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Journal articles on the topic 'Crime forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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

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2

Abdurasulova, Kumrinisa, and Raziya Buranova. "CRIME FORECASTING AND METHODS OF IMPLEMENTATION." Review of Law Sciences 7, no. 3 (2023): 76–87. http://dx.doi.org/10.51788/tsul.rols.2023.7.3./tamw8465.

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The article reveals the history of the development of forecasting, the concept of forecasting, scientific forecasting, the importance of forecasting in criminology, including other sciences, the essence, features, and differentiation of criminological forecasting, its tasks, goals, and types, which include crime in general, but also the concept of forecasting individual criminal behavior, criminological situations, subjects and objects of forecasting, and their role in choosing the method to be used for forecasting crime. The general and special methods of realization of crime forecasting are

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3

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

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4

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 pres

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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 (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

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6

Singh, Rohit. "Crime Forecasting in India." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02917.

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Since British rule, India has kept track of crime. Today, the Home Affairs Ministry directs the National Crime Records Bureau to compile annual statistics. Crime rates are rising as the country develops and grows, necessitating analysis. The goal of this study is to identify trends in crime over time in order to forecast future developments and improve the nation's crime rate. To identify trends in crime records, the study employs a variety of exploratory techniques. Additionally, it displays data with its various features and attempts to predict future trends using machine learning. The goal

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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 (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

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Yadav, Kajal. "Crime Mapping and Forecasting Using Geospatial Techniques: A Case of Ajmer." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1323–38. http://dx.doi.org/10.22214/ijraset.2023.56178.

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Abstract: Advances in computer technology, the development of GIS application software, and the ability to access various geographic data through open-source data sources have allowed police and law enforcement agencies to use it effectively. Crime mapping and spatial analysis using GIS tools such as hotspot generation, zoning, crime navigation and profiling, mobile location recognition, and various web applications are clearly recognized and can be scientifically applied to improve citizenship while being effectively used to predict and control crime. The present study analyzed the trends and

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9

D G, Kavyashree, and Amruth M M. "Crime Trends Analysis." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–8. https://doi.org/10.55041/ijsrem.spejss010.

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Abstract—Crime has become a growing concern in urban and rural regions alike, posing significant challenges to law enforcement agencies and policymakers. The analysis and forecasting of crime rates through data-driven methods is the main topic of this paper. This study intends to uncover crime patterns, hotspot regions, and temporal trends by utilizing statistical approaches, machine learning techniques, and historical crime data. The system integrates real-time data (where available) and uses predictive models to forecast the likelihood of various types of crimes in specific areas. The primar

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D G, Kavyashree, and Ajay Kumar M. "Criminal Incidence Rate." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–7. https://doi.org/10.55041/ijsrem.spejss013.

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Abstract—Crime has become a growing concern in urban and rural regions alike, posing significant challenges to law enforcement agencies and policymakers. The analysis and forecasting of crime rates through data-driven methods is the main topic of this paper. This study intends to uncover crime patterns, hotspot regions, and temporal trends by utilizing statistical approaches, machine learning techniques, and historical crime data. The system integrates real-time data (where available) and uses predictive models to forecast the likelihood of various types of crimes in specific areas. The primar

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11

Gorr, Wilpen, and Richard Harries. "Introduction to crime forecasting." International Journal of Forecasting 19, no. 4 (2003): 551–55. http://dx.doi.org/10.1016/s0169-2070(03)00089-x.

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12

Parshintseva, Lidiya Sergeevna. "Analysis and forecasting of crime in Russia as a tool of effective public administration in the social sphere." Вопросы безопасности, no. 2 (February 2023): 9–18. http://dx.doi.org/10.25136/2409-7543.2023.2.40844.

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The purpose of the study is to identify the main trends and patterns of crime development in Russia in order to develop effective public administration measures aimed at improving the criminogenic situation. The object of the study is crime in the Russian Federation by categories of crimes. The subject of the study is statistical data characterizing the state and dynamics of crime in Russia. The study was conducted using statistical methods such as the method of relative values, index analysis, time series analysis and forecasting methods, in particular, adaptive methods, correlation and varia

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13

Ms. D. Kalpana and Mrs. A. Sathiya Priya. "Crime Type and Occurrence Prediction Using Machine Learning Algorithm." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1110–22. https://doi.org/10.32628/cseit25112473.

