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PrefaceList of FiguresList of TablesIntroductionCyberspace2.1 What is cyberspace?2.2 The impact of cyberspace2.3 Identity and authentication2.4 Encryption2.5 Crime is changing2.6 Policing is changingNew opportunities for criminality3.1 Unprecedented access to information3.2 Crimes directed against cyberspace3.2.1 Malware3.2.2 Crimes of destruction3.2.3 Monetized cybercrimes3.2.4 Data theft crimes3.2.5 Secondary markets3.3 Crimes that rely on cyberspace3.3.1 Spam, scams, and cons3.3.2 Financial crime3.3.3 Online shopping3.3.4 Crimes against children3.4 Crimes done differently because of cyberspace3.4.1 Disseminating hatred3.4.2 Selling drugs3.4.3 Stalking and crime preparation3.4.4 Digital vigilantes3.5 Money laundering3.5.1 Cash3.5.2 The financial system3.5.3 International money laundering3.5.4 Cryptocurrencies3.6 Overlap with violent extremismNew ways for criminals to interact4.1 Criminal collaboration4.2 Planning together4.3 Information sharing4.3.1 Sharing techniques4.3.2 Sharing resources4.3.3 Sharing vulnerabilities4.4 International interactionsData analytics makes criminals easier to find5.1 Understanding by deduction5.2 Understanding by induction5.3 Subverting data analytics5.4 Intelligence-led policing5.5 Hot spot policing5.5.1 Place5.5.2 Time5.5.3 Weather5.5.4 People involved5.5.5 Social network position5.6 Exploiting skewed distributionsData collection6.1 Ways to collect data6.2 Types of data collected6.2.1 Focused data6.2.2 Large volume data6.2.3 Incident data6.2.4 Spatial data6.2.5 Temporal data6.2.6 Non-crime data6.2.7 Data fusion6.2.8 Protecting data collected by law enforcement6.3 Issues around data collection6.3.1 Suspicion6.3.2 Wholesale data collection6.3.3 Privacy6.3.4 Racism and other -isms6.3.5 Errors6.3.6 Bias6.3.7 Sabotaging data collection6.3.8 Getting better data by sharingTechniques for data analytics7.1 Clustering7.2 Prediction7.3 Meta issues in prediction7.3.1 Classification versus regression7.3.2 Problems with the data7.3.3 Why did the model make this prediction?7.3.4 How good is this model?7.3.5 Selecting attributes7.3.6 Making predictions in stages7.3.7 Bagging and boosting7.3.8 Anomaly detection7.3.9 Ranking7.3.10 Should I make a prediction at all?7.4 Prediction techniques7.4.1 Counting techniques7.4.2 Optimization techniques7.4.3 Other ensembles7.5 Social network analysis7.6 Natural language analytics7.7 Making data analytics available7.8 Demonstrating complianceCase studies8.1 Predicting crime rates8.2 Clustering RMS data8.3 Geographical distribution patterns8.4 Risk of gun violence8.5 Copresence networks8.6 Criminal networks with a purpose8.7 Analyzing online posts8.7.1 Detecting abusive language8.7.2 Detecting intent8.7.3 Deception8.7.4 Detecting fraud in text8.7.5 Detecting sellers in dark-web marketplaces8.8 Behavior ⁰́₃ detecting fraud from mouse movements8.9 Understanding drug trafficking pathwaysLaw enforcement can use interaction too9.1 Structured interaction through transnational organizations9.2 Divisions within countries9.3 Sharing of information about crimes9.4 Sharing of data9.5 Sharing models9.6 International issuesSummaryIndex

Discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a range of application areas. This book helps you determine which matrix is appropriate for your dataset and what the results mean. It also shows how matrix decompositions can be used to find documents on the Internet.

Abstract.Theories of radicalization make implicit predictions about variation among attitudes in the communities from which radicals are drawn. This article subjects some popular theories to empirical testing. The precise process by which individuals come to sympathize with, provide material support for or actually engage in political violence is fully comprehensible only by longitudinal analysis, but much existing work tries to reconstruct the process by looking only at one part of its outcomes: those who have become radicalized. The result is a large number of theories and mechanisms, with little compelling empirical support. A cross-sectional snapshot of an at-risk community cannot definitively support a particular theory of radicalization, but it can rule out those whose predictions about attitudes are at variance with the empirical observations. We designed a survey instrument to measure attitudes to issues widely believed to be relevant to radicalization and deployed it among Muslim communities in Ottawa. The results are remarkably inconsistent with patterns of variation in attitudes predicted by popular theories of radicalization. Instead, they show variation of attitudes along three independent dimensions: social/economic/political satisfaction/dissatisfaction, moral/religious satisfacton/dissatisfaction, and a dimension that appears to be associated with radicalization. This suggests that governments may have less policy leverage to mitigate radicalization than generally supposed.Résumé.Les théories de la radicalisation font des prévisions implicites quant à la variation parmi les attitudes au sein des communautés dont les radicaux sont issus. Cet article soumet quelques unes des théories populaires à une vérification empirique. Le processus précis par lequel des individus sympathisent avec, appuient ou s'engagent dans la violence politique ne peut être complètement saisi que par une analyse longitudinale, bien qu'une bonne partie de la recherche à date essaie de reconstruire le processus en regardant seulement une partie de ces résultats : ceux qui sont devenus radicalisés. En conséquence, un grand nombre de théories et de mécanismes sont soutenus par peu de preuve empirique. Un aperçu transversal d'une communauté à risque ne peut pas confirmer une théorie particulière de radicalisation, mais il peut éliminer celles dont les prévisions ne se conforment pas aux observations empiriques. Nous avons conçu un instrument de sondage pour mesurer des attitudes envers des enjeux qui sont largement perçus comme se rapportant à la radicalisation et nous l'avons administré au sein de communautés musulmanes à Ottawa. Le désaccord entre les résultats et les variations des attitudes postulées par les théories de la radicalisation est remarquable. Les résultats manifestent davantage une variation des attitudes autour de trois dimensions indépendantes : l'in/satisfaction sociale/économique/politique, l'in/satisfaction morale/religieuse ainsi qu'une dimension qui semble être liée à la radicalisation. Il s'ensuit que les politiques publiques risquent d'être moins utiles aux gouvernements pour atténuer la radicalisation que ce qui est généralement supposé.