Spatiotemporal Analysis of Web News Archives for Crime Prediction

Umair, Areeba; Sarfraz, Muhammad Shahzad; Habib, Usman; Ullah, Muhammad Habib; Mazzara, Manuel

doi:10.3390/app10228220

Cited by 19 publications

(16 citation statements)

References 36 publications

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“…The Linear regression and the long short term memory were combined to make the prison term prediction and the natural language processing techniques, such as the word embedding, were also used to improve the performance. Umair et al [79] transformed the spatio-temporal analysis into natural language processing scheme. The dataset used in this methods was the data crawled from the newspaper, such as the Dawn News, Dunya News, Ary News, The News, Daily Parkistan, Panistan Press Foundation, The Nation, and Journalism Pakistan by the Data Miner Tools.…”

Section: The Models Based On Different Media Typementioning

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: The Models Based On Different Media Typementioning

confidence: 99%

Crime Prediction Methods Based on Machine Learning: A Survey

Yin¹

2023

Computers, Materials &Amp; Continua

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“…The increasing amount of media information as newspapers and social media entries demand new ways of data processing, in a form of automation and specific machine learning algorithms that can be useful for criminology and crime prevention, constituting a new research frontier in this area (e.g., Umair et al, 2020).…”

Section: Caap and Media Archivesmentioning

confidence: 99%

“…Research is currently exploring new methods to work, for instance, with web news to handle a high volume of data streams (Za'in et al, 2017) to be able to make such data useful. As suggested by Umair et al (2020), there is a clear potential on this vast amount of information coming from archives to detect and predict crime locations but also predict patterns and transform them into useful information in crime prevention.…”

Section: Potentialitiesmentioning

confidence: 99%

Crimes Against Animal Production: Exploring the use of Media Archives

Ceccato

Abraham

Lundqvist

2021

International Criminal Justice Review

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Swedish media have revealed an increase in crimes against animal production (CAAP) in the last decade. We investigate the nature of such crimes (especially against mink, pig, and rabbit farms), with a focus on those crimes whose suspects are animal rights groups by utilizing data from media archives from 2009 to 2019. Newspaper articles show that while vandalism and trespassing are often committed against mink farms, property crimes occur more often against pig and rabbit farms. Because there are indications that crime suspects are not a homogeneous group and express different motivations to commit CAAP, a multipronged approach is needed to prevent CAAP. The use of newspaper articles from media archives has proven to be successful for obtaining a general perspective of CAAP, but it is limited for capturing crime incidence or for investigating CAAP spatiotemporal nature when using geographical information systems. The automated usage of digital media archives should be further explored and critically assessed in future research in criminology.

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“…964973. Research (Umair et al, 2020) uses data on crime in Pakistan, resulting in the conclusion that the accuracy of k-NN can reach a maximum of 92%, while the Random Forest algorithm can only reach 62%. The results showed that the higher the value of K, the better the k-NN algorithm, where for K values of 3, 5, 7, and 9 the k-NN algorithm has an accuracy rate of 89%, 93%, 93%, and 94% .…”

Section: Introductionmentioning

confidence: 99%

Crime of theft prediction using Machine Learning K-Nearest Neighbour Algorithm at Polresta Bandar Lampung

Hermawan

Prianggono

2023

SinkrOn

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The era of the industrial revolution 4.0 is a time where cyber and physical technology collaborate. This study aims to predict the types of theft crimes that occur in the Bandar Lampung Police area with the K-Nearest Neighbor algorithm, evaluate the prediction results and profiling the prediction results carried out by Bandar Lampung Police investigators in efforts to prevent and handle criminal acts of theft in the jurisdiction of the Bandar Lampung Police Lampung. The approach was carried out using the quantitative method of the K-Nearest Neighbor algorithm using the Rapidminer application by utilizing 1671 police report data from the Bandar Lampung Police and a questionnaire survey method conducted on 49 police investigators from the Bandar Lampung Police. Data collection techniques are carried out in a valid and reliable manner as a support for predictive validity. Based on the results of the classification and questionnaire, it was found that the majority of victims of the crime of theft were adult men who did not have a job and lived in urban areas. It was found that the majority of thefts occurred in parking lots in urban areas on Monday morning where the perpetrators used tools and targeted moving objects by tampering with locks which caused losses of around 10-50 million rupiah. This type of theft is theft by weighting (CURAT) which applies to Article 363 of the Criminal Code. The prediction results show that the neighboring value (K) and the distribution ratio of training and testing data are K=3 and 7:3, respectively. Predictions using K values and data sharing ratios show a high level of accuracy, namely 99. 20%. The results of the questionnaire show results that are in line with the results of the classification with an accuracy rate of the actual data of 75. 7122%. So by increasing the understanding skills of Bandar Lampung Police investigators using technology to predict the crime of theft, the number of theft crimes can be reduced.

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Spatiotemporal Analysis of Web News Archives for Crime Prediction

Cited by 19 publications

References 36 publications

Crime Prediction Methods Based on Machine Learning: A Survey

Crime Prediction Methods Based on Machine Learning: A Survey

Crimes Against Animal Production: Exploring the use of Media Archives

Crime of theft prediction using Machine Learning K-Nearest Neighbour Algorithm at Polresta Bandar Lampung

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