Predictive Crime Analysis using R

Predictive Crime Analysis using R PDF Author: Jeffrey Strickland
Publisher: Lulu.com
ISBN: 0359431593
Category : Law
Languages : en
Pages : 345

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Book Description
Predictive Crime Analysis using R is Dr. Strickland's second crime analysis book. In this volume, rather than using data to describe crime history, he uses it to predict crime using pattern created with advanced clustering methods, crime series linkage, and text analysis. Coverage includes prediction of conventional crime and terrorist attacks. The open-source software R is introduced and used in developing crime data, including Geo-spatial data, and constructing predictive models and performing post analysis. Using actual crime data from cities like Atlanta, Dr. Strickland also shows how to simulate additional data from actual data. Simulated data can then be used in cities with insufficient actual data, but with similar demographics and human behavior.

Predictive Crime Analysis using R

Predictive Crime Analysis using R PDF Author: Jeffrey Strickland
Publisher: Lulu.com
ISBN: 0359431593
Category : Law
Languages : en
Pages : 345

Get Book Here

Book Description
Predictive Crime Analysis using R is Dr. Strickland's second crime analysis book. In this volume, rather than using data to describe crime history, he uses it to predict crime using pattern created with advanced clustering methods, crime series linkage, and text analysis. Coverage includes prediction of conventional crime and terrorist attacks. The open-source software R is introduced and used in developing crime data, including Geo-spatial data, and constructing predictive models and performing post analysis. Using actual crime data from cities like Atlanta, Dr. Strickland also shows how to simulate additional data from actual data. Simulated data can then be used in cities with insufficient actual data, but with similar demographics and human behavior.

Predictive Policing

Predictive Policing PDF Author: Walt L. Perry
Publisher: Rand Corporation
ISBN: 0833081551
Category : Computers
Languages : en
Pages : 187

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Book Description
Predictive policing is the use of analytical techniques to identify targets for police intervention with the goal of preventing crime, solving past crimes, or identifying potential offenders and victims. These tools are not a substitute for integrated approaches to policing, nor are they a crystal ball. This guide assesses some of the most promising technical tools and tactical approaches for acting on predictions in an effective way.

Data Science Applications using R

Data Science Applications using R PDF Author: Jeffrey Strickland
Publisher: Lulu.com
ISBN: 035981042X
Category : Business & Economics
Languages : en
Pages : 434

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Book Description
To write a single book about data science, at least as I view the discipline, would result in several volumes. I have come to view Data Science as a multidisciplinary field. People who engage in data science may be statisticians, economists, mathematicians, operations research analysts, and a myriad of other scientific professionals. Most would agree that data scientist have advance degrees in one or more of these disciplines. All practitioners would agree that Data is at center stage. This book is intended to demonstrate the multidisciplinary application of data science, using R-programming with R Studio.

Crime Mapping and Spatial Data Analysis using R

Crime Mapping and Spatial Data Analysis using R PDF Author: Juan Medina Ariza
Publisher: CRC Press
ISBN: 100085079X
Category : Mathematics
Languages : en
Pages : 523

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Book Description
Crime mapping and analysis sit at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis. This book brings together relevant knowledge from these fields into a practical, hands-on guide, providing a useful introduction and reference material for topics in crime mapping, the geography of crime, environmental criminology, and crime analysis. It can be used by students, practitioners, and academics alike, whether to develop a university course, to support further training and development, or to hone skills in self-teaching R and crime mapping and spatial data analysis. It is not an advanced statistics textbook, but rather an applied guide and later useful reference books, intended to be read and for readers to practice the learnings from each chapter in sequence. In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data. We then introduce a series of tools to study spatial homogeneity and dependence. A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation. The final chapters introduce the use of basic spatial models, which account for the distribution of crime across space. In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis.

