Analysis Of Crime Data Research Articles

Crime Risk Maps: A Multivariate Spatial Analysis of Crime Data

In crime analyses, maps showing the degree of risk help police departments to make decisions on operational matters, such as where to patrol or how to deploy police officers. This study statistically models spatial crime data for multiple crime types in order to produce joint crime risk maps. To effectively model and map the spatial crime data, we consider two important characteristics of crime occurrences: the spatial dependence between sites, and the dependence between multiple crime types. We reflect both characteristics in the model simultaneously using a generalized multivariate conditional autoregressive model. As a real‐data application, we examine the number of incidents of vehicle theft, larceny, and burglary in 83 census tracts of San Francisco in 2010. Then, we employ a Bayesian approach using a Markov chain Monte Carlo method to estimate the model parameters. Based on the results, we detect the crime hotspots, thus demonstrating the advantage of using a multivariate spatial analysis for crime data.

A Proposed Bi-layer Crime Prevention Framework Using Big Data Analytics

The future of science and technology sounds very promising. The need to adopt new technologies while navigating towards industry 4.0 has changed the perceptions of law enforcement agency to contend against criminal minds. It is sad but true that the conventional crime prevention system followed by government agencies is not effective for long-term implications. With advanced technologies that constantly generate and exchange data, big data analytics can be applied to predict and prevent crime from happening. However, dealing with the overwhelming amount of complex and heterogeneous crime-related data is never an easy task. Additionally, there are many data analytical techniques and each of them has its own strengths and weaknesses. In order to identify the most efficient techniques, recent literature is reviewed to spotlight the trend as well as to shed light on the research gaps and challenges in various areas. The areas include crime data collection and preprocessing, crime data analysis, crime prediction and crime prevention. These techniques are further analyzed by considering the advantages and disadvantages which then provides insight to propose a bi-layer crime prevention framework. The first layer intends to support the law enforcement agency’s daily operation while the second layer serves as a countermeasure for first layer. Both layers aim to reduce the crime rate by involving law enforcement agency through the utilization of various big data sources and techniques effectively. The proposed crime prevention framework will progressively collect data to deter criminal behavior for city’s environmental design. Ultimately, a safe and secure city is molded in the near future.

About Discursive Storylines and Techno-Fixes: The Political Framing of the Implementation of Predictive Policing in Germany

One important reason for the current rise of predictive policing in Germany is the recent boost in the development of digital technologies and the associated possibility of analysing huge data sets at relatively low cost by utilising mathematically deduced algorithms. Economic motives also play a vital role in the implementation process, as it is hoped that policing can be organised more rationally by the more effective allocation of police patrols. However, in addition to technical and economic factors, the rise of predictive policing in Germany is above all a political phenomenon, involving the discursive shaping of domestic burglary as a security problem. Furthermore, the ways, how the technologies utilised for predictive crime data analysis are discursively referred to, play a vital role in this discourse. These new prediction tools facilitate rhetorical links for politicians and police authorities in legitimising their ambitions to fight crime and enhance public security, presenting their methods as innovative and effective and making these technologies important components of corresponding security discourses.

Cyber Intelligence and OSINT: Developing Mitigation Techniques Against Cybercrime Threats on Social Media

Open Source Intelligence (OSINT) involve the collection or processes of gathering data and profiling of publicly available private and public sector information sources about individuals and business intelligence purposes. These sources includes internet and other social media platforms such as Facebook, emails, twitters, what’s apps for. Much debate and research has been done on the threats, vulnerabilities and the impact of the use of social media sites but this study is to minimize bias. Objective: To systematic review and synthesis findings on current empirical research topic on cyber intelligence and open source intelligence profiling to identifying both the threats and vulnerabilities on online social networks for mitigation purposes. Methods: A systematic narrative review of research using rigorous searching on online databases. The results were then subjected to review using a quantitative and quality appraisal tool and a narrative synthesis methodology. A theoretical framework was developed for the synthesis using concepts from the literature ‘The Effectiveness of Neighborhood Watch’. A Campbell Systematic Review Results: The systematic search retrieved 18 original research papers investigating and exploring the effects of online social media technologies on open source intelligence concepts. The use of social media were reported as enhancing social cohesion among peers, improving business opportunities as information gets to customers quickly. Safe identity experimentations, OSINT and cyber intelligence social media gathering is especially vital in the modern war on terror. Understanding terrorist network topologies, crime data analysis and mining, countering improvised explosive devices. The study also highlighted potential negative impacts and threats and the effect of social engineering threats in SNSs, threats of social networking and identity crime. Vulnerabilities of HTTP header information and cookies being sent to third-party aggregators as well harmful effects of exposure to threats. Conclusion: The systematic review has revealed extraordinary evidences and contradictory concepts. It has also revealed the underlining research challenges impacting on open source intelligence. Due to the invincibility nature of social media technologies, social media platforms are constantly being used for social, business and intelligence gathering purposes but to ensure proper and advance mitigating circumstance, further research is required to gain situational awareness and appropriate counter measure.

