Characterizing the Temporal Regularities of Crime

There are a lot of studies that show that criminal activities exhibit certain temporal and spatial regularities. However, they often focus on either specific cities or types of crime and cannot clearly explain the patterns for the crime. What are the temporal patterns at the microlevel spatial scale? How general? Understanding the regularities of urban crime is important because it can help us improve the economy and safety of the cities and maintain harmony. This study analyzes the theft and burglary crime data from five cities in the United States. We successfully find the spatiotemporal patterns of two types of crime in different time series across cities.

Enabling Collaboration and Communication Across Law Enforcement Jurisdictions: Data Sharing in a Multiagency Partnership

Offenders do not always operate within jurisdictional boundaries and, as such, neighboring law enforcement agencies can benefit from sharing crime data and other investigation-related information with one another, with the shared goal of reducing crime throughout their region. In 2016, one such partnership was formed with seven law enforcement agencies, the District Attorney’s Office, and public health officials in King County, Washington. As part of a larger evaluation of this regional collaboration, the authors assessed the data and intelligence-sharing behaviors of key personnel from each participating agency over an 18-month period. This was done through a series of interviews with key personnel and the use of social network analysis. Results suggest that, although data-sharing networks increased in size and project personnel were able to identify benefits to sharing crime data with one another (e.g., seeing the “bigger picture” regarding crime in their region, using shared crime data to track and combat violent crime), they also identified a number of obstacles associated with cross-jurisdictional data sharing. Findings from this evaluation contribute to the collective understanding and implementation of a regional approach to crime control. If criminal justice agencies plan to work together to reduce crime, data and information sharing are essential. Therefore, it is imperative that agencies are aware of the positive outcomes associated with regional data sharing and the challenges that can arise throughout this collaborative effort.

Crime Data Forecasting Using Machine Learning and Big Data Analytics

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.”

Using Crime Data to Assess Longitudinal Relationships Between Community Violent Crime and Aggressive Behavior Among At-Risk Youth

The goal of this study was to assess longitudinal, predictive relationships between community violent crime and reactive and proactive aggression. Community violent crime data were gathered from local law enforcement agencies and combined with an existing dataset of at-risk youth. Aggression was assessed by parents using the Reactive and Proactive Aggression Questionnaire (RPQ). Data were examined over four time points. Autoregressive cross-lagged modeling was used to test two models: one for proactive aggression and one for reactive aggression. Results revealed a positive relationship between community violent crime and proactive aggression, whereas the model including reactive aggression had poor model fit. Therefore, results support reactive and proactive aggression as distinct constructs. Findings also demonstrate that publicly accessible violent crime data can be used to predict children’s behavior over time. Finally, results have important implications for preventive interventions for at-risk youth.

Estimating and predicting the temporal information of apartment burglaries that possess imprecise time stamps: A comparative study using eight different temporal approximation methods in Vienna, Austria

This research compares and evaluates different approaches to approximate offense times of crimes. It contributes to and extends all previously proposed naïve and aoristic temporal approximation methods and one recent study [1] that showed that the addition of historical crimes with accurately known time stamps to temporal approximation methods can outperform all traditional approximation methods. It is paramount to work with crime data that possess precise temporal information to conduct reliable (spatiotemporal) analysis and modeling. This study contributes to and extends existing studies on temporal analysis. One novel and one relatively new temporal approximation methods are introduced that rely on weighting aoristic scores with historic offenses with exactly known offense times. It is hypothesized that these methods enhance the accuracy of the temporal approximation. In total, eight different methods are evaluated for apartment burglaries in Vienna, Austria, for yearly and seasonal differences. Results show that the one novel and one relatively new method applied in this research outperform all other existing approximation methods to estimate and predict offense times. These two methods are particularly useful for both researchers and practitioners, who often work with temporally imprecise crime data.

Crime Pattern Analysis, Visualization and Pridiction Using Data Mining

Crime against women these days has become problem of every nation around the globe many countries are trying to curb this problem. Preventive are taken to reduce the increasing number of cases of crime against women. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analyzing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyze crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyze, visualize and predict crime using crime data set. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. We group data objects using clustering technique. Dataset is classified on the basis of some predefined condition. Here grouping is done according to various types of crimes against women taking place in different states and cities of India. Crime mapping will help the administration to plan strategies for prevention of crime, further using data mining technique data can be predicted and visualized in various form in order to provide better understanding of crime patterns.

Route Generation Heuristics for the Automated Creation of Hot Spot Policing Patrol Routes

Hot spot policing is a form of targeted police patrol deployment for decreasing crime. For hot spot policing to be effective, it requires analysis of crime data to identify the specific locations where crime is concentrated and create suitable patrol routes. The creation of hot spot policing patrol routes is a manual task that police officers perform, requiring skills and knowledge about hot spot policing and crime pattern analysis. This can limit the use of hot spot policing where these skills and knowledge are not available, and where they are available, the creation of patrol routes can be a time-consuming task. In this paper, we introduce two computational route generation heuristics that automate creating hot spot policing patrol routes. Both approaches identify the specific locations where crime concentrates and then use different methods to create the patrol routes. We compare the performance of each approach using metrics associated with effective patrol route creation and through visual inspection. We conclude that the heuristics we introduce provide an accurate means for creating hot spot policing patrol routes, which can support greater and improved use of hot spot policing as an effective type of intervention for decreasing crime.

Mining the crime data using naïve Bayes model

<p>A massive number of documents on crime has been handled by police departments worldwide and today's criminals are becoming technologically elegant. One obstacle faced by law enforcement is the complexity of processing voluminous crime data. Approximately 439 crimes have been registered in sanchez mira municipality in the past seven years. Police officers have no clear view as to the pattern crimes in the municipality, peak hours, months of the commission and the location where the crimes are concentrated. The naïve Bayes modelis a classification algorithm using the Rapid miner auto model which is used and analyze the crime data set. This approach helps to recognize crime trends and of which, most of the crimes committed were a violation of special penal laws. The month of May has the highest for index and non-index crimes and Tuesday as for the day of crimes. Hotspots were barangay centro 1 for non-index crimes and barangay centro 2 for index crimes. Most non-index crimes committed were violations of special law and for index crime rape recorded the highest crime and usually occurs at 2 o’clock in the afternoon. The crime outcome takes various decisions to maximize the efficacy of crime solutions.</p>

Quantile-Based Estimation of Liu Parameter in the Linear Regression Model: Applications to Portland Cement and US Crime Data

In multiple linear regression models, the multicollinearity problem mostly occurs when the explanatory variables are correlated among each other. It is well known that when the multicollinearity exists, the variance of the ordinary least square estimator is unstable. As a remedy, Liu in [1] developed a new method of estimation with biasing parameter d. In this paper, we have introduced a new method to estimate the biasing parameter in order to mitigate the problem of multicollinearity. The proposed method provides the class of estimators that are based on quantile of the regression coefficients. The performance of the new estimators is compared with the existing estimators through Monte Carlo simulation, where mean squared error and mean absolute error are considered as evaluation criteria of the estimators. Portland cement and US Crime data is used as an application to illustrate the benefit of the new estimators. Based on simulation and numerical study, it is concluded that the new estimators outperform the existing estimators in certain situations including high and severe cases of multicollinearity. 95% mean prediction interval of all the estimators is also computed for the Portland cement data. We recommend the use of new method to practitioners when the problem of high multicollinearity exists among the explanatory variables.