Examining the impact of cross-domain learning on crime prediction

Bappee, Fateha Khanam; Soares, Amílcar; Petry, Lucas May; Matwin, Stan

doi:10.1186/s40537-021-00489-9

Cited by 12 publications

(4 citation statements)

References 54 publications

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“…(3) Method Based on Some Other Neural Network Transfer learning [72] is a powerful learning method to use the related learning strategy by the different data to predict or forecast the collected data. The single and multi-domain representations are used to evaluate the performance of the classification.…”

Section: ) Methods Based On Convolution Networkmentioning

confidence: 99%

Crime Prediction Methods Based on Machine Learning: A Survey

Yin¹

2023

Computers, Materials &Amp; Continua

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The objective of crime prediction, one of the most important technologies in social computing, is to extract useful information from many existing criminal records to predict the next process-related crime. It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas. With the rapid growth of big data, the Internet of Things, and other technologies, as well as the increasing use of artificial intelligence in forecasting models, crime prediction models based on deep learning techniques are accelerating. Therefore, it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms. Existing crime prediction methods can be roughly divided into two categories: those based on conventional machine learning and those based on contemporary deep learning. This survey analyses the fundamental theories and procedures. The most frequently used data sets are then enumerated, and the fundamental procedures of various algorithms are also analyzed in this paper. In light of the insufficient scale of existing data in this field, the ambiguity of data types used to predict crimes, and the absence of public data sets that have a significant impact on the research of algorithm models, this survey proposes the construction of a machine learning-based big data research model to address these issues. Future researchers who will enter this field are provided with a guide to the direction of future research development.

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Section: ) Methods Based On Convolution Networkmentioning

confidence: 99%

Crime Prediction Methods Based on Machine Learning: A Survey

Yin¹

2023

Computers, Materials &Amp; Continua

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“…The study of transfer learning is inspired by the idea that humans can logically use previously acquired knowledge to solve new problems quickly and accurately. Bappee et al [ 46 ] explored transfer learning to predict crime in neighbouring city boroughs. Crime data from New York City from 2012 to 2013 was collected to evaluate the theoretical framework presented in this paper.…”

Section: Related Workmentioning

confidence: 99%

Leveraging transfer learning with deep learning for crime prediction

Butt,

Letchmunan,

Hassan

et al. 2024

PLoS ONE

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Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

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“…The literature designed an instance-based transformer learning setup to address the problem of sparse data in small cities from a cross-domain perspective [68]. They incorporated 19 features from six different scenarios based on seasonal perspectives, such as crime, population, socio-economics, geographic location, and street lighting; these were used to train a GB classifier model to transform knowledge from different domains in Toronto and Vancouver (source domain) to Halifax (target domain).…”

Section: ) Ensemble Modelmentioning

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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Examining the impact of cross-domain learning on crime prediction

Cited by 12 publications

References 54 publications

Crime Prediction Methods Based on Machine Learning: A Survey

Crime Prediction Methods Based on Machine Learning: A Survey

Leveraging transfer learning with deep learning for crime prediction

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

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