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Journal articles on the topic 'Crime forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 February 2023

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1

Tariq, Haseeb, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Sabeen Bari, Muhammad Shahzad Sarfraz, and Rozita Jamili Oskouei. "Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem." Security and Communication Networks 2021 (March 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/5587511.

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Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.
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2

Khairuddin, Alif Ridzuan, Razana Alwee, and Habibollah Harun. "Comparative Study on Artificial Intelligence Techniques in Crime Forecasting." Applied Mechanics and Materials 892 (June 2019): 94–100. http://dx.doi.org/10.4028/www.scientific.net/amm.892.94.

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An application of efficient crime analysis is beneficial and helpful to understand the behavior of trend and pattern of crimes. Crime forecasting is an area of research that assists authorities in enforcing early crime prevention measures. Statistical technique has been widely applied in the past to develop crime forecasting models. However, it has been observed that researchers have begun to shift their research interests from statistical model to artificial intelligence model in crime forecasting. Thus, this study is conducted to observe the capabilities of artificial intelligence technique in improving crime forecasting. The main objective of this study is to conduct a comparative analysis on forecasting performance capabilities of four artificial intelligence techniques, namely, artificial neural network (ANN), support vector regression (SVR), random forest (RF), and gradient tree boosting (GTB) in forecasting crime rate. Forecasting capability of each technique was assessed in terms of measurement of errors. From the result obtained, GTB showed the highest performance capability where it scored the lowest measurement of errors compared to SVR, RF, and ANN.
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3

Stupar, Davor. "Criminal intelligence as a prerequisite for quality crime forecasting." Zurnal za bezbjednost i kriminalistiku 3, no. 2 (2021): 57–74. http://dx.doi.org/10.5937/zurbezkrim2101057s.

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Crime forecasting in studying new and adapting one's own methods, and as a part of criminalistic strategy in combating crime as a preliminary stage, must gain a wider knowledge of the same. For any crime forecasting, it is necessary to have crime data, which are systematically processed in their form and structure and stored in records and databases, while data collection is an essential part on which criminal intelligence activity is based. Criminal intelligence activity precedes criminal intelligence analysis, which can be defined as a system in the process of collecting, processing and presenting data to achieve police goals and thus quality crime forecasting. Basically, this task can be described as data collection and storage through criminal intelligence activity, which are then analytically processed in order to shed light on crimes in the tactical sense, and crime forecasting in the strategic sense. This paper addresses the role of data collection through criminal intelligence and criminal intelligence system as a prerequisite for quality crime forecasting.
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Mohamad Zamri, Nurul Farhana, Nooritawati Md Tahir, Megat Syahirul Amin Megat Ali1, Nur Dalila Khirul Ashar, and Ali Abd Al-misreb. "Mini-review of Street Crime Prediction and Classification Methods." Jurnal Kejuruteraan 33, no. 3 (August 30, 2021): 391–401. http://dx.doi.org/10.17576/jkukm-2021-33(3)-02.

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Crime rates are one of the biggest problems in today’s modern society, especially in urban cities. Various techniques on crime prediction and detection have been developed by previous researchers in reducing the crime rates that keep increasing throughout the year as well as to assist the government authorities in combating crimes. These include studies on forecasting crime activities based on both primary and secondary data that include numerical data, statistics, video, and images related to various categories of crimes. Thus, in this study, a mini-review is conducted related to the database used as well as methods that have been developed by previous researches related to crime classification, crime analysis and forecasting of crime or crime prediction. Further, a new technique will be proposed in the detection of crime activities. The proposed technique involves evaluation and validation of several Deep Learning (DL) specifically the Convolutional Neural Network (CNN) along with the type of database to be used specifically for street crime detection that focuses on snatch theft.
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Gorr, Wilpen, and Richard Harries. "Introduction to crime forecasting." International Journal of Forecasting 19, no. 4 (October 2003): 551–55. http://dx.doi.org/10.1016/s0169-2070(03)00089-x.

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6

Lee, YongJei, and SooHyun O. "Flag and boost theories for hot spot forecasting: An application of NIJ’s Real-Time Crime forecasting algorithm using Colorado Springs crime data." International Journal of Police Science & Management 22, no. 1 (August 12, 2019): 4–15. http://dx.doi.org/10.1177/1461355719864367.

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By operationalizing two theoretical frameworks, we forecast crime hot spots in Colorado Springs. First, we use a population heterogeneity (flag) framework to find places where the hot spot forecasting is consistently successful over months. Second, we use a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm is implemented in Microsoft Excel®, making it simple to apply and completely transparent. Results shows high accuracy and high efficiency in hot spot forecasting, even if the data set and the type of crime we used in this study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non-serious crime for forecasting are different from each other. We also find that the spatial patterns of forecasted hot spots are different between calls for service and crime event. Future research should consider both flag and boost theories in hot spot forecasting.
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7

Wang, Chenyu, Zongyu Lin, Xiaochen Yang, Jiao Sun, Mingxuan Yue, and Cyrus Shahabi. "HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4193–200. http://dx.doi.org/10.1609/aaai.v36i4.20338.

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The goal of the crime forecasting problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph if the edges reveal high dependency or correlation. However, this distance-based pre-defined graph can not fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make a more accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.
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8

Lee, YongJei, O. SooHyun, and John E. Eck. "A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting." Police Quarterly 23, no. 2 (November 12, 2019): 174–201. http://dx.doi.org/10.1177/1098611119887809.

