Relevant bibliographies by topics / Crime forecasting

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Crime forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "Crime forecasting"

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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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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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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 (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 (2003): 551–55. http://dx.doi.org/10.1016/s0169-2070(03)00089-x.

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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 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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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 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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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 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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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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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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Dissertations / Theses on the topic "Crime forecasting"

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Chukwu, Idam Oko. "Public expenditures and crime in a free society." CSUSB ScholarWorks, 1999. https://scholarworks.lib.csusb.edu/etd-project/1802.

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Cohn, Ellen Gail. "The effects of weather and temporal variables on calls for police service." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386413.

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Breetzke, Gregory Dennis. "Geo-analysis of offenders in Tshwane: towards an urban ecological theory of crime in South Africa /." Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-01062009-141141/.

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Alshalan, Abdullah. "Cyber-crime fear and victimization." Diss., Mississippi State : Mississippi State University, 2006. http://library.msstate.edu/etd/show.asp?etd=etd-01232006-095728.

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Воскобойнік, І. І. "Статистичний аналіз та прогнозування рівня злочинності в Україні". Thesis, Одеський національний економічний університет, 2020. http://dspace.oneu.edu.ua/jspui/handle/123456789/12542.

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У роботі розглядаються теоретичні та методологічні питання щодо статистичної оцінки правопорушень та злочинності в Україні. Проаналізовано динаміку рівня злочинності в Україні та проаналізована структура злочинів. Проведено аналіз регіональних особливостей розповсюдженості злочинності в Україні. Проведено аналіз тенденції розвитку та рівня злочинності в Україні.<br>The paper considers theoretical and methodological issues related to statistical assessment of offenses and crime in Ukraine. The dynamics of the crime rate in Ukraine is analyzed and the structure of crimes is analyzed. The analysis of regional features of crime prevalence in Ukraine is carried out. An analysis of development trends and crime rates in Ukraine.
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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.
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Cicconi, Claudia. "Essays on macroeconometrics and short-term forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209660.