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Crime forecasting is essential for improving public safety and maximizing law enforcement resources. This paper describes a machine learning-based framework for forecasting both the type and incidence of crimes in cities. Using historical crime records, socio-economic conditions, and spatial patterns, we apply an array of machine learning algorithms, such as decision trees, random forests, and neural networks, to learn and forecast crime events. Our research explores the efficiency of feature selection methods to improve prediction accuracy and discovers influential predictors of crime occurre

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14

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 (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 correla

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15

Kipāne, Aldona, and Andrejs Vilks. "Crime Forecasting in the Digital Age: A Theoretical Framework." SOCRATES. Rīgas Stradiņa universitātes Juridiskās fakultātes elektroniskais juridisko zinātnisko rakstu žurnāls / SOCRATES. Rīga Stradiņš University Faculty of Law Electronic Scientific Journal of Law 2, no. 26 (2023): 1–9. http://dx.doi.org/10.25143/socr.26.2023.2.01-09.

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The aim of the study is to describe the criminological framework of crime forecasting based on special literature, prac-tice materials and research. Analytical, synthesis, inductive, deductive and descriptive research methods are used inthe article. The authors conclude that the development of full, comprehensive and highly reliable crime forecasts is alaborious and complex process. Predictive measures should be designed in a more urgent manner – this might en-compass the reporting of anticipated crimes in advance, as well as the indication of changes in the overall structure ofcrime and dange

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G. Pavan, S. Uday Kiran, S. Harshitha, S. Leela Sai Kumar, and VVDS Phanindra Naidu. "CRIME FORECASTING WITH MACHINE LEARNING AND DEEP LEARNING: A SYSTEMATIC ANALYSIS." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 194–200. https://doi.org/10.36893/jnao.2025.v16.i01.025.

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This study analyzes and predicts crime patterns using a dataset of crimes from Chicago starting from 2001. Through exploratory data analysis (EDA), we uncovered unique and non-unique case numbers and crime distributions. The data was preprocessed for machine learning by handling duplications, extracting date components, and encoding categorical data. Visualization techniques highlighted key trends and high-crime areas. We developed a predictive model using an artificial neural network (ANN), equipped with dense and dropout layers to prevent overfitting, and optimized using the Adam optimizer f

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17

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

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18

Lee, YongJei, O. SooHyun, and John E. Eck. "A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting." Police Quarterly 23, no. 2 (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 th

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19

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 (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 Selec

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20

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

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21

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

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22

Шестак, В. А., and А. Д. Цыплакова. "Issues regarding enhancing crime forecasting." Расследование преступлений: проблемы и пути их решения, no. 3(41) (October 16, 2023): 78–84. http://dx.doi.org/10.54217/2411-1627.2023.41.3.008.

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В работе исследована вероятная возможность использования методов машинного обучения для совершенствования прогнозирования преступности, а также обобщены результаты зарубежных исследований по оценке эффективности отдельных алгоритмов и моделей. Методологическую основу работы преимущественно составляют методы формальной логики (анализ и дедукция), классификации и системно-функциональный метод при сопоставлении данных. В доктрине выделяются следующие математические модели с использованием машинного обучения: глубинный анализ данных (англ. data mining), глубокое изучение (англ. deep learning), пре

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Yaya, Sudarya Triana, and Retnowardhani Astari. "Enhance interval width of crime forecasting with ARIMA model-fuzzy alpha cut." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1193–201. https://doi.org/10.12928/TELKOMNIKA.v17i3.12233.