Analysis of Crime Data in the Limpopo Province

South Africa has a very high rate of murders, assaults, rapes and other crimes compared to most countries. Most South Africans who immigrate to other countries cite crime as the major reason. Crime has become a concern for all, the police, private security industry, real estate developer, car manufacturers, businessmen, etc. There is a high demand for crime prevention; and this calls for a continuous use of new, advanced and reliable methods to prevent crime. How bad is the level of crime in Limpopo and what are the major crime types? This study uses secondary data from the 2011 Census conducted by Statistics South Africa; and tries to look at the composition of crime in the province and the variables that influence crime, in order to propose measures to tackle and minimize crime in the province. Multivariate statistical analysis has been employed, and the study shows that the following variables; the population size, number of households, youth unemployment, growth rate, and dependency ratio; have a positive influence in the occurrence of crime. The study recommends the slowing down of population growth rate, decrease in household size and the reduction of youth unemployment to curb crime in the province.

Analysis of Crime Data in the Limpopo Province

South Africa has a very high rate of murders, assaults, rapes and other crimes compared to most countries. Most South Africans who immigrate to other countries cite crime as the major reason. Crime has become a concern for all, the police, private security industry, real estate developer, car manufacturers, businessmen, etc. There is a high demand for crime prevention; and this calls for a continuous use of new, advanced and reliable methods to prevent crime. How bad is the level of crime in Limpopo and what are the major crime types? This study uses secondary data from the 2011 Census conducted by Statistics South Africa; and tries to look at the composition of crime in the province and the variables that influence crime, in order to propose measures to tackle and minimize crime in the province. Multivariate statistical analysis has been employed, and the study shows that the following variables; the population size, number of households, youth unemployment, growth rate, and dependency ratio; have a positive influence in the occurrence of crime. The study recommends the slowing down of population growth rate, decrease in household size and the reduction of youth unemployment to curb crime in the province.

Spatiotemporal Convergence of Crime and Vehicle Crash Hotspots: Additional Consideration for Policing Places

Policing strategies that seek to simultaneously combat crime and vehicle crashes operate under the assumption that these two problems have a corollary relationship—an assumption that has received scant empirical attention and is the focus of the present study. Geocoded vehicle crash, violent crime, and property crime totals across were aggregated to Indianapolis census blocks over a 36-month period (2011-2013). Time series negative binomial regression and local indicators of spatial autocorrelation analyses were conducted. Results indicate that both violent and property crime are significantly related to vehicle crash counts, both overall and during the temporal confines of patrol tours. Relationship strength was modest. Spatiotemporal analysis of crime and crash data can identify places for police intervention and improved scholarly evaluation.

Principal Component Analysis of Crime Data in Gwagwalada Area Command, Abuja from 1995 – 2015

This paper analyses Abuja crime data which consists of the averages of twenty major crimes reported to the police for the period 1995 – 2015. Correlation analysis and principal component analysis (PCA) were employed to explain the correlation between the crimes and to determine the distribution of the crimes over the three Area Councils under the Gwagwalada Area Command. The result has shown a significant correlation between robbery and rape, grievous hurt and wound (GHW), theft, assault, murder and unlawful escape. Gwagwalada Area Council has the highest overall crime rate i.e. Rape, Robbery, GHW, Theft, Assault, Murder and unlawful escape in the Area Command. Unlawful possession, breach of peace and broken store are more prevalence in Kwali Area Council while Vehicle theft, car stealing and burglary are more prevalence in Kuje Area Council Area. The PCA has suggested retaining three components (Rape, Robbery and GHW) that explain about 86.873 percent of the total variability of the data set.