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The thesis, entitled "Essays on macroeconometrics and short-term forecasting",<p>is composed of three chapters. The first two chapters are on nowcasting,<p>a topic that has received an increasing attention both among practitioners and<p>the academics especially in conjunction and in the aftermath of the 2008-2009<p>economic crisis. At the heart of the two chapters is the idea of exploiting the<p>information from data published at a higher frequency for obtaining early estimates<p>of the macroeconomic variable of interest. The models used to compute<p>the nowcasts are dynamic models conceived for handling in an efficient way<p>the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases<p>the time series have in general missing data at the end of the sample. While<p>the first chapter uses a small model like a VAR for nowcasting Italian GDP,<p>the second one makes use of a dynamic factor model, more suitable to handle<p>medium-large data sets, for providing early estimates of the employment in<p>the euro area. The third chapter develops a topic only marginally touched<p>by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.<p>The firrst chapter assesses the accuracy of the Italian GDP nowcasts based<p>on a small information set consisting of GDP itself, the industrial production<p>index and the Economic Sentiment Indicator. The task is carried out by using<p>real-time vintages of data in an out-of-sample exercise over rolling windows<p>of data. Beside using real-time data, the real-time setting of the exercise is<p>also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector<p>Autoregressive (VAR) model, cast in state-space form and estimated by<p>maximum likelihood. The results show that the model can provide quite accurate<p>early estimates of the Italian GDP growth rates not only with respect<p>to a naive benchmark but also with respect to a bridge model based on the<p>same information set and a mixed-frequency VAR with only GDP and the industrial production index.<p>The chapter also analyzes with some attention the role of the Economic Sentiment<p>Indicator, and of soft information in general. The comparison of our<p>mixed-frequency VAR with one with only GDP and the industrial production<p>index clearly shows that using soft information helps obtaining more accurate<p>early estimates. Evidence is also found that the advantage from using soft<p>information goes beyond its timeliness.<p>In the second chapter we focus on nowcasting the quarterly national account<p>employment of the euro area making use of both country-specific and<p>area wide information. The relevance of anticipating Eurostat estimates of<p>employment rests on the fact that, despite it represents an important macroeconomic<p>variable, euro area employment is measured at a relatively low frequency<p>(quarterly) and published with a considerable delay (approximately<p>two months and a half). Obtaining an early estimate of this variable is possible<p>thanks to the fact that several Member States publish employment data and<p>employment-related statistics in advance with respect to the Eurostat release<p>of the euro area employment. Data availability represents, nevertheless, a<p>major limit as country-level time series are in general non homogeneous, have<p>different starting periods and, in some cases, are very short. We construct a<p>data set of monthly and quarterly time series consisting of both aggregate and<p>country-level data on Quarterly National Account employment, employment<p>expectations from business surveys and Labour Force Survey employment and<p>unemployment. In order to perform a real time out-of-sample exercise simulating<p>the (pseudo) real-time availability of the data, we construct an artificial<p>calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic<p>factor model allowing for mixed-frequency data, missing data at the beginning<p>of the sample and ragged edges typical of non synchronous data releases. Our<p>results show that using country-specific information as soon as it is available<p>allows to obtain reasonably accurate estimates of the employment of the euro<p>area about fifteen days before the end of the quarter.<p>We also look at the nowcasts of employment of the four largest Member<p>States. We find that (with the exception of France) augmenting the dynamic<p>factor model with country-specific factors provides better results than those<p>obtained with the model without country-specific factors.<p>The third chapter of the thesis deals with dynamic factor models on data<p>characterized by local cross-correlation due to the presence of block-structures.<p>The latter is modeled by introducing block-specific factors, i.e. factors that<p>are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo<p>simulations to evaluate the effects of modeling or not the block-structure on<p>the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help<p>reducing the model miss-specification and getting more accurate estimates<p>of the common factors; second, that imposing a wrong block-structure or<p>imposing a block-structure when it is not present does not have negative<p>effects on the estimates of the common factors. These two results allow us<p>to conclude that it is always recommendable to model the block-structure<p>especially if the characteristics of the data suggest that there is one.<br>Doctorat en Sciences économiques et de gestion<br>info:eu-repo/semantics/nonPublished
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Barbosa, Guilherme Valentim. "Análise de bolhas imobiliárias ao redor do mundo." reponame:Repositório Institucional do FGV, 2018. http://hdl.handle.net/10438/24764.

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Submitted by Guilherme Valentim Barbosa (guilhermevalentim@gmail.com) on 2018-09-18T02:06:09Z No. of bitstreams: 1 Guilherme Valentim Barbosa - Dissertação - 20180917.pdf: 947230 bytes, checksum: a6fd42e0aefd410304cfa0f0ba723d7b (MD5)<br>Approved for entry into archive by Joana Martorini (joana.martorini@fgv.br) on 2018-09-18T14:55:42Z (GMT) No. of bitstreams: 1 Guilherme Valentim Barbosa - Dissertação - 20180917.pdf: 947230 bytes, checksum: a6fd42e0aefd410304cfa0f0ba723d7b (MD5)<br>Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-09-19T13:06:39Z (GMT) No. of bitstreams: 1 Guilherme Valentim Barbosa - Dissertação - 20180917.pdf: 947230 bytes, checksum: a6fd42e0aefd410304cfa0f0ba723d7b (MD5)<br>Made available in DSpace on 2018-09-19T13:06:39Z (GMT). No. of bitstreams: 1 Guilherme Valentim Barbosa - Dissertação - 20180917.pdf: 947230 bytes, checksum: a6fd42e0aefd410304cfa0f0ba723d7b (MD5) Previous issue date: 2018-08-10<br>Este trabalho busca analisar empiricamente a existência de bolhas imobiliárias ao redor do mundo e identificar quando esses comportamentos explosivos no preço dos imóveis ocorreram. Os resultados foram obtidos por meio de uma metodologia recursiva de testes de raiz unitária, os testes SADF e GSADF propostos por Phillips e co-autores. Foram coletados dados de preços de imóveis para 28 países e seus respectivos índices de preços ao consumidor. Os resultados obtidos apontaram a existência de comportamentos explosivos em aproximadamente 90% das séries analisadas.<br>This study aims to empirically analyze the existence of real estate bubbles around the world and to identify when these explosive behavior in real estate prices occurred. The results were obtained through a recursive methodology of unit root tests, the SADF and GSADF tests proposed by Phillips and co-authors. Real estate price data were collected for 28 countries and their respective consumer price indexes. The results obtained indicate the existence of explosive behavior in about 90% of the analyzed series.
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Singh, Prakash. "Time series forecasting on crime data in Amsterdam for a software company." Master's thesis, 2018. http://hdl.handle.net/10362/57826.