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Real-time crime hot spot forecasting presents challenges to policing. There is a high volume of hot spot misclassifications and a lack of theoretical support for forecasting algorithms, especially in disciplines outside the fields of criminology and criminal justice. Transparency is particularly important as most hot spot forecasting models do not provide their underlying mechanisms. To address these challenges, we operationalize two different theories in our algorithm to forecast crime hot spots over Portland and Cincinnati. First, we use a population heterogeneity framework to find places that are consistent hot spots. Second, we use a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm is implemented in Excel, making it extremely simple to apply and completely transparent. Our forecasting models show high accuracy and high efficiency in hot spot forecasting in both Portland and Cincinnati context. We suggest previously developed hot spot forecasting models need to be reconsidered.
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9

Alwee, Razana, Siti Mariyam Hj Shamsuddin, and Roselina Sallehuddin. "Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/951475.

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Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
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10

Gorr, Wilpen, Andreas Olligschlaeger, and Yvonne Thompson. "Short-term forecasting of crime." International Journal of Forecasting 19, no. 4 (October 2003): 579–94. http://dx.doi.org/10.1016/s0169-2070(03)00092-x.

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11

CLEMENTS, MICHAEL P., and ROBERT WITT. "FORECASTING QUARTERLY AGGREGATE CRIME SERIES*." Manchester School 73, no. 6 (December 2005): 709–27. http://dx.doi.org/10.1111/j.1467-9957.2005.00473.x.

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12

Wickrama, Chathura B., Ruwan D. Nawarathna, and Lakshika S. Nawarathna. "Forecasting homicides, rapes and counterfeiting currency: A case study in Sri Lanka." Biometrics & Biostatistics International Journal 9, no. 6 (December 31, 2020): 209–15. http://dx.doi.org/10.15406/bbij.2020.09.00322.

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Crimes have been disturbing threats to all the Sri Lankans all over the country. Finding the main variables associated with crimes are very vital for policymakers. Our main goal in this study is to forecast of homicides, rapes and counterfeiting currency from 2013 to 2020 using auto-regressive conditional Poisson (ACP) and auto-regressive integrated moving average (ARIMA) models. All the predictions are made assuming that the prevailing conditions in the country affecting crime rates remain unchanged during the period. Moreover, multiple linear regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to identify the key variables associated with crimes. Profiling of districts as safe or unsafe was performed based on the overall total crime rate of Sri Lanka which is to compare with individual district’s crime rates. Data were collected from the Department of Police and Department of Census and Statistics, Sri Lanka. It is observed that there are 14 safe and 11 unsafe districts in Sri Lanka. Moreover, it is found that the total migrant population and percentage of urban population is positively correlated with total crime. Besides, total migrant population, unemployment rate, mean household income and percentage of the urban population are significant variables for total crimes, and total migrant population, Gini index, mean household income and percentage of the urban population are significant variables for homicides. Random K-nearest neighbour (RKNN) algorithm classified districts as safe and unsafe with 84% of prediction accuracy.
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13

Chua, Everly. "Crime Data Forecasting using Exponential Smoothing." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.1 S I (February 15, 2020): 69–75. http://dx.doi.org/10.30534/ijatcse/2020/1391.12020.

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14

Berk, Richard. "Forecasting Methods in Crime and Justice." Annual Review of Law and Social Science 4, no. 1 (December 2008): 219–38. http://dx.doi.org/10.1146/annurev.lawsocsci.3.081806.112812.

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15

Rattner, Arye. "Social indicators and crime rate forecasting." Social Indicators Research 22, no. 1 (February 1990): 83–95. http://dx.doi.org/10.1007/bf00286392.

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16

Ismail, Suzilah, and Nurulhuda Ramli. "Short-term Crime Forecasting in Kedah." Procedia - Social and Behavioral Sciences 91 (October 2013): 654–60. http://dx.doi.org/10.1016/j.sbspro.2013.08.466.

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17

Hälterlein, Jens. "Epistemologies of predictive policing: Mathematical social science, social physics and machine learning." Big Data & Society 8, no. 1 (January 2021): 205395172110031. http://dx.doi.org/10.1177/20539517211003118.

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Predictive policing has become a new panacea for crime prevention. However, we still know too little about the performance of computational methods in the context of predictive policing. The paper provides a detailed analysis of existing approaches to algorithmic crime forecasting. First, it is explained how predictive policing makes use of predictive models to generate crime forecasts. Afterwards, three epistemologies of predictive policing are distinguished: mathematical social science, social physics and machine learning. Finally, it is shown that these epistemologies have significant implications for the constitution of predictive knowledge in terms of its genesis, scope, intelligibility and accessibility. It is the different ways future crimes are rendered knowledgeable in order to act upon them that reaffirm or reconfigure the status of criminological knowledge within the criminal justice system, direct the attention of law enforcement agencies to particular types of crimes and criminals and blank out others, satisfy the claim for the meaningfulness of predictions or break with it and allow professionals to understand the algorithmic systems they shall rely on or turn them into a black box. By distinguishing epistemologies and analysing their implications, this analysis provides insight into the techno-scientific foundations of predictive policing and enables us to critically engage with the socio-technical practices of algorithmic crime forecasting.
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18

K, Vijay, and Amal Prakash. "A Systematic Review on Tanpin Kandri Based Crime Prediction." Remittances Review 7, no. 2 (November 19, 2022): 01–11. http://dx.doi.org/10.47059/rr.v7i2.2407.

