public order (i9) Partial autocorrelation graph
Robbery (i10) Autocorrelation graph
Robbery (i10) Partial autocorrelation graph
Shoplifting (i11) Autocorrelation graph
Shoplifting (i11) Partial autocorrelation graph
Theft from the person (i12) Autocorrelation graph
Theft from the person (i12) Partial autocorrelation graph
Violence and sexual offences (i14) Autocorrelation graph
Violence and sexual offences (i14) Partial autocorrelation graph
1.2 A2. R Code
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Liu, D., Tang, J., Zhang, C. (2019). Application of Data Analysis in Trend Prediction of Different Crime Types in London. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_35
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DOI: https://doi.org/10.1007/978-981-15-0121-0_35
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