Predict and Surveil

2020 ◽  
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Los Angeles ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Civil Liberties ◽  
Big Data Analytics ◽  
Police Department ◽  
Law Enforcement Agencies ◽  
Criminal Suspects

The scope of criminal justice surveillance, from policing to incarceration, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences. This book offers an inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world—the Los Angeles Police Department. Drawing on original interviews and ethnographic observations from over two years of fieldwork with the LAPD, the text examines the causes and consequences of big data and algorithmic control. It reveals how the police use predictive analytics and new surveillance technologies to deploy resources, identify criminal suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-driven practices. While big data analytics has the potential to reduce bias, increase efficiency, and improve prediction accuracy, the book argues that it also reproduces and deepens existing patterns of inequality, threatens privacy, and challenges civil liberties.

The Criminal Law and Law Enforcement Implications of Big Data

2018 ◽  
Vol 14 (1) ◽  
pp. 293-308 ◽  
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Data Collection ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Police Departments ◽  
Law Enforcement Agencies ◽  
Future Directions ◽  
Legal Questions ◽  
Legal Responses

Law enforcement agencies increasingly use big data analytics in their daily operations. This review outlines how police departments leverage big data and new surveillant technologies in patrol and investigations. It distinguishes between directed surveillance—which involves the surveillance of individuals and places under suspicion—and dragnet surveillance—which involves suspicionless, unparticularized data collection. Law enforcement's adoption of big data analytics far outpaces legal responses to the new surveillant landscape. Therefore, this review highlights open legal questions about data collection, suspicion requirements, and police discretion. It concludes by offering suggestions for future directions for researchers and practitioners.

Big Data Surveillance: The Case of Policing

2017 ◽  
Vol 82 (5) ◽  
pp. 977-1008 ◽  
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Los Angeles ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Police Department ◽  
Social Consequences ◽  
Risk Scores ◽  
Data Systems ◽  
Wide Range ◽  
Data Surveillance

This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Directed Surveillance

2020 ◽  
pp. 56-73
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Los Angeles ◽  
Police Department ◽  
Criminal Activity ◽  
Intelligence Gathering ◽  
Predictive Policing ◽  
Crime Patterns ◽  
General Terms ◽  
Using Data

This chapter focuses on directed surveillance, or the surveillance of people and places deemed suspicious. Big data is associated with a shift from reactive to predictive policing. Predictive policing refers to analytic techniques used by law enforcement to forecast potential criminal activity. It involves using data to determine current crime patterns and direct patrol resources, such as where officers should go and who they should stop. In general terms, the stages of predictive policing are collection, analysis, intervention, and response. The chapter analyzes how the Los Angeles Police Department (LAPD) conducts person- and place-based predictive policing; why it adopted different forms of predictive policing; how it uses algorithms to quantify criminal risk; how the police do—and do not—incorporate insights from the algorithms into their work in the field; and how predictive policing serves as a foundation for ongoing intelligence gathering.

scholarly journals An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach

2020 ◽  
Vol 12 (4) ◽  
pp. 132-146
Author(s):  
Gabriel Kabanda
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Document Analysis ◽  
Heterogeneous Data ◽  
Discourse Theory ◽  
Mining Machine ◽  
Data Types ◽  
Qualitative Research Methodology

Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

Survey of the Different Type of Data Analytics Algorithms

2017 ◽  
Vol 8 (1) ◽  
pp. 50-64 ◽  
Author(s):  
Michael Kevin Hernandez
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
High Volume ◽  
Data Driven ◽  
Advantages And Disadvantages ◽  
Definition Of ◽  
Data Driven Decisions ◽  
Quantitative Definition

From as early as 1854 to today, society has been gathering, processing, transforming, modeling and visualizing data to help drive data-driven decisions. The qualitative definition of big data can be defined more conclusively as data that has high volume, velocity, and variety. Whereas, the quantitative definition of big data does vary with respect to time due to the dependence of the time's technology and processing capabilities. However, making use of that big data to facilitate data-driven decisions, one should employ either descriptive, predictive, or prescriptive analytics. This article has discussed and summarized the advantages and disadvantages of the algorithms that fell under descriptive and predictive analytics. Given the sheer number of the different types of algorithms and the amount of versatile data mining software available sometimes, the best big data analytics results can come from mixing two to three of the mentioned algorithms.

scholarly journals Cloud Enabled Predictive Big data Analytics Framework for Healthcare

2019 ◽  
Vol 8 (3) ◽  
pp. 3784-3789
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Detailed Examination ◽  
Healthcare Applications ◽  
Large Size ◽  
Architecture Framework ◽  
Framework Model ◽  
Hidden Patterns

Big data is one of the recently emerged domain in today's digital age where everything is being digitized. Very large size of data is produced from various organizations all through the globe .This very large size of data is termed as big data..Conventional databases are not able to handle the challenges with enormous data. Detailed examination of large amounts of data requires a lot of efforts at various levels with the aim of finding knowledge , hidden patterns for decision making. Big data analytics plays an essential character in order to attain predictive analytics in healthcare. Cloud systems can be employed for the storage of big data so that users can be able to access it from anywhere . The objective of this paper is to review the concept of big data , healthcare in the context of big data and cloud computing. It also proposes architecture framework model for predictive big data analytics in healthcare area. It also presents technology for big data analytics. This paper also addresses the use of big data analytics along with specifying healthcare applications

Police Pushback

2020 ◽  
pp. 74-99
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Los Angeles ◽  
Data Collection ◽  
Big Data Analytics ◽  
Police Department ◽  
Professional Autonomy ◽  
Experiential Knowledge ◽  
Managerial Control ◽  
Patrol Officers ◽  
New Developments

This chapter highlights how the police resist and contest big data analytics. Their resistance stems in large part from the proliferation of high-frequency observations and data collection sensors resulting in the police themselves coming under increased surveillance. New developments in surveillance technologies are frequently viewed with suspicion, with officers believing technology is a means of deskilling, entrenching managerial control, devaluing experiential knowledge, and threatening their professional autonomy. Novel tech ultimately serves to reinforce old divisions—such as those between managers and patrol officers—even as it creates new distinctions within the Los Angeles Police Department (LAPD). Understanding these patterns of contestation underscores how big data is ultimately social. It also allows one to consider the extent to which data-based surveillance is—and is not—associated with deeper organizational change.

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