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Predictive policing refers to the potential preventive measure to fight crimes in the modern age. Crimes are social issues that have a negative effect on society. The number of cases filed under the Indian Penal Code (IPC) in 2020 increased by over 430 percent compared to the same year-ago period. Data analytics is the process of inspecting, transforming, and modelling unstructured data into useful information. Predictive policing and crime analytics with Artificial intelligence increase attention among a multifarious scientific community. Crime pattern analysis is the computational approach to examining aspects of volume crime for the purposes of prevention where it either targets one crime or a series of discrete crimes for detection. Currently, there are not many sufficing analysis methods for the Law and order agencies. By using the Tanpin Kanri model, a Japanese business strategy, this idea makes decision points on what crime and at what rate. Crime analysis functions are generally focused on seven key areas: crime pattern detection, target profiles, forecast crime rates, resource allocation, and criminal investigations, according to a study by the International Association of Chiefs of Police. Generally, it is implemented for the most recurring crimes. This idea helps in the prevention of crimes using crime forecasting and gives a unique perspective on the areas one may not be aware of. Thus crime prediction is a valuable strategy to identify and profile those areas which are at risk, and those can be prevented.
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Khatun, Most Rokeya, Safial Islam Ayon, Md Rahat Hossain, and Md Jaber Alam. "Data mining technique to analyse and predict crime using crime categories and arrest records." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 1052. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1052-1060.

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Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies at the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN) and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, theft, etc. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.
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20

Koroleva, Marina V. "Self-Determination of Organized Crime." Legal education and science 11 (November 19, 2020): 21–26. http://dx.doi.org/10.18572/1813-1190-2020-11-21-26.

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Purpose. To study the problem of self-determination of organized crime, which can influence the intensification of other types of crime. Methodology: it includes the following methods: historical and legal, comparative legal, analysis and forecasting. Conclusions. 1. Organized crime is determined by the interaction of many such negative phenomena and processes in various spheres of the functioning of society such as social, economic, political, legal and others. 2. Organized crime is the main corrupt person, involving a wide range of officials in corrupt relations, who then contribute to the preservation and development of organized criminal structures, help them evade taxes, acquire property, evade criminal liability for crimes committed, etc. Scientific and practical significance. The conclusions presented in the article are aimed at increasing the effectiveness of combating organized crime in general.
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21

Laskowska, Katarzyna. "The theoretical Basis for Criminological forecasting of Crime (with Russia Example)." Internal Security 10, no. 1 (November 27, 2018): 95–105. http://dx.doi.org/10.5604/01.3001.0012.7492.

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The paper discusses the problems of criminological forecasting of crime. The analyses have been based on issues concerning theoretical foundations and assumptions of this process in Russia. Since it is a country with considerable socio-demographic, religious and cultural diversity, it must analyse, assess and monitor these territorial differences in crime and skilfully anticipate the changes taking place there. Russia is the country that already has experience in predicting the development of crime. Current Russian criminological literature has been used in the deliberations. The publication presents various definitions of criminological crime forecasting. Attention was drawn not only to the need to anticipate changes in criminal trends but also to the evolution of crime causes. The sources of information necessary to forecast this phenomenon have also been indicated. It has been emphasized that this is, in particular, knowledge about socio-economic and political transformations taking place in the country. The several stages and complexity of the forecasting process, as well as its result in the form of a crime forecast, have also been underlined. By presenting a number of forecasting objectives, its usefulness for the protection of the security of the state and its citizens have been emphasized. The basics of conducting research analyses in the form of three scientific methods (extrapolation, modelling, and application of criminological expertise) have been discussed, as well. The classifications of forecasting and the periods applied in practice have been presented. The above issues have been discussed on the basis of theoretical assumptions adopted by Russian criminologists. Attention has been focused on the most important issues for preventing and combatting crime. The considerations have confirmed the need to anticipate the development of crime.
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22

Lee, Jun-Hyeong and Sangho Kim. "Current Characteristics and Forecasting of Foreigner Crime." Journal of Korean Public Police and Security Studies 10, no. 1 (May 2013): 79–100. http://dx.doi.org/10.25023/kapsa.10.1.201305.79.

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23

Kostyuk, O. "The role of social networks in combating crimes against the fundamentals of national security." Yearly journal of scientific articles “Pravova derzhava”, no. 33 (September 2022): 584–92. http://dx.doi.org/10.33663/1563-3349-2022-33-584-592.

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Introduction. Defi ning the role of the media in combating crimes in general and crimes against national security in particular is extremely relevant today. In this context, it is equally important to study the criminological potential of social networks. An analysis of scientifi c sources shows that there is currently a somewhat simplified description of the relationship between the media and the Internet. Authors mostly defi ne the classifi cation of media as print media, television and the Internet, which is usually understood as electronic media, at most author’s blogs. There are also more extensive classifi cations of online media, which, however, remain simplifi ed and do not refl ect the full range of tools available today. The aim of the article. Disclosure of aspects of the use of social networks in combating crimes against the foundations of national security. Results. Given the importance of social networks, it is on them, and not on the traditional media, that attention should be focused on the issues of forecasting and combating crimes in general and crimes against the foundations of national security of Ukraine, in particular. At the same time, appropriate actions should be taken not only by law enforcement agencies, but also by civil society, which includes not only users of social networks, but also their owners.. Conclusions. A separate modern direction of combating crime with the use of social networks is the analysis of data contained in them. This requires the creation, adjustment and improvement of algorithms for analyzing user activity. Key words: national security, mass media, social networks, crime prevention, crime forecasting
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Butt, Umair Muneer, Sukumar Letchmunan, Fadratul Hafinaz Hassan, and Tieng Wei Koh. "Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy." PLOS ONE 17, no. 9 (September 7, 2022): e0274172. http://dx.doi.org/10.1371/journal.pone.0274172.