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Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>In recent years, there have been many discussions of data mining technology implementation in the fight against terrorism and crime. Sentient as a software company has been supporting the police for years by applying data mining techniques in the DataDetective application (Sentient, 2017). Experimenting with various types of predictive model solutions, selecting the most efficient and promising solution are the objectives of this internship. Initially, extended literatures were reviewed in the field of data mining, crime analysis and crime data mining. Sentient provided 7 years of crime data which was aggregated on daily basis to create a univariate dataset. Also, an incidence type daily aggregation was done to create a multivariate dataset. The prediction length for each solution was 7 days. The experiments were divided into two major categories: Statistical models and neural network models. Neural networks outperformed statistical models for the crime data. This paper provides the overview of statistical models and neural network models used. A comparative study of all the models on similar dataset gives a clear picture of their performance on available data and generalization capability. Evidently, the experiments showed that Gated Recurrent units (GRU) produced better prediction in comparison to other models. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in DataDetective.
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Miles, Thomas John. "Three empirical essays in the economics of crime /." 2000. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:9965120.

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Books on the topic "Crime forecasting"

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English, Kim. Measuring crime rates of prisoners. Colorado Dept. of Public Safety, Division of Criminal Justice, Office of Research and Statistics, 1992.

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Babachinaitė, G. Nusikalstamumas Lietuvoje ir jo prognozė iki 2015 m.: Monografija. Mykolo Romerio universiteto Leidybos centras, 2008.

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Vserossiĭskiĭ nauchno-issledovatelʹskiĭ institut (Russia (Federation). Ministerstvo vnutrennikh del), ed. Sostoi͡a︡nie prestupnosti v Rossiĭskoĭ Federat͡s︡ii i ee dolgosrochnyĭ prognoz. Ministerstvo vnutrennikh del Rossiĭskoĭ Federat͡s︡ii. Vserossiĭskiĭ nauchno-issl. institut, 1998., 1998.

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Chalka, Robert. Prognóza vývoja kriminality v Slovenskej republike. Akadémia policajného zboru v Bratislave, 2000.

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Beken, T. vander. European organised crime scenarios for 2015. Maklu, 2006.

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Sheilagh, Hodgins, ed. Mental disorder and crime. Sage Publications, 1993.

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Deadman, Derek. Forecasting recorded property crime using a time-series econometric model. Public Sector Economics Research Centre, 1995.

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Muraskin, Roslyn. Visions for change: Crime and justice in the twenty-first century. 4th ed. Prentice Hall, 2005.

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Babaev, Mikhail Matveevich, and V. A. Kikotʹ. Kriminogennai︠a︡ situat︠s︡ii︠a︡ v Rossiĭskoĭ Federat︠s︡ii v nachale XXI veka. VNII MVD RF, 2001.

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Smirnyĭ, A. M. Tendent︠s︡ii prestupnosti v Rossii v nachale XXI veka. VNII MVD Rossii, 2002.

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Book chapters on the topic "Crime forecasting"

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Gottschalk, Petter. "Forecasting Crime Occurrence." In Trusted White-Collar Offenders. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73862-4_10.

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Johnson, Shane D., and Kate J. Bowers. "Near Repeats and Crime Forecasting." In Encyclopedia of Criminology and Criminal Justice. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-5690-2_210.

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Faujdar, Neetu, Yashita Verma, Yogesh Singh Rathore, and P. K. Rohatgi. "Crime Forecasting Using Time Series Analysis." In Advances in Interdisciplinary Research in Engineering and Business Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0037-1_20.