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With qualified data or information a better decision can be made. The interval width of forecasting is one of data values to assist in the selection decision making process in regards to crime prevention. However, in time series forecasting, especially the use of ARIMA model, the amount of historical data available can affect forecasting result including interval width forecasting value. This study proposes a combination technique, in order to get get a better interval width crime forecasting value. The propose combination technique between ARIMA model and Fuzzy Alpha Cut are presented. The us

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Sawalkar, Meera, Nilesh Rajput, Sakshi MahadikPatil, Tanvi Panhale, Shradha Jadhav, and Akash Pargaonkar. "Spatio-Temporal Crime Predictions." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 1418–22. http://dx.doi.org/10.22214/ijraset.2023.56227.

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Abstract: Crime and transgressions pose a formidable challenge to the principles of justice and require vigilant control. Precise crime prediction and forecasting of future trends hold the potential to significantly bolster urban safety through computational means. The inherent limitations of human capacity to process intricate information from vast datasets impede our ability to achieve early and precise crime prognostication. The accurate estimation of crime rates, categories, and focal points based on historical patterns presents a plethora of computational challenges and opportunities. Not

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Ibrahim, Abdulladeef Abubakar, Yusuf Musa Malgwi, and Yahaya Ali. "A HYBRID MACHINE LEARNING MODEL FOR CRIME RATE PREDICTION." FUDMA JOURNAL OF SCIENCES 8, no. 6 (2024): 101–6. https://doi.org/10.33003/fjs-2024-0806-2789.

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Crime prediction is vital for public safety and resource management. This study developed a hybrid machine learning model integrating Convolutional Neural Networks (CNN) and K-means clustering for crime rate prediction. Historical crime data from Mubi and Yola from the year 2015 to 2023 yielded training and testing accuracies exceeding 90%, surpassing traditional models (Random Forest and Decision Tree Classifiers). Results underscore the effectiveness of CNN and K-means integration in recognizing spatial patterns and clustering data, demonstrating improved predictive accuracy and forecasting

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Xiaomin Li, Lixuan Zhao, Qi Wu, Wei Du, Shangxuan Jiang, Shuo Wen,. "Machine Learning-Based Prediction Methods for Home Burglary Crimes." Journal of Electrical Systems 20, no. 2 (2024): 123–30. http://dx.doi.org/10.52783/jes.1106.

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In today’s rapidly evolving society, as technology continues to advance, various new forms and methods of crime emerge incessantly. It becomes particularly crucial to accurately predict future criminal behaviors. This paper delves into the study of forecasting home burglary crimes in the realm of property-related offenses. Utilizing a dataset of criminal cases, relevant variables with high correlation to crime prediction are selected as features. Through employing diverse machine learning algorithms, the likelihood of the occurrence of home burglary crimes is forecasted. Consequently, a crime

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Баженова, М. Д., та А. Ерланқызы. "ВИКТИМОЛОГИЯ. ҚЫЛМЫСТЫ БОЛЖАУ ЖӘНЕ ЖОСПАРЛАУ". Bulletin of Zhetysu University named after I.Zhansugurov, № 3(100) (27 жовтня 2021): 25–30. https://doi.org/10.53355/zhu.2021.100.3.003.

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Виктимология — криминология бөлімі, қылмыс құрбанының доктринасы, қылмыстық әрекеттің құрбанына айналуы мүмкін жеке немесе топтық құрбандар туралы ғылым. Қылмыс құрбаны туралы ғылым, криминологияның бір бөлімі. Виктимология құрбан болған адамның жеке басын, қылмыскер мен құрбан арасындағы қатынасты, қылмыс пен қылмыскерліктің туындау себебіндегі оның рөлін зерттейді. Виктимология осы негізде қылмыстың құрбанына айналу ықтималдылығын, қылмыс құрбанымен жұмыс істеу тәсілін болжайтын теорияны жасайды, сондай-ақ қылмыс құрбаны болуы мүмкін адамдарды қорғау тәсілі туралы ақпараттар береді. Қылмысты

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Hälterlein, Jens. "Epistemologies of predictive policing: Mathematical social science, social physics and machine learning." Big Data & Society 8, no. 1 (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 impli

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Yarovyi, A. A., O. F. Shevchuk, A. V. Kozlovskyi, Yu M. Panochyshyn, and S. V. Simonchuk. "USING ARIMA MODELS FOR FORECASTING OF OVERALL CRIME RATE IN UKRAINE." Ukrainian Journal of Information Technology 6, no. 2 (2024): 49–56. https://doi.org/10.23939/ujit2024.02.049.

