PubMed Health⌕ Search

PubMed · 16893621

Concepts and possibilities in forensic intelligence.

Abstract

Forensic intelligence can be viewed as comprising two parts, one directly concerning intelligence delivery in forensic casework, the other considering performance aspects of forensic work, loosely termed here as business intelligence. Forensic casework can be viewed as processes that produce an intelligence product useful to police investigations. Traditionally, forensic intelligence production has been confined to discipline-specific activity. This paper examines the concepts, processes and intelligence products delivered in forensic casework, the information repositories available from forensic examinations, and ways to produce within- and across-discipline casework correlations by using information technology to capitalise on the information sets available. Such analysis presents opportunities to improve forensic intelligence services as well as challenges for technical solutions to deliver appropriate data-mining capabilities for available information sets, such as digital photographs. Business intelligence refers primarily to examination of efficiency and effectiveness of forensic service delivery. This paper discusses measures of forensic activity and their relationship to crime outcomes as a measure of forensic effectiveness.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chris Bell. 2006-08-07. Concepts and possibilities in forensic intelligence.. https://doi.org/10.1016/j.forsciint.2006.06.030

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