Impact of crime on spatial analysis of house prices: evidence from a UK city

Purpose There is a lack of understanding in the literature on the spatial relationships between crime and house price. This paper aims to test the impact of spatial effects in the housing market, how these are related to the incidence of crime and whether effects vary by the type of crime. Design/methodology/approach The analysis initially explores univariate and bivariate spatial patterns in crime and house price data for the Belfast Metropolitan Area using Moran’s I and Local Indicator Spatial Association (LISA) models, and secondly uses spatial autoregression models to estimate the role of crime on house prices. A spatially weighted two-stage least-squares model is specified to analyse the joint impact of crime variables. The analysis is cross sectional, based on a panel of data. Findings The paper illustrates that the pricing impact of crime is complex and varies by type of crime, property type and location. It is shown that burglary and theft are associated with higher-income neighbourhoods, whereas violence against persons, criminal damage and drugs offences are mainly associated with lower-priced neighbourhoods. Spatial error effects are reduced in models based on specific crime variables. Originality/value The originality of this paper is the application of spatial analysis in the study of the impact of crime upon house prices. Criticisms of hedonic price models are based on unexplained error effects; the significance of this paper is the reduction of spatial error effects achievable through the analysis of crime data.

Correcting crime rates for touristic centers based on the number of guests

Incorporating data on the number of tourists, the author attempts to conduct an objective analysis of crime data in touristic centers in Hungary.

Visualization and spatial analysis of police open data as a part of community policing in the city of Pardubice (Czech Republic)

ABSTRACTDifferent types of spatial analyses and visualizations can be used in the police practice for investigation, crime prediction, and planning of police forces. The public availability of crime data is one of the often discussed issues for the police, general public and academia. The efforts to open police data are rooted in the philosophy of the so-called ‘community policing’. In this article, we demonstrate the possibilities of spatial analysis and cartographic visualization of open crime data. We provide two use cases based on the data gathered by the municipal police in Pardubice, Czech Republic. We investigate the impact of gambling sites on crime offence intensity and found that gambling sites considerably influence their surroundings within 100 m. The other use case is focused on traffic offences caused by cyclists. We extracted hot spots of these offences and tried to identify their causation, since the police should not only carry out repressive measures, but also strive to eliminate the causes (e.g. add cycle lanes, bike paths, underpasses or overpasses). Different types of cartographic visualization have been designed and discussed for both use cases. The advantages, limitations and future development of the described concepts are commented on in the conclusion.

Crime Data Analysis Using Pig with Hadoop

Big data is the voluminous and complex collection of data that comes from different sources such as sensors, content posted on social media website, sale purchase transaction etc. Such voluminous data becomes tough to process using ancient processing application. There are various tools and techniques in the market for big data analytics. With continually increasing population, crimes and crime rate analyzing related data is a huge issue for governments to make strategic decisions so as to maintain law and order. This is really necessary to keep the citizens of the country safe from crimes. The best place to look up to find room for improvement is the voluminous raw data that is generated on a regular basis from various sources by applying Big Data Analytics (BDA) which helps to analyze certain trends that must be discovered, so that law and order can be maintained properly and there is a sense of safety and well-being among the citizens of the country.

지하철역사 내 성폭력범죄 발생에 영향을 주는 건축계획특성 연구

The purpose of this study was to deduce implications for the improvement in subway station for crime prevention based on the comprehensive analysis of the results of crime data analysis, expert interview, and computer simulation. To deduce characteristics of architectural design affecting the risk of sex offenses, passenger data, architectural documents, and crime data of seventeen non-transfer stations which have similar circumstance characteristics were investigated. And for simulating how passengers move from platform to ticket gate in stations, the Simulex was used. The results showed that the higher sexual crime rate of the station is correlated with the place where the congestion and crowding occurs. In conclusion, the architectural design elements of subway station which cause the passenger congestion were summarized and measures of architectural design for crime prevention in subway station were suggested.

A Feasibility Study to Inaugurate the Rangsit Crime Survey

Thailand’s crime data collection system is yet to be on par with international standards, resulting in distortedanalysis of actual crime situation and difficulty in formulating crime reduction policy. This research analysescrime data collection system of the United States and the United Kingdom in order to study ways to inaugurate acrime survey and find out the best means of carrying out such project by Rangsit University. This research isconducted qualitatively via in-depth interview of six crime data collection and analysis experts in order torecognise the problems and formulate solutions for crime data collection in Thailand. In addition, a focus groupconsisting of eight crime data collection experts will be formed to determine the procedure involved in theRangsit Crime Survey. The researchers found out that crime data collection system in Thailand, in particular theRoyal Thai Police’s five-group crime data collection, still has shortcomings in terms of redundant and outdatedoffense categorisation, and problems in data collection and dissemination. Crime survey conducted by the Officeof Justice Affairs still has limitations regarding the development of interview forms, data collection, budgetconstraints, and dissemination of data which may be harmful to other state agencies. As for the feasibility ofconducting crime survey by Rangsit University, the experts agree that such survey be conducted in the form ofCrime Fear Poll, the most feasible method which incurs least costs and minimal staff requirements. The name ofthe project will be the “RSU Crime Survey”.