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The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been made in the crime prediction area due to the deficiency of spatiotemporal crime data. Several machine learning, deep learning, and time series analysis techniques are exploited, but accuracy issues prevail. Thus, this study proposed a Bidirectional Long Short Term Memory (Bi-LSTM) and Exponential Smoothing (ES) hybrid for crime forecasting. The proposed technique is evaluated using New York City crime data from 2010–2017. The proposed approach outperformed as compared to state-of-the-art Seasonal Autoregressive Integrated Moving Averages (SARIMA) with low Mean Absolute Percentage Error (MAPE) (0.3738, 0.3891, 0.3433,0.3964), Root Mean Square Error (RMSE)(13.146, 13.669, 13.104, 13.77), and Mean Absolute Error (MAE) (9.837, 10.896, 10.598, 10.721). Therefore, the proposed technique can help law enforcement agencies to prevent and control crime by forecasting crime patterns.
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Brindha, R., and Dr M. Thillaikarasi. "Crime Data Forecasting Using Machine Learning and Big Data Analytics." Webology 18, Special Issue 04 (December 8, 2021): 591–606. http://dx.doi.org/10.14704/web/v18si04/web18284.

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Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”
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LLOYD, DAVID J. B., NARATIP SANTITISSADEEKORN, and MARTIN B. SHORT. "Exploring data assimilation and forecasting issues for an urban crime model." European Journal of Applied Mathematics 27, no. 3 (December 2, 2015): 451–78. http://dx.doi.org/10.1017/s0956792515000625.

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In this paper, we explore some of the various issues that may occur in attempting to fit a dynamical systems (either agent- or continuum-based) model of urban crime to data on just the attack times and locations. We show how one may carry out a regression analysis for the model described by Shortet al.(2008,Math. Mod. Meth. Appl. Sci.) by using simulated attack data from the agent-based model. It is discussed how one can incorporate the attack data into the partial differential equations for the expected attractiveness to burgle and the criminal density to predict crime rates between attacks. Using this predicted crime rate, we derive a likelihood function that one can maximise in order to fit parameters and/or initial conditions for the model. We focus on carrying out data assimilation for two different parameter regions, namely in the case where stationary and non-stationary crime hotspots form. It is found that the likelihood function is ‘flat’ for large ranges of parameters, and that this has major implications for crime forecasting. Hence, we look at how one might carry out a goodness-of-fit and forecasting analysis for crime rates given the range of parameter fits. We show how one can use the Kolmogorov–Smirnov statistic to assess the goodness-of-fit. The dynamical systems analysis of the partial differential equations proves invaluable to understanding how the crime rate forecasts depend on the parameters and their sensitivity. Finally, we outline several interesting directions for future research in this area where we believe that the combination of dynamical systems modelling, analysis, and data assimilation can prove effective in developing policing strategies for urban crime.
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Kocher, Milena, and Michael Leitner. "Forecasting of Crime Events Applying Risk Terrain Modeling." GI_Forum 1 (2015): 30–40. http://dx.doi.org/10.1553/giscience2015s30.

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Tosti, Padideh. "Forecasting crime and narcobusiness: Iraq after the war." Conflict, Security & Development 4, no. 1 (April 2004): 91–95. http://dx.doi.org/10.1080/1467880042000206886.

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29

Dugato, Marco, Serena Favarin, and Antonio Bosisio. "Isolating Target And Neighbourhood Vulnerabilities In Crime Forecasting." European Journal on Criminal Policy and Research 24, no. 4 (May 11, 2018): 393–415. http://dx.doi.org/10.1007/s10610-018-9385-2.

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30

Cruz-Nájera, Mariel Abigail, Mayra Guadalupe Treviño-Berrones, Mirna Patricia Ponce-Flores, Jesús David Terán-Villanueva, José Antonio Castán-Rocha, Salvador Ibarra-Martínez, Alejandro Santiago, and Julio Laria-Menchaca. "Short Time Series Forecasting: Recommended Methods and Techniques." Symmetry 14, no. 6 (June 14, 2022): 1231. http://dx.doi.org/10.3390/sym14061231.

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This paper tackles the problem of forecasting real-life crime. However, the recollected data only produced thirty-five short-sized crime time series for three urban areas. We present a comparative analysis of four simple and four machine-learning-based ensemble forecasting methods. Additionally, we propose five forecasting techniques that manage the seasonal component of the time series. Furthermore, we used the symmetric mean average percentage error and a Friedman test to compare the performance of the forecasting methods and proposed techniques. The results showed that simple moving average with seasonal removal techniques produce the best performance for these series. It is important to highlight that a high percentage of the time series has no auto-correlation and a high level of symmetry, which is deemed as white noise and, therefore, difficult to forecast.
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Novichkov, V. E. "FORECASTING IN COMBATING CRIME AND IMPROVING THE MANAGEMENT PRACTICES OF LAW ENFORCEMENT AGENCIES ON THE APPLICATION OF MEASURES OF CRIMINAL AND LEGAL IMPACT ON CRIME WITH THE CRITERIA OF THEIR EFFECTIVENESS." Proceedings of the Southwest State University 21, no. 5 (October 28, 2017): 204–11. http://dx.doi.org/10.21869/2223-1560-2017-21-5-204-211.

