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Date: November 22, 2024 Fri
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2 results foundAuthor: Great Britain. Home Office Title: Forensic Science Strategy: A national approach to forensic science delivery in the criminal justice system Summary: Vision 1. Forensic science is the application of science to a criminal investigation and court proceedings. This includes crime scene investigation and the collection, identification, analysis and interpretation of potential evidence such as DNA, fingerprints, digital evidence, drug analysis and footwear marks. 2. The Government's vision for forensic science is for a clearer system of governance to ensure quality standards and proper ethical oversight, and a cost effective service that delivers to law enforcement and the criminal justice system (CJS) robust and relevant forensic evidence, and in so doing strengthens public and judicial trust in forensic science. Context 3. Crime is falling, but it is also changing. Latest figures from the Crime Survey for England and Wales (CSEW) show that there were an estimated 6.5 million crimes in the year to June 2015. This is an 8% decrease compared with last year and the lowest estimate since the CSEW began in 1981. Police Recorded Crime shows a long-term shift away from 'traditional' volume crime, such as burglary and theft from a vehicle, and an increase in offences with a digital element, such as child sexual abuse and indecent imagery offences. The shift to digital not only enables new types of crime, but also means that traditional volume crimes can be committed in ways that leave a digital as well as a physical trail. 4. Demand for digital forensics has grown in parallel with the increased use of digital devices over the past 20 years. At the same time, there has been a decline in the demand for traditional forensic science such as DNA and fingerprints. Forensic science can make a significant contribution to improving policing outcomes and efficiency, but will only be able to meet this challenge through a whole system approach, from the crime scene to the court. Landscape 5. There are currently a variety of forensic delivery models in existence ranging from forensic teams in forces, collaborative/regional structures and some operating models linked to wider partnership approaches. All models have a combination of services delivered by forces and external Forensic Service Providers (FSP). 6. In early 2016, the police service will consider options on how best to develop a digital approach which could encompass biometrics (e.g. fingerprint bureau) or broader areas of forensics, including scene of crime officers, digital forensics and other in house forensic facilities. Scoping work setting out business case options is expected to be completed in Spring 2016. A national approach to forensic science delivery, proposed and delivered by police forces, would aim to ensure greater consistency of service quality; resilient, reliable capability and with economies of scale. Details: London: Home Office, 2016. 27p. Source: Internet Resource: Cm 9217: Accessed March 16, 2016 at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/506652/54493_Cm_9217_Forensic_Science_Strategy_Accessible.pdf Year: 2016 Country: United Kingdom URL: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/506652/54493_Cm_9217_Forensic_Science_Strategy_Accessible.pdf Shelf Number: 138256 Keywords: Criminal InvestigationDNAEvidence GatheringFingerprint AnalysisForensic Science |
Author: Laros, Jeroen F.J. Title: Metrics and Visualisation for Crime Analysis and Genomics Summary: Informally speaking, Data Mining [67] is the process of extracting previously unknown and interesting patterns from data. In general this is accomplished using different techniques, each shedding light on different angles of the data. Due to the explosion of data and the development of processing power, Data Mining has become more and more important in data analysis. It can be viewed as a subdomain of Artificial Intelligence (AI [61]), with a large statistical component [4, 28]. Amongst the patterns that can be found by the usage of Data Mining techniques, we can identify Associations. Examples of this can be found in market basket analysis. One of the (trivial) examples would be that tobacco and cigarette paper are often sold together. A more intricate example is that certain types of tobacco (light, medium, heavy) are correlated with different types of cigarette paper. This so-called Association Mining is an important branch of Data Mining. Other patterns that are frequently sought are Sequential patterns. Sequential patterns are patterns in sets of (time)sequences. These patterns can be used to identify trends and to anticipate behaviour of individuals. Associations and Sequential patterns will play a major role in this thesis. Once patterns have been identified, we often need a visualisation of them to make the discovered information insightful. This visualisation can be in the form of graphs, charts and pictures or even interactive simulations. Data Mining is commonly used in application domains such as marketing and fraud detection, but recently the focus also shifts towards other (more delicate) application domains, like pharmaceutics and law enforcement. In this thesis we focus on the application domains law enforcement and sequence analysis. In law enforcement, we have all the prerequisites needed for Data Mining: a plethora of data, lots of categories, temporal aspects and more. There is, however, a reluctance when it comes to using the outcome of an analysis. When used with care, Data Mining can be a valuable tool in law enforcement. It is not unthinkable, for example, that results obtained by Data Mining techniques can be used when a criminal is arrested. Based on patterns, this particular criminal could have a higher risk of carrying a weapon, or an syringe, for example. In law enforcement, this kind of information is called tactical data. After the Data Mining step, statistics is usually employed to see how significant the found patterns are. In most cases, this can be done with standard statistics. When dealing with temporal sequences though, and lots of missing or uncertain data, this becomes exceedingly harder. Details: Leiden: University of Leiden, 2009. Source: Internet Resource: Thesis: Accessed October 13, 2016 at: https://openaccess.leidenuniv.nl/bitstream/handle/1887/14533/thesis.pdf?sequence=2 Year: 2009 Country: International URL: https://openaccess.leidenuniv.nl/bitstream/handle/1887/14533/thesis.pdf?sequence=2 Shelf Number: 144935 Keywords: Crime AnalysisCriminal IntelligenceData MiningDNA |