Forecasting Crime Using ARIMA Model

Islam, Khawar; Raza, Akhter

doi:10.48550/arxiv.2003.08006

Cited by 4 publications

(4 citation statements)

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“…In addition to the methods mentioned above, there are many models and techniques that have been utilized in crime prediction research and have achieved positive prediction results, such as ARIMA [27], least absolute shrinkage and selection operator (LASSO) [28], and agent-based modeling (ABM) [29].…”

Section: Other Prediction Methodsmentioning

confidence: 99%

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Ding

2023

IJGI

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Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temporal crime prediction is divided into short-term prediction, medium-term prediction, and long-term prediction, and spatial crime prediction is divided into micro-, meso-, and macro-level prediction. Spatio-temporal crime prediction classification can be a permutation of temporal and spatial crime prediction classifications. A variety of crime prediction methods and evaluation metrics are also summarized, and different prediction methods and models are compared and evaluated. After sorting out the literature, it was found that there are still many limitations in the current research: (i) data sparsity is difficult to deal with effectively; (ii) the practicality, interpretability, and transparency of predictive models are insufficient; (iii) the evaluation system is relatively simple; and (iv) the research on decision-making application is lacking. In this regard, the following suggestions are proposed to solve the above problems: (i) the use of transformer learning technology to deal with sparse data; (ii) the introduction of model interpretation methods, such as Shapley additive explanations (SHAPs), to improve the interpretability of the models; (iii) the establishment of a set of standard evaluation systems for crime prediction at different scales to standardize data use and evaluation metrics; and (iv) the integration of reinforcement learning to achieve more accurate prediction while promoting the transformation of the application results.

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Section: Other Prediction Methodsmentioning

confidence: 99%

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Ding

2023

IJGI

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“…A decomposed crime time series consists of the original time series and the three decomposed parts with the estimated trend component, seasonal component, and remainder component. The calculation of trends and seasonality are used for the long-term prediction of crime events to facilitate strategic planning in police agencies [20,21]. Several studies, e.g., Malik et al [22] and Borges et al [23], use STL as an initial time series analysis for later modeling a predictive policing approach.…”

Section: Time Series Analysismentioning

confidence: 99%

Using Forecasting Methods on Crime Data: The SKALA Approach of the State Office for Criminal Investigation of North Rhine-Westphalia

Seidensticker¹,

Schwarz²

2022

The 8th International Conference on Time Series and Forecasting

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“…In response to the high incidence of crime in London, Khawar Islam et al utilized the Arima model for the prediction of the number of crimes in London. After training the model using five years of crime data, the model was then used to predict crime over two years and was found to achieve better results [8 ] . Mei et al used the LSTM model to validate and compare the predictability of commercial robbery, pedestrian robbery and residential robbery at different spatial and temporal scales [9] .…”

Section: Introductionmentioning

confidence: 99%

Prediction of excavation-type heritage crimes based on random forests

Ding

Lin

et al. 2023

International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022)

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In recent years, driven by the huge profits in the illegal cultural relics circulation market, smuggling and excavation of cultural relics have been repeated, and the situation of heritage crimes has become more and more serious. It is important to understand the occurrence pattern of excavation-type heritage crimes and construct a time-series prediction model of excavation-type heritage crimes to prevent them. This paper uses the random forests algorithm to construct a time-series prediction model of heritage crimes, which effectively solves the problem of poor timeliness of traditional prevention methods and is an attempt in the field of heritage crimes prediction. This paper constructs a time-series data of heritage crimes at several time scales and finds that the model has the best prediction effect when the time step is set to 30. It suggests that there may be a certain pattern of occurrence of excavation-type heritage crimes at the monthly scale. The findings of this paper are expected to provide decision support for the deployment of prevention and control resources for protected heritage units.

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Forecasting Crime Using ARIMA Model

Cited by 4 publications

References 0 publications

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Using Forecasting Methods on Crime Data: The SKALA Approach of the State Office for Criminal Investigation of North Rhine-Westphalia

Prediction of excavation-type heritage crimes based on random forests

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