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The article discusses the possibility of prediction in combating crime in envisioning ways to improve the efficiency of law enforcement to impact crime through criminal law and other measures. Underline the fact that specified in the scope of this article aspects of domestic criminal law theory has not worked out common approaches, as evidenced by ongoing discussions on this issue. In particular, there is no common understanding of the logical-linguistic phenomena, among them the basic concept: "measures of criminal and legal impact", in connection with which the article is their original definition. For criminal law science, as with other legal Sciences remain difficult surveys to develop criteria for an effective impact on crime and forecasting. The paper presents the concept of "work" and management practices of law enforcement agencies on the effective application of measures of criminal and legal impact on crime and, primarily, on the basis of one of the main objectives of the criminal law - the prevention of crimes. The structure of this scheme consists of four groups that must be included in the development of forecasts in the sphere of fight against crime and its control and management. Is this: criminally-legal measures of crime prevention are in the educational effect on volatile and other persons and do not involve criminal responsibility; criminal-legal measures of crime prevention with the prevention of harmful consequences of the criminal act, deprivation of an offender to continue criminal activities, etc. achieved in the PU, the application of the perpetrators of legitimate violence (necessary defence, detention of the criminal) and criminal-law enforcement (criminal prosecution for preparation or attempted crime or completed less severe, compared to warned a crime); criminal - legal measures of implementation of criminal responsibility; other measures of impact on crime in furtherance of the purposes of criminal liability, beyond the considered groups, although having a number of their characteristics, as the application of the procedural measures of restraint in respect of suspects and accused persons, the application of compulsory measures of a medical nature to condemn alcoholics and drug addicts, all that is subordinated to the goal of preventing recurrence of crimes. Considering the issues of measures of criminal and legal impact on crime in connection with the prediction of the whole sphere of combating crime and related law enforcement the article notes the broad approach to the application of measures of criminal and legal impact on crime from the point of view of direct use of such measures in law enforcement for the prevention, suppression of crimes and the implementation of criminal responsibility is gained, sitela, which are based on criminal and other laws regulating the fight against crime. From the point of view of assessing practice effectiveness of the application of the criminal law as observationsas measures, the paper proposes to evaluate it according to formal parameters: the number of publications and broadcasts on television and radio, lectures, etc., and the effectiveness of these measures, as individual preventive measures is proposed to determine two parameters: the rate of detection of potential offenders (by retrospective analysis of the criminal cases of intentional crimes); the level of the positive impact of advocacy on identified potential offenders (by definition of the dynamics of the share of those who have committed crimes).
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32

Rakesh, Bathula. "Deep Learning Process in Analyzing Crimes." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3074–83. http://dx.doi.org/10.22214/ijraset.2022.44577.

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Abstract: This research explores its use of machine learning to predict crime. The data from the last 15 years of Vancouver crime is studied using two alternative data-processing methods in this study approaches. A crime is detected through machinelearning predictive techniques such as K-nearest Neighbour and boosted decision tree. When the forecast accuracy is between 39 percent and 44 percent, Vancouver crime forecasting
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33

Suhadiyah, Elsyah, Sajaratud Dur, and Hendra Cipta. "Forecasting of The Crime Rate Using Automatic Clustering and Fuzzy Logic Relationship Method In North Sumatra." International Journal of Science and Environment (IJSE) 2, no. 1 (February 27, 2022): 14–23. http://dx.doi.org/10.51601/ijse.v2i1.14.

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Currently the crime rate is very alarming and reported in various mass and electronic media. The high crime rate in the Province of Nort Sumatra is very unsettling for the community. The purpose of this research is to get the result of forecasting the crime ratel in the 2021 - 2024 using Automatic Clustering And Fuzzy Logic Relationship (ACFLR) method. The advantage of this method is that the method has a high level of accuracy because the Mean Absolute Percentage Error (MAPE) value is relative small and the results of forecasting analysis obtained in 2021 there are 31522 cases, in 2022 are 31533 cases, in 2023 are 31574 cases and the last one in 2024 was 31602 cases. In addition, the prediction error rate MAPE obtained is 0,35 % Keywords: Crime rate, Automatic Clustering And Fuzzy Logic Relationship.
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34

Caplan, Joel M., Leslie W. Kennedy, and Eric L. Piza. "Joint Utility of Event-Dependent and Environmental Crime Analysis Techniques for Violent Crime Forecasting." Crime & Delinquency 59, no. 2 (November 16, 2012): 243–70. http://dx.doi.org/10.1177/0011128712461901.

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35

Noor, Noor Maizura Mohamad, Astari Retnowardh, Mohd Lazim Abd, and Md Yazid Mohd Saman. "Crime Forecasting using ARIMA Model and Fuzzy Alpha-cut." Journal of Applied Sciences 13, no. 1 (December 15, 2012): 167–72. http://dx.doi.org/10.3923/jas.2013.167.172.

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36

Hanslmaier, Michael, Stefanie Kemme, Katharina Stoll, and Dirk Baier. "Forecasting Crime in Germany in Times of Demographic Change." European Journal on Criminal Policy and Research 21, no. 4 (March 26, 2015): 591–610. http://dx.doi.org/10.1007/s10610-015-9270-1.

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37

Skoromnyi, Danylo A. "Legal entities' crime prevention: Foreign experience and prospects of application in Ukraine." Journal of the National Academy of Legal Sciences of Ukraine 28, no. 3 (September 17, 2021): 301–10. http://dx.doi.org/10.37635/jnalsu.28(3).2021.301-310.

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The relevance of the problem under study lies in the fact that crime among legal entities is currently increasing in the countries of the world. This phenomenon is extremely dangerous, because corporate crime is associated with the commission of economic crimes – the legalisation of illegally obtained income and corruption, both of which negatively affect the economy of an individual state and the global economy. To prevent crime among legal entities, governments of countries need to take measures aimed at countering corporate crime, take advantage of technological advance in detecting and preventing offences among legal entities. The purpose of this study is to identify the features of measures to prevent corporate crime in foreign countries, to analyse the prospects for applying the experience of other states in developing their effective counteraction measures. Innovative approaches and methods that will increase the effectiveness of measures to combat corporate crime were also proposed. The leading methods employed in this study are theoretical: the study of scientific literature, as well as regulatory documents to clarify the state of the problem under study. Analysis, synthesis, comparison, generalisation, and modelling were used, which allowed describing the terminology. Furthermore, the system method, dialectical, and historical analysis methods were used in the study of regulations, also including such special methods as the method of legal interpretation, the method of legal forecasting. The result of the present paper is the identification of the importance of corporate crime prevention, effective measures that are applied to legal entities to detect and prevent corporate crime. As a result of this study, possible measures aimed at preventing corporate crime were proposed, considering the positive experience of foreign countries. Having analysed the state of corporate crime in other countries of the world, the authors conclude that Ukraine should implement measures to prevent crimes among legal entities to reduce the number of offences and increase the level of the national economy
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Skoromnyi, Danylo A. "Legal entities' crime prevention: Foreign experience and prospects of application in Ukraine." Journal of the National Academy of Legal Sciences of Ukraine 28, no. 3 (September 17, 2021): 301–10. http://dx.doi.org/10.37635/jnalsu.28(3).2021.301-310.

