There is an increasing prevalence of large and complex datasets within the social sciences, and notably criminology in recent years. This data explosion has led to the development of specialized techniques for extracting information from such data. This book presents an introduction to "Big Data Science" for criminology and the social sciences, taking a case study-based approach to explaining the concepts. The theory is introduced as needed to answer scientific questions based on real data problems in the application areas. Some R and Python code is included to give support with implementation of the methods.
Part 1: Big Data for Criminology, Criminal Justice and Sociology.
Chapter 1: Criminology;
Chapter 2: Sociology; Part 2: Big Data Science.
Chapter 3: Big Data Science;
Chapter 4: Visualisation of Big Data. Appendices: Probability; Statistics; Statistical Sampling; Network Theory; Information Theory.
Marcello Trovati (Department of Computing and Mathematics, University of Derby, UK), Fionn Murtagh (Goldsmiths University of London, United Kingdom), Richard Hill, Philip Hodgson, Philip Burton-Cartledge, Michael Teague, Charlotte Hargreaves.