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Crime rate forecasting is a critical element in the development of strategies for sustainable socio-economic growth in a rule-of-law state. Accurate forecasting becomes particularly important in times of economic instability and geopolitical crises, as is the case in Ukraine. This article explores the problem of constructing and applying autoregressive integrated moving average (ARIMA) models to predict the total number of crimes committed in Ukraine. The statistical analysis of the crime time series was conducted using the Python programming language, utilizing specialized libraries such as n

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K, Vijay, and Amal Prakash. "A Systematic Review on Tanpin Kandri Based Crime Prediction." Remittances Review 7, no. 2 (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 t

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Grigorieva, A. Е. "Method of forensic forecasting in crime investigation." Vestnik of North-Eastern Federal University. History. Political Science. Law, no. 4 (January 9, 2024): 12–16. http://dx.doi.org/10.25587/2587-5612-2023-4-12-16.

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Based on the study of conceptual provisions, the most substantiated, from the author’s point of view, approach to formulating the concept of the doctrine of forensic prognostication and its relationship with forensic forecasting is identified and presented. It is noted that these concepts are correlated as “teaching – teaching method”, and the forecasting method, as the main one in forensic prognosis, combines a set of other methods used to form a forecast. The range of objects, subjects, types and methods of forensic forecasting were considered. New approaches to the practice of forensic fore

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Sonali Pakhmode, Vanshita Gaikwad, Bhargavee Talekar, Neha Golatkar, and Vipin Jaiswal. "Regional Crime Data Analysis and Insights Using Fb Prophet." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1395–401. http://dx.doi.org/10.47392/irjaem.2024.0192.

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Crime is one of the most dominate and formidable aspect of the society. Crimes are committed daily, which has made the lives of the common people restive. Therefore, preventing the crime from occurring can be achieved through a systematic approach by recognizing and evaluating the criminal trends specific to a certain location. Data analysis can assist us to explore former criminal incidents and help us analyze the patterns and hidden correlations which can be used for prevention of crime. Our system aims to analyze crime-prone regions by using the location and time of the crime. Data visualiz

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

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

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

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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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V. Joseph Peter, S. Jeya Selvakumari,. "Identification of Crime using Multi Embedding BiLSTM." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 837–44. http://dx.doi.org/10.17762/ijritcc.v11i9.8974.

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Crimes pose significant societal challenges with implications for a nation's well-being, economic progress, and reputation. Precisely measuring crime rates, categories, and hotspots from historical patterns presents various computational complexities and opportunities. This study introduces and improves a deep learning approach for predicting crime types with high precision. The system can predict both crime categories and associated risk levels by analyzing concise summaries from criminal case reports. The predictive model is built on a neural network with LSTM and Bi-LSTM components, demonst

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Hongyon, Siraswaya, and Prabhas Chongstitvatana. "Crime Prediction Through Collaborative Analysis of Proximate Police Stations Data." วารสารงานวิจัยและพัฒนาเชิงประยุกต์ โดยสมาคม ECTI 4, no. 1 (2024): 10–19. http://dx.doi.org/10.37936/ectiard.2024-4-1.252848.

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Crime prediction is a crucial aspect of law enforcement strategies and crime prevention efforts. Machine learning has emerged as a valuable tool in crime prediction, allowing for more accurate and data-driven forecasting. In this study, we focus on forecasting the number of crimes at Pathumwan Police Station in Thailand. Utilizing criminal records from various police stations across Thailand, we employ the K-Means clustering algorithm to group police stations exhibiting similar crime patterns to the Pathumwan Police Station. The clustering results reveal that Wang Thonglang, Nang Loeng, Dusit,

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Stupnyk, Y. V., and O. O. Dolynko. "Analytical forecasting in the field of crime prevention: information tools and methods." Analytical and Comparative Jurisprudence 2, no. 3 (2025): 468–74. https://doi.org/10.24144/2788-6018.2025.03.2.75.