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The relevance of the problem under study lies in the fact that crime among legal entities is currently increasing in the countries of the world. This phenomenon is extremely dangerous, because corporate crime is associated with the commission of economic crimes – the legalisation of illegally obtained income and corruption, both of which negatively affect the economy of an individual state and the global economy. To prevent crime among legal entities, governments of countries need to take measures aimed at countering corporate crime, take advantage of technological advance in detecting and preventing offences among legal entities. The purpose of this study is to identify the features of measures to prevent corporate crime in foreign countries, to analyse the prospects for applying the experience of other states in developing their effective counteraction measures. Innovative approaches and methods that will increase the effectiveness of measures to combat corporate crime were also proposed. The leading methods employed in this study are theoretical: the study of scientific literature, as well as regulatory documents to clarify the state of the problem under study. Analysis, synthesis, comparison, generalisation, and modelling were used, which allowed describing the terminology. Furthermore, the system method, dialectical, and historical analysis methods were used in the study of regulations, also including such special methods as the method of legal interpretation, the method of legal forecasting. The result of the present paper is the identification of the importance of corporate crime prevention, effective measures that are applied to legal entities to detect and prevent corporate crime. As a result of this study, possible measures aimed at preventing corporate crime were proposed, considering the positive experience of foreign countries. Having analysed the state of corporate crime in other countries of the world, the authors conclude that Ukraine should implement measures to prevent crimes among legal entities to reduce the number of offences and increase the level of the national economy
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39

Koimshidi, G. F., D. K. Chirkov, and A. A. Litvinov. "Territorial analysis of crime in rural areas and its short-term forecast for 2022." Russian Journal of Economics and Law 16, no. 3 (September 10, 2022): 610–24. http://dx.doi.org/10.21202/2782-2923.2022.3.610-624.

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Objective: to analyze the criminological situation in the Russian Federation and the territorial differences in crime in rural areas, as well as to make a short-term forecast of this type of crime in the Russian Federation.Methods: dynamic, statistical, historical, systemic-structural and comparative-legal methods, as well as a specific sociological methodology of estimating rural crime.Results: the main trend of rural crime from January 1, 2019 to October 1, 2021 can be considered a uniform straightline decrease: a constant monthly decrease by 1.1 thousand crimes. This trend characterizes a situation when the effect of criminogenic factors is neutralized by the impact of anti-criminogenic ones. If we take the above-mentioned data as a basis and generalize them for the next year, we can assume that in 2022 from 331.2 thousand to 388.8 thousand crimes will be registered in rural areas of the Russian Federation. Meanwhile, it should be noted that official data on the state of crime in rural areas, as well as the criminal statistics in the Russian Federation as a whole, cause well-founded doubts. The landslide reduction in crimes in rural areas does not follow from the ongoing social changes in rural areas and gives reason to believe that such a reduction is due to a violation of accounting and registration discipline on the part of staff of the law-enforcement bodies.Scientific novelty: for the first time, a comparative legal analysis of the dynamics of registered crimes in rural and urban areas was carried out, as a result of which the features of crime in rural areas were identified and the author’s short-term forecast for 2022 was proposed. Despite the tendency to reduce the number of crimes registered in rural areas in 2009-2021, the criminal situation in rural areas remains complicated. Statistical indicators do not reflect the real state of this type of crime, since this trend is due to artificial regulation of the number of crimes reflected in statistical reporting towards understatement.Practical significance: as a result of the conducted criminological research, a high degree of public danger of crime in rural areas has been revealed, and its forecasting makes it possible to pay closer attention to the need to develop effective measures to combat and prevent this type of crime.
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40

Gabay, Polina G. "On the Concept of Criminological Ratios of Unintentional Crime in Medical Assistance Rendering." Legal education and science 11 (November 19, 2020): 31–34. http://dx.doi.org/10.18572/1813-1190-2020-11-31-34.

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Purpose. To study the indices of carelessness in the provision of medical care from a criminological standpoint. Methodology: it includes the following methods: historical and legal, comparative legal, analysis and forecasting. Conclusions. 1. Elements of everyday carelessness in health care can be distinguished only conditionally and only in the sphere of relations that are within the framework of personal relations between medical personnel and patients. 2. The structure of health crime includes three groups of crimes: professional crimes of health workers; malfeasance of employees of the studied area; crimes, the responsibility for which arises for these subjects along with other persons. Scientific and practical significance. The conclusions presented in the article are aimed at increasing the effectiveness of counteracting careless criminality in the healthcare sector in the provision of medical care.
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41

Zhesterov, Pavel V. "The Place of Criminal Repression in the Modern Criminal Law Futurology." Legal education and science 10 (October 8, 2020): 35–40. http://dx.doi.org/10.18572/1813-1190-2020-10-35-40.