Foundations of Crime Analysis

Foundations of Crime Analysis PDF Author: Jeffery T. Walker
Publisher: Routledge
ISBN: 1317507002
Category : Social Science
Languages : en
Pages : 334

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Book Description
In recent years, the fields of crime analysis and environmental criminology have grown in prominence for their advancements made in understanding crime. This book offers a theoretical and methodological introduction to crime analysis, covering the main techniques used in the analysis of crime and the foundation of crime mapping. Coverage includes discussions of: The development of crime analysis and the profession of the crime analyst, The theoretical roots of crime analysis in environmental criminology, Pertinent statistical methods for crime analysis, Spatio-temporal applications of crime analysis, Crime mapping and the intersection of crime analysis and police work, Future directions for crime analysis. Packed with case studies and including examples of specific problems faced by crime analysts, this book offers the perfect introduction to the analysis and investigation of crime. It is essential reading for students taking courses on crime analysis, crime mapping, crime prevention, and environmental criminology. A companion website offers further resources for students, including flashcards and video and website links. For instructors, it includes chapter-by-chapter PowerPoint slides.

Predictive Policing and Artificial Intelligence

Predictive Policing and Artificial Intelligence PDF Author: John McDaniel
Publisher: Routledge
ISBN: 0429560389
Category : Computers
Languages : en
Pages : 459

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Book Description
This edited text draws together the insights of numerous worldwide eminent academics to evaluate the condition of predictive policing and artificial intelligence (AI) as interlocked policy areas. Predictive and AI technologies are growing in prominence and at an unprecedented rate. Powerful digital crime mapping tools are being used to identify crime hotspots in real-time, as pattern-matching and search algorithms are sorting through huge police databases populated by growing volumes of data in an eff ort to identify people liable to experience (or commit) crime, places likely to host it, and variables associated with its solvability. Facial and vehicle recognition cameras are locating criminals as they move, while police services develop strategies informed by machine learning and other kinds of predictive analytics. Many of these innovations are features of modern policing in the UK, the US and Australia, among other jurisdictions. AI promises to reduce unnecessary labour, speed up various forms of police work, encourage police forces to more efficiently apportion their resources, and enable police officers to prevent crime and protect people from a variety of future harms. However, the promises of predictive and AI technologies and innovations do not always match reality. They often have significant weaknesses, come at a considerable cost and require challenging trade- off s to be made. Focusing on the UK, the US and Australia, this book explores themes of choice architecture, decision- making, human rights, accountability and the rule of law, as well as future uses of AI and predictive technologies in various policing contexts. The text contributes to ongoing debates on the benefits and biases of predictive algorithms, big data sets, machine learning systems, and broader policing strategies and challenges. Written in a clear and direct style, this book will appeal to students and scholars of policing, criminology, crime science, sociology, computer science, cognitive psychology and all those interested in the emergence of AI as a feature of contemporary policing.

Classification, Clustering, and Data Mining Applications

Classification, Clustering, and Data Mining Applications PDF Author: David Banks
Publisher: Springer Science & Business Media
ISBN: 3642171036
Category : Language Arts & Disciplines
Languages : en
Pages : 642

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Book Description
This volume describes new methods with special emphasis on classification and cluster analysis. These methods are applied to problems in information retrieval, phylogeny, medical diagnosis, microarrays, and other active research areas.

The Cambridge Handbook of Policing in the United States

The Cambridge Handbook of Policing in the United States PDF Author: Tamara Rice Lave
Publisher: Cambridge University Press
ISBN: 1108420559
Category : Law
Languages : en
Pages : 615

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Book Description
A comprehensive collection on police and policing, written by experts in political theory, sociology, criminology, economics, law, public health, and critical theory.

Modern Statistics with R

Modern Statistics with R PDF Author: Måns Thulin
Publisher:
ISBN: 9781032497457
Category : Mathematics
Languages : en
Pages : 0

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Book Description
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.

Public Policy Analytics

Public Policy Analytics PDF Author: Ken Steif
Publisher: CRC Press
ISBN: 1000401618
Category : Business & Economics
Languages : en
Pages : 254

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Book Description
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.