A computational intelligent approach to multi-factor analysis of violent crime information system

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

주택유형이 범죄에 미치는 영향 분석 - 서울시 25개 자치구를 중심으로 -

The purpose of this study is to explore the relationship between housing types and crime and to suggest the appropriate strategies and interventions of housing policies for crime prevention. For spatial analysis of crime data, spatial autocorrelation is tested by Moran's I Test. A Ordinary Least Squares-based regression model is employed because crime data used in this study fails to show spatial autocorrelation. Results show that housing type variables except non-residential housing type are not associated with crime. Among land-use characteristics, the percentage of commercial areas is likely to better explain crime occurrence rather than housing types. It is surprising that residents' satisfaction to housing environment has a positive direction in its relationship with crime even though it cannot have a statistical significance. However, fear of crime shows a negative direction with crime although it fails to have a statistical significance. The findings of this study can contribute to understand the association between housing types and crime when setting housing policies for crime prevention.

Experiences from Assessing Safety in Vingis Park, Vilnius, Lithuania

The aim of this article is to suggest a multi-method approach for assessing safety in parks. The study is based on the analysis of police crime data combined with information from a safety walk and safety survey of park users. The framework is tested in an urban park, Vingis, in the inner city of Vilnius, the capital of Lithuania. Findings show that Vingis is perceived as a safe park, but, compared with police statistics, the survey and the safety walk provide a more nuanced diagnostic of safety in this green area. Vingis’ safety is compromised by car traffic and illicit parking practices, the park’s poor capacity to accommodate users’ needs (e.g. the elderly, parents with small children, young people) and the inadequate infrastructure for users at certain times, such as in the evenings and dark months of the year. Patterns of safety expressed by citizens of Vingis park do not differ from the ones found in parks elsewhere (both in relation to their physical and social environment), despite the recent transformations Vilnius, as the capital city, has overcome since the country’s independence. The article concludes with an assessment of the proposed framework and directions for research and intervention.

Evaluating the effects of the introduction of off-sale alcohol outlets on violent crime.

To examine the effects on violence of a policy change that ended prohibition of off-sale alcohol outlets in Lubbock, Texas. Time-series analysis of violent crime data from police records comparing the periods before and after the policy change. The effect of the policy change on both total violent crime and aggregated assault was small and did not approach statistical significance. Increased availability of alcohol through off-sale premises may not influence the type of violence reported to the police in Lubbock, Texas.

Jurisdictional boundaries and crime analysis: policy and practice

AbstractAll data on crime rates, crime incident locations, crime hot spots, deployment of law enforcement personnel and facility distribution are circumscribed by the boundaries of law enforcement jurisdictions. How these boundaries are defined and change over time has a critical impact on the apparent rates and foci of crime. There are also a range of issues that arise near and spanning law enforcement jurisdiction boundaries. Different law enforcement agencies have different authorities, and there are examples of both conflict and co‐operation across jurisdictional divides and in areas with overlapping or convoluted jurisdiction. In this paper the authors demonstrate in a variety of specific cases the importance of boundaries on the spatial analysis of crime data and the conclusions that can be drawn from that analysis.

The likelihood property in general retrieval operations

Probabilistic models of information retrieval rank objects (e.g. documents) in response to a query according to the probability of some matching criterion (e.g. relevance). These models rarely yield an actual probability and their scoring functions are interpreted to be purely ordinal within a given retrieval task. In this paper we show that some scoring functions possess a likelihood property, which means that the scoring function indicates the likelihood of matching when compared to other retrieval tasks. This is potentially more useful than pure ranking even though it cannot be interpreted as an actual probability. This property can be detected by using two modified effectiveness measures, entire precision and entire recall. Experimental evidence is offered to show the existence of this property both for traditional document retrieval and for the analysis of crime data where suspects of an unsolved crime are ranked according to the probability of culpability.