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Purpose. The author reveals the issues of a new direction of criminological foresight - criminal law futurology. The author clarifies the role of predictions in the fight against crime and the prevention of crimes by criminal means. Methodology: the study uses a set of dialectical, systemic, logical methods. The author pays special attention to the genesis of the essence and content of criminological forecasting. Conducts a comparative analysis of the latest forecasting methods, based on the use of modern technologies and based on mathematical tools. Conclusions. The author concludes that further short-term and long-term criminological studies of a prognostic nature are necessary, the results of which can be more widely used in the formation and implementation of criminal policy. The author indicates promising directions for further scientific research.
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42

Dugato, Marco, Francesco Calderoni, and Giulia Berlusconi. "Forecasting Organized Crime Homicides: Risk Terrain Modeling of Camorra Violence in Naples, Italy." Journal of Interpersonal Violence 35, no. 19-20 (June 13, 2017): 4013–39. http://dx.doi.org/10.1177/0886260517712275.

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Mafia homicides are usually committed for retaliation, economic profit, or rivalry among groups. The variety of possible reasons suggests the inefficacy of a preventive approach. However, like most violent crimes, mafia homicides concentrate in space due to place-specific social and environmental features. Starting from the existing literature, this study applies the Risk Terrain Modeling approach to forecast the Camorra homicides in Naples, Italy. This approach is based on the identification and evaluation of the underlying risk factors able to affect the risk of a homicide. This information is then used to predict the most likely location of future events. The findings of this study demonstrate that past homicides, drug dealing, confiscated assets, and rivalries among groups make it possible to predict up to 85% of 2012 mafia homicides, identifying 11% of city areas at highest risk. By contrast, variables controlling for the socio-economic conditions of areas are not significantly related to the risk of homicide. Moreover, this study shows that, even in a restricted space, the same risk factors may combine in different ways, giving rise to areas of equal risk but requiring targeted remedies. These results provide an effective basis for short- and long-term targeted policing strategies against organized crime- and gang-related violence. A similar approach may also provide practitioners, policy makers, and local administrators in other countries with significant support in understanding and counteracting also other forms of violent behavior by gangs or organized crime groups.
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43

SHCHOKIN, Rostyslav, Vitaliy MAZIYCHUK, Oleksandr MYKHAILYK, Alina KOLOMIIETS, Svitlana AKIFZADE, and Yuriy TYMOSHENKO. "THE IMPACT OF THE CRIME RATE ON THE HOSPITALITY AND TOURISM INDUSTRY IN THE EU COUNTRIES." GeoJournal of Tourism and Geosites 46, no. 1 (March 31, 2023): 135–47. http://dx.doi.org/10.30892/gtg.46115-1009.

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Tourism is the largest service industry in the world. The direct and reverse relationship between crime and tourism significantly affects the economy, society and individuals. Studying this impact is necessary for the state policy making and the organization of law enforcement agencies. The aim of this study was to determine the specifics of the criminal situation in the hospitality and tourism in the EU. The research involved system approach, descriptive analysis, systematic sampling, doctrinal approach, statistical analysis and forecasting as research methods. The results of specialized studies were summarized, and the lack of a comprehensive background for effective law enforcement was revealed. The main trends of the European tourism industry were identified. General European trends regarding the impact of crime on the tourism industry were revealed: a positive correlation of the security index with terrorist attacks; the relationship between the statistical significance of migration processes and the peculiarities of human trafficking in particular EU countries. The theoretical and practical problems of imperfect statistics on tourism-related crimes were confirmed. An approach to the preventive policy principles in the tourism industry was presented. The impact of crime on the hospitality and tourism is a multidimensional problem that requires the development of special preventive policies. The prospects for improving crime prevention in the tourism sector are related to the improvement of the practice of registering crimes and attracting additional opportunities for the public and subjects of the tourism industry. Prospects for further research include identifying the relationship between crime and various types of tourism.
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44

Wang, Tien-Chin, and Bi-Chao Lee. "Community security is the key to sustainable governance: Methods and functions of crime hotspot predictions." Corporate Governance and Sustainability Review 5, no. 2 (2021): 57–72. http://dx.doi.org/10.22495/cgsrv5i2p5.

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Forecasting is becoming increasingly important in corporate sustainability governance, as is government governance, and the prediction of police crime hotspots is related to human rights, so transparency is needed. There are many ways to predict hotspots of criminal activity in urban areas. Experts assume that if many crimes occur somewhere, even more, are likely to happen at subsequent times. Such predictions may rely on a state dependency model such as the Poisson distribution algorithm to formulate re-occurrence, its results can provide a visualized hotspot map with Q-GIS maps. Forecasting sets the threshold for re-occurrence and affects the distribution of the forecast. This paper studies the occurrence of criminal activity in urban areas, refers to the metrics set by the NIJ’s crime prediction contest and focuses on the presentation of the results by accumulating different historical data. It was determined that when the amount of cumulative data is greater, its prediction measures by the prediction accuracy index (PAI) insures that accuracy is improved, but the prediction efficiency index (PEI) that efficiency level is worse. Because threshold setting directly affects the performance of the forecast, it can be used differently. Here sets four different indicators, hit rate, useful rate, waste rate, and missing rate. It was determined that the hit rate, missing rate, the PAI value, and the PEI value are directly proportional to the threshold value, while the trend of useful rate and waste rate are inversely related. Concerned policymakers can set different thresholds dependent up the number and budgetary constraints of police forces, and they can work towards achieving crime prevention in urban hotspots. Importantly, Poisson’s approach can be simply implemented with Excel, be conducive to drive by the office practitioner, and elevate the transparency of crime prediction.
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45

Tymchyshyn, Andriy, Anna Semeniaka, Serhii Bondar, Nataliia Akhtyrska, and Olena Kostiuchenko. "The use of big data and data mining in the investigation of criminal offences." Revista Amazonia Investiga 11, no. 56 (October 18, 2022): 278–90. http://dx.doi.org/10.34069/ai/2022.56.08.27.