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Mu, Yang, Wei Ding, Melissa Morabito, and Dacheng Tao. "Empirical Discriminative Tensor Analysis for Crime Forecasting." In Knowledge Science, Engineering and Management. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25975-3_26.

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Beken, Tom Vander. "Risk, Uncertainty and the Assessment of Organised Crime." In Forecasting, Warning and Responding to Transnational Risks. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230316911_6.

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Devi, J. Vimala, and K. S. Kavitha. "Time Series Analysis and Forecasting on Crime Data." In Algorithms for Intelligent Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6332-1_26.

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Yu, Chung-Hsien, Wei Ding, Ping Chen, and Melissa Morabito. "Crime Forecasting Using Spatio-temporal Pattern with Ensemble Learning." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06605-9_15.

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Piraján, Freddy, Andrey Fajardo, and Miguel Melgarejo. "Towards a Deep Learning Approach for Urban Crime Forecasting." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31019-6_16.

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Muthamizharasan, M., and R. Ponnusamy. "Forecasting Crime Event Rate with a CNN-LSTM Model." In Innovative Data Communication Technologies and Application. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7167-8_33.

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Hu, Xiaochen, Xudong Zhang, and Nicholas P. Lovrich. "Forecasting Identity Theft Victims: Analyzing Characteristics and Preventive Actions through Machine Learning Approaches." In The New Technology of Financial Crime. Routledge, 2022. http://dx.doi.org/10.4324/9781003258100-10.

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Conference papers on the topic "Crime forecasting"

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Borowik, Grzegorz, Zbigniew M. Wawrzyniak, and Pawel Cichosz. "Time series analysis for crime forecasting." In 2018 26th International Conference on Systems Engineering (ICSEng). IEEE, 2018. http://dx.doi.org/10.1109/icseng.2018.8638179.

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Yu, Chung-Hsien, Max W. Ward, Melissa Morabito, and Wei Ding. "Crime Forecasting Using Data Mining Techniques." In 2011 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2011. http://dx.doi.org/10.1109/icdmw.2011.56.

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Chen, Peng, Hongyong Yuan, and Xueming Shu. "Forecasting Crime Using the ARIMA Model." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.222.

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Bogucki, Robert, Jan Kanty Milczek, and Patryk Miziuła. "A simple crime hotspot forecasting algorithm." In 2020 Federated Conference on Computer Science and Information Systems. IEEE, 2020. http://dx.doi.org/10.15439/2020f5.

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Mitchell, Mark B., Donald E. Brown, and James H. Conklin. "A Crime Forecasting Tool for the Web-Based Crime Analysis Toolkit." In 2007 IEEE Systems and Information Engineering Design Symposium. IEEE, 2007. http://dx.doi.org/10.1109/sieds.2007.4373988.

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Marzan, Charlie S., Maria Jeseca C. Baculo, Remedios de Dios Bulos, and Conrado Ruiz. "Time Series Analysis and Crime Pattern Forecasting of City Crime Data." In the International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3127942.3127959.

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Retnowardhani, Astari, and Yaya Sudarya Triana. "Classify interval range of crime forecasting for crime prevention decision making." In 2016 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS). IEEE, 2016. http://dx.doi.org/10.1109/kicss.2016.7951409.

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Zhuo, Eugenia R., and Jake Libed. "Analysis of Crime Rates in Rizal Province using Crime Forecasting Models." In ICCMB 2020: 2020 The 3rd International Conference on Computers in Management and Business. ACM, 2020. http://dx.doi.org/10.1145/3383845.3383884.

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Wawrzyniak, Zbigniew M., Radoslaw Pytlak, Paweł Cichosz, et al. "The data-based methodology for crime forecasting." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2020, edited by Ryszard S. Romaniuk and Maciej Linczuk. SPIE, 2020. http://dx.doi.org/10.1117/12.2583580.

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M, Dhanush, Hasifa A. S, and Merin Meleet. "Crime Prediction and Forecasting using Voting Classifier." In 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2021. http://dx.doi.org/10.1109/icecct52121.2021.9616911.

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