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Date: November 22, 2024 Fri
Time: 11:58 am
Time: 11:58 am
Results for data mining
3 results foundAuthor: Mack, Elizabeth A. Title: Sex Offenders and Residential Location: A Predictive Analytic Framework Summary: Despite the growing body of research dealing with sex offenders and the collateral consequences of legislation governing their post release movements, a complete understanding of the residential choices of registered sex offenders remains elusive. The purpose of this paper is to introduce a predictive analytical framework for determining which demographic and socioeconomic factors best forecast the residential choices of convicted sex offenders. Specifically, using a derived index of social disorganization (ISDOR) and a commercial geographic information system (GIS), we implement both linear statistical and non-linear data mining approaches to predict the presence of sex offenders in a community. The results of this analysis are encouraging, with nearly 75% of registered offender locations predicted correctly. The implications of these approaches for public policy are discussed. Details: Tempe, AZ: Arizona State University, GeoDa Center for Geospatial Analysis and Computation, 2010. 37p. Source: Internet Resource: Working Paper No. 2010-03: Accessed October 14, 2010 at: http://geodacenter.asu.edu/drupal_files/2010-03_0.pdf Year: 2010 Country: United States URL: http://geodacenter.asu.edu/drupal_files/2010-03_0.pdf Shelf Number: 119955 Keywords: Data MiningGeographic StudiesGeospatial AnalysisGISResidency RestrictionsSex OffendersSocioeconomic Status |
Author: Bachner, Jennifer Title: Predictive Policing: Preventing Crime with Data and Analytics Summary: In this report, Dr. Bachner tells compelling stories of how new policing approaches in communities are turning traditional police officers into "data detectives." Police departments across the country have adapted business techniques -- initially developed by retailers, such as Netflix and WalMart, to predict consumer behavior -- to predict criminal behavior. The report presents case studies of the experiences of Santa Cruz, CA; Baltimore County, MD; and Richmond, VA, in using predictive policing as a new and effective tool to combat crime. While this report focuses on the use of predictive techniques and tools for preventing crime in local communities, these techniques and tools can also be applied to other policy arenas, as well, such as the efforts by the Department of Housing and Urban Development to predict and prevent homelessness, or the Federal Emergency Management Agency's efforts to identify and mitigate communities vulnerable to natural disasters. Details: Washington, DC: IBM Center for The Business of Government, 2013. 40p. Source: Internet Resource: Accessed August 12, 2014 at: http://www.businessofgovernment.org/sites/default/files/Predictive%20Policing.pdf Year: 2013 Country: United States URL: http://www.businessofgovernment.org/sites/default/files/Predictive%20Policing.pdf Shelf Number: 133020 Keywords: Crime AnalysisCrime PreventionData MiningPredictive Policing |
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 |