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The aim of this study was to determine the features and prospects of using Big Data and Data Mining in criminal proceedings. The research involved the methods of a systematic approach, descriptive analysis, systematic sampling, formal legal approach and forecasting. The object of using Big Data and Data Mining are various crimes, the common features of which are the seriousness and complexity of the investigation. The common tools of Big Data and Data Mining in crime investigation and crime forecasting as interrelated tasks were identified. The creation of databases is the result of the processing of data sources by Data Mining methods, each being distinguished by the specifics of use. The main risks of implementing Big Data and Data Mining are violations of human rights and freedoms. Improving the use of Big Data and Data Mining requires standardization of procedures with strict adherence to the fundamental ethical, organizational and procedural rules. The use of Big Data and Data Mining is a forensic innovation in the investigation of serious crimes and the creation of an evidence base for criminal justice. The prospects for widespread use of these methods involve the standardization of procedures based on ethical, organizational and procedural principles. It is appropriate to outline these procedures in framework practical recommendations, emphasizing the responsibility of officials in case of violation of the specified principles. The area of further research is the improvement of innovative technologies and legal regulation of their application.
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46

Kleymenov, M. P. "Methodology of criminal law forecasting." Law Enforcement Review 6, no. 4 (December 26, 2022): 277–88. http://dx.doi.org/10.52468/2542-1514.2022.6(4).277-288.

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The subject. Criminal law forecasting is a scientifically based analysis of the prospects for the development of criminal law in order to optimize criminal legislation and the practice of its application. Its subject includes: foreseeing the needs of society in the criminal law regulation of public relations, their criminalization and decriminalization; the dynamics of the development of criminal law relations in society, the development of a criminal law concept of combating socially dangerous phenomena for the foreseeable period; foreseeing the consequences of changes in criminal legislation; the presence of real prerequisites for its application; prognostic assessments of the effectiveness of criminal law norms in the process of law-making and law-realization activities; scenarios and models for the implementation of criminal law institutions and norms; technologies for combating criminality in the application of criminal law norms; prospectsfor the development of the science of criminal law itself, taking into account its scientific potential.The purpose of the article is to establish the ideology and main trends in the development of criminal legislation and the practice of its application in post-Soviet Russia, to determine the methodology for the modernization of criminal policy in the new geopolitical conditions.The methodology of research includes axiological and system approaches, determinative analysis, search and normative forecasting, extrapolation, expert assessments, modeling.The main results, scope of application. There are two diametrically opposed ideologies that are of fundamental importance for criminal law and criminal law forecasting: 1) liberal and 2) conservative. The criminal law policy of the Russian Federation has so far developed in line with liberal ideology. Its main goal is to modernize criminal legislation in terms of decriminalizing economic crimes and humanizing the treatment of white-collar criminals. Conservative criminal law policy is based on the methodology of normative forecasting, which is aimed at achieving the desired (for the state and society) results. This methodology is based on a systematic approach. From the standpoint of this approach, the object of criminal law forecasting is an organized system with an extremely complex structure consisting of three subsystems: managing, managed and criminal law norms. The content of each of these subsystems requires corrective action in order to achieve compliance with the traditional axiological scale and common sense. It is also necessary to solve the problem of coordinating criminal law and criminological legislation.Conclusions. Criminal law forecasting allows us to formulate a number of theses that should be the basis for the concept of optimizing the criminal policy of the Russian Federation: (a) rejection of the liberal model of criminal law regulation of public relations, the transition to a conservative model, which should be dominated by state and public, not private interests; (b) recognition of organized economic and official crime as priority objects of criminallegal influence; (c) coordination of criminal-legal and criminological legislation; (d) adoption of the Federal Law "On Combating Organized Crime".
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47

JOHNSON, SHANE D. "A brief history of the analysis of crime concentration." European Journal of Applied Mathematics 21, no. 4-5 (April 15, 2010): 349–70. http://dx.doi.org/10.1017/s0956792510000082.

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Decades of research demonstrate that crime is concentrated at a range of spatial scales. Such findings have clear implications for crime forecasting and police resource allocation models. More recent work has also shown that crime clusters in space and time with a regularity that might improve methods of crime prediction. In this paper I review some of the available evidence and provide illustrations of the types of analysis – spatial and spatio-temporal – conducted hitherto. With a few exceptions, the application of formal Mathematics in the study of space–time patterns of crime has been rather limited, and so a central aim of the paper is to stimulate interest in this area of research.
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Wan, Jia Di, Yuan Biao Zhang, Wen Jing Yu, and Chuan He. "A New Model for Forecasting the Locations of Next Crime." Applied Mechanics and Materials 29-32 (August 2010): 1116–21. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.1116.

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The paper is aimed at forecasting the locations of next crime. Based on local demographic factors, geographic factors and environment psychological factors, it establishes a Multi-factor Evaluation Model. We then use an actual case (The Yorkshire Ripper case) for model testing, and get good test results.
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DEADMAN, D., and D. PYLE. "FORECASTING RECORDED PROPERTY CRIME USING A TIME-SERIES ECONOMETRIC MODEL." British Journal of Criminology 37, no. 3 (January 1, 1997): 437–45. http://dx.doi.org/10.1093/oxfordjournals.bjc.a014179.

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50

Wang, Bao, Penghang Yin, Andrea Louise Bertozzi, P. Jeffrey Brantingham, Stanley Joel Osher, and Jack Xin. "Deep Learning for Real-Time Crime Forecasting and Its Ternarization." Chinese Annals of Mathematics, Series B 40, no. 6 (November 2019): 949–66. http://dx.doi.org/10.1007/s11401-019-0168-y.

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