Crime scene examiners and volume crime investigations: an empirical study of perception and practice

Most police forces in the UK employ specially trained crime scene examiners (CSEs) to provide forensic science support to the investigation of crime. Previous research has shown wide variations in the management, deployment, and performance of this staff group. There is also evidence that informal elements of professional and organisational culture, in particular the role characterisations of crime scene examiners, also have a bearing on their effective use in the investigation of high volume property crime. These issues are explored as part of a more extensive study of forensic science provision in the two largest police forces in Scotland and by the four main Scottish Police Services Authority Forensic Services (SPSA FS) units. A range of staff in these organisations described their understandings of the role of crime scene examiners – as evidence collectors, forensic investigators, specialist advisers, or any combination of these. Whilst two thirds (62%) of respondents recognised the complexity and scope of the role of CSEs including its cognitive elements, a substantial minority (38%) categorised the role as having a single element – collecting evidence – and therefore perceived it as limited largely mechanical in character. The reasons for, and consequences of, this perception are considered, and the paper concludes with a challenge to reconsider this limited view of what crime scene examiners can contribute to volume crime investigations

PROCESS OF IMPLEMENTATION AND BENEFITS OF CRIME SCENE INVESTIGATION IN CASE OF CRIMINAL INVESTIGATION IN INDONESIA

The case of crime scene conducted by the investigator is the main part in the disclosure of criminal case because in the crime scene can be found the interaction between the perpetrator of the crime, the evidence used and the witness/victim of crime at the time of the criminal event. Therefore this paper analyzes the implementation process and the benefits of crime scene in the process of criminal investigation in Indonesia. The research method used was sociological juridical, with primary and secondary data sources. Primary data were obtained by conducting interviews with respondents, they were investigators who investigate criminal case. The results of the research on the procedures for the implementation of the Crime Scene Investigation is started from the preparation of the crime scene, the trip to the scene, the first action at the crime scene (TPTKP). The crime scene investigation covers the general observations, photography, sketching, evidence collection, victim handling, and perpetrators of crime scene organization, and the end of the crime scene, which consists of consolidation, opening/exempting the crime scene, the making of investigation report at the scene, and the evacuation of activities. The benefit of crime scene investigation in criminal investigation is as evidence, source of information to look for witness, as source to look for evidence and as saber to find perpetrator, legal basis of investigator in execution of crime scene is JUKLAK (standard operational procedure) Police of Republic of Indonesia with number 04/1982 and JUKNIS Police of the Republic of Indonesia with the number JUKNIS 01/11/1982

A Model for Managing Crime Scene Examiners

Police forces in the UK employ specially trained Crime Scene Examiners (CSEs) to provide forensic science support to the investigation of crime. Previous research (Bradbury and Feist 2005; Williams 2004) has shown wide variations in the management, deployment, and performance of this staff group and, as such, there is a need to develop performance indicators as a measure of effectiveness. This paper looks at the performance and management of CSEs in Durham Constabulary and discusses a model which focuses on the quality of the work of CSEs rather than the quantity of scenes visited, fingermarks lifted or DNA samples collected. Durham Constabulary focus on three main areas of performance to manage their crime scene examiners: level of activity, quality of materials collected, and the conversion of forensic materials into intelligence matches. In this paper we explore a model of performance management which demonstrates how activity measures and review processes can be implemented and utilised to provide insight into the effectiveness of forensic science. Performance management data collected from 24 CSEs over a one-year period (January to December 2011) is used to discuss the role of forensic performance measures in a scientific support unit, reflecting on the strengths and weaknesses of the measures collected

Mining Consumer Knowledge from Shopping Experience: A case study on Indian E_Commerce Industry

E_Commerce becomes far much popular in recent years. E Commerce nowadays is almost everywhere. People go through online ; meanwhile, they are more and more accustomed to buy goods via E_Commerce channel. - The E-Commerce web sites are facing lots of problems today. Customers prefer traditional way to purchase the products and not from E-Commerce web sites. If we see the history of E-Commerce, then we get that E-Commerce is the purpose of Internet and the web to conduct business Even in recession, it is thriving and has become one of the most important consumption modes. This study uses cluster analysis to identify the profiles of E_Commerce consumers. The rules between E_Commerce spokespersons and commodities from consumers are recognized by using association analysis. Depicting the marketing knowledge map of spokespersons, the best endorsement portfolio is found out to make recommendations. By the analysis of spokespersons, period, customer profiles and products, four business modes of E_Commerce are proposed for consumers: new product, knowledge, low price and luxury product; the related recommendations are also provided for the industry reference

Mining Consumer Knowledge from Shopping Experience: TV Shopping Industry

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The polymorhism of crime scene investigation: an exploratory analysis of the influence of crime and forensic intelligence on decisions made by crime scene examiners

A growing body of scientific literature recurrently indicates that crime and forensic intelligence influence how crime scene investigators make decisions in their practices. This study scrutinises further this intelligence-led crime scene examination view. It analyses results obtained from two questionnaires. Data have been collected from nine chiefs of Intelligence Units (IUs) and 73 Crime Scene Examiners (CSEs) working in forensic science units (FSUs) in the French speaking part of Switzerland (six cantonal police agencies). Four salient elements emerged: (1) the actual existence of communication channels between IUs and FSUs across the police agencies under consideration; (2) most CSEs take into account crime intelligence disseminated; (3) a differentiated, but significant use by CSEs in their daily practice of this kind of intelligence; (4) a probable deep influence of this kind of intelligence on the most concerned CSEs, specially in the selection of the type of material/trace to detect, collect, analyse and exploit. These results contribute to decipher the subtle dialectic articulating crime intelligence and crime scene investigation, and to express further the polymorph role of CSEs, beyond their most recognised input to the justice system. Indeed, they appear to be central, but implicit, stakeholders in intelligence-led style of policing

Predictive Modeling of Fuel Efficiency of Trucks

This research studied the behavior of several controllable variables that affect the fuel efficiency of trucks. Re-routing is the process of modifying the parameters of the routes for a set of trips to optimize fuel consumption and also to increase customer satisfaction through efficient deliveries. This is an important process undertaken by a food distribution company to modify the trips to adapt to the immediate necessities. A predictive model was developed to calculate the change in Miles per Gallon (MPG) whenever a re-route is performed on a region of a particular distribution area. The data that was used, was from the Dallas center which is one of the distribution centers owned by the company. A consistent model that could provide relatively accurate predictions across five distribution centers had to be developed. It was found that the model built using the data from the Corporate center was the most consistent one. The timeline of the data used to build the model was from May 2013 through December 2013. The predictive model provided predictions of which about 88% of the data that was used, was within the 0-10% error group. This was an improvement on the lesser 43% obtained for the linear regression and K-means clustering models. The model was also validated on the data for January 2014 through the first two weeks of March 2014 and it provided predictions of which about 81% of the data was within the 0-10 % error group. The average overall error was around 10%, which was the least for the approaches explored in this research. Weight, stop count and stop time were identified as the most significant factors which influence the fuel efficiency of the trucks. Further, neural network architecture was built to improve the predictions of the MPG. The model can be used to predict the average change in MPG for a set of trips whenever a re-route is performed. Since the aim of re-routing is to reduce the miles and trips; extra load will be added to the remaining trips. Although, the MPG would decrease because of this extra load, it would be offset by the savings due to the drop in miles and trips. The net savings in the fuel can now be translated into the amount of money saved