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The article examines the essence and components of information and analytical support for law enforcement agencies in the fight against crime. The methodological approaches and main tasks of information and analytical activities of operational units are revealed. It is determined that proper information support is an integral part of the effective functioning of any social system, including mechanisms for combating criminal manifestations. Special attention is paid to modern trends in the development of information technologies, which are actively integrated into the field of combating crime.

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P. Karthik, P. Jayanth, K. Tharun Nayak, and K. Anil Kumar. "Crime Prediction Using Machine Learning and Deep Learning." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 08–15. http://dx.doi.org/10.32628/ijsrset241134.

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The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and e

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Laskowska, Katarzyna. "The theoretical Basis for Criminological forecasting of Crime (with Russia Example)." Internal Security 10, no. 1 (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 p

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Bitanov, A. "FORECASTING THE NUMBER OF CORRUPTION CRIMES IN KAZAKHSTAN: A MACHINE LEARNING APPROACH." Herald of the Kazakh-British technical university 22, no. 1 (2025): 84–93. https://doi.org/10.55452/1998-6688-2025-22-1-84-93.

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This study aims to predict the number of corruption crimes in Kazakhstan using machine learning methods. The research is based on official monthly crime statistics collected from the Legal Statistics Portal, specifically the Report Form No. 3-K, which records corruption-related offenses since 2016 [3]. Three regression models were applied: k-Nearest Neighbors (kNN), Extreme Gradient Boosting (XGBoost), and Linear Regression. Model performance was assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) score. The findings indicate that Linear Regression achieved t

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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 (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

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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 (2021): 1052–60. https://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 for the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information an

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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 p

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Redoblo, Cristine V., Jose Leo G. Redoblo, Rene A. Salmingo, Charwin M. Padilla, and Jan Carlo T. Arroyo. "Forecasting the influx of crime cases using seasonal autoregressive integrated moving average model." International Journal of ADVANCED AND APPLIED SCIENCES 10, no. 8 (2023): 158–65. http://dx.doi.org/10.21833/ijaas.2023.08.018.

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Crime constitutes a profound challenge to the societal fabric of a nation and often finds its roots in factors such as avarice, destitution, and economic adversity. This study endeavors to proactively address the issue of crime through the employment of a crime forecasting model, aimed at uncovering latent correlations and underlying patterns. Specifically, it employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to project the future incidence of criminal cases. The research objectives encompass forecasting crime case numbers through time series analysis, appraising the

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Tobias, Chitani Jarves Bob, and Brave Mwanza. "Spatial and Temporal Analysis: A GIS-Based Application Tool for Crime Monitoring and Clustering in Malawi." European Scientific Journal, ESJ 20, no. 8 (2024): 167. http://dx.doi.org/10.19044/esj.2024.v20n8p167.

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For the purposes of monitoring, evaluating, and conducting a geographical analysis of crime-related data, the study used geospatial technology to collect crime data based on spatial location and the Malawi Police Data Digest of 2019 and 2020. In a more generic sense, knowing the geographic patterns of crime in Malawi using GIS technology can help determine how to make and implement important decisions to reduce crimes in Malawi. The Malawi Police Service has established a number of database management systems to help with crime monitoring. Notwithstanding, it has not yet fully integrated Geogr

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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

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

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ILYIN, ILYA. "DYNAMIC ASPECT OF FORECASTING DEMONSTRATIVE-PROTEST CRIME." ECONOMIC PROBLEMS AND LEGAL PRACTICE 19, no. 1 (2023): 221–25. http://dx.doi.org/10.33693/2541-8025-2023-19-1-221-225.

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When using demonstrative-protest crime, the author realizes the possibilities of the theory of cycles for the knowledge of its essence. The concept of «cycle» in this case can be used in two aspects: dynamic and structural. This article changes the dynamics of demonstrative-protest crime, observed by the cycles of protest activity, which in turn depends on the cycles of political activity. In the conditions of stable development of society, these cycles are formally set by constitutional provisions on the turnover of government bodies, which are carried out by elections. It is no coincidence t

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