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Begins with an examination of the principles underlying the construction of social indicators to be used for the express purpose of assessing performance in achieving the social agenda of the EU. The end product in this context is a portfolio of common indicators applied across the EU, and the principles involved in its construction may concern either the single indicators or the portfolio as a whole. Indicators can take different forms, and the second part of Ch. 2 considers indicator properties; since any portfolio of indicators involves selecting from a range of competing alternatives, it is essential to understand the full set of possibilities. Choice of indicators involves compromise over objectives, but will also be constrained by data limitations and by institutional differences across countries; these are the subjects of the third part of Ch. 2.

This chapter argues that creative theorizing in social science has to begin with observation. The way one observes at the early stage of the research differs from the way one does it later. What matters at this point is to get a real sense for the topic so that one can develop some good ideas, which may later be used to construct a theory. This means, among other things, that one has to open oneself up to what is happening, with all one's sense as well as with one's subconscious. Moreover, what matters at this stage is not only to collect data but also to collect social data. This means data about social life, about what happens between people who live in groups, communities, and societies.

This chapter discusses the potential use of GIS in social policy analysis. It describes the advantages of GIS as an approach to social policy enquiry and provides a number of extant examples from a variety of policy fields. Although it is typically thought of as a quantitative method, the chapter also emphasises more recent qualitative uses of GIS. The chapter addresses some of the barriers to the use of GIS in social policy and how these can be overcome. The chapter concludes with an overview of how recent innovations in GIS and the availability of social data can have a positive impact on the use of GIS in the field of social policy.

Measures of social difference are commonplace in mainstream research in the quantitative social sciences. They are used to explain collective outcomes ranging from voluntary contributions in elementary schools to civil wars. This research assumes that the categories of classification that we use are individually and socially relevant and that groups cannot easily cooperate to further common goals. This chapter challenges these assumptions. It provides a comparative account of social data collection, highlighting the fluidity of identity and the political and ideological influences on its classification. It also collates recent evidence on deliberative democracy that warns against using demographic maps to understand community performance. It concludes with reflections on the potential role of social data collection in policies aimed at reducing group inequalities.

This chapter asks the question, what does this unthinking mean for current anthropogenic climate change policies? This is answered in two ways. First, the concept of urban demand is discussed in its current manifestation as the product of a global Advertising-Big Data-Social Media complex. Second, the mechanisms behind the immensity of Chinese urban growth in recent decades are described. In their different, but intertwined, ways these two expressions of today’s modernity are pointing irrevocably towards terminal consumption. The only means to stop this happening appears to a reinvention of the city, creating an urban demand for stewarding nature for future generations, a posterity city

The term Big Data is applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, currently ranging from a few dozen terabytes to many petabytes of data in a single data set. This chapter addresses some of the theoretical and practical issues raised by the possibility of using massive amounts of social and cultural data in the humanities and social sciences. These observations are based on the author’s own experience working since 2007 with large cultural data sets at the Software Studies Initiative at the University of California, San Diego. The issues discussed include the differences between ‘deep data’ about a few people and ‘surface data’ about many people; getting access to transactional data; and the new “data analysis divide” between data experts and researchers without training in computer science.

This chapter reviews professional ethical considerations related to data collection as an integral part of the forensic mental health assessment evaluation. An ethical approach to forensic evaluation—which calls for neutrality, objectivity, and scientific reasoning—safeguards against various inferential, allegiance, and pecuniary biases that can impact a forensic evaluator. Ethical considerations related to remaining within the scope of the evaluation, assessing response styles, the collection and use of collateral interviews and documentation, examinee privacy, forensic assessment feedback, and the role and use of Internet and social media data are discussed. Expert commentary addresses the importance of testing rival hypotheses in conducting a forensic evaluation.

This chapter discusses Social Network Analysis, a technique used to analyze social networks within social games as a method to enhance retention in games. We will show how one can use this method by applying it to the problem of retention within the game Tom Clancy’s The Division (TCTD). Using the game and the analysis will help you understand how to use SNA to understand types of players and influential players, and, as a result, understand how to engage different players, especially influencers, to increase retention. While the chapter will focus on the use of SNA for TCTD as a case study, the methods discussed under SNA can be applied to other types of games. Please note that this chapter is an extension of the work done by several collaborators to the authors, including Casper Harteveld (professor, Northeastern University), Sebastian Deterding (professor, York University), and Ahmad Azadvar (User Research Lead at Ubisoft Massive), and the work was accomplished with the support of Ubisoft, the Games Lab, and the Live Ops team at Massive Entertainment.

Technology is all too often seen as being inherently good, and there are powerful interests limiting the amount of attention paid to the darker side of ICTs and Internet access in particular. However, these darker aspects are crucial to understanding ICT4D, especially since they can more seriously impact the poor, both countries and people, than the rich. The following main challenges are covered in the chapter: privacy and security; the Surface Web and the Dark Web; cyber-security and resilience; negative aspects of the exploitation of Big Data and the abuse of people through social media; and the increasing dehumanization of people through the use of ICTs and the Internet of Things.

The chapters in this book imply a way forward for the direction of economic policy in the Middle East and North Africa. This chapter summarizes three main conclusions from the evidence compiled in the book. First, more research needs to be done using innovative social data collection techniques to better understand how institutions and incentives can be reordered to better foster youth inclusion under post–Arab Spring regimes. Second, several areas of major policy reform are highlighted using the evidence compiled in this book. Third, the most important area of future reforms lies in small-scale social innovation that can help foster dynamic policy responses to the challenges young people will continue to face even as the political economy landscape continues to shift.

This chapter discusses P and NP through Frenemy, an imaginary world where every pair of people comprises either friends or enemies. Frenemy has about 20,000 inhabitants. Every individual seems normal, but put two in close vicinity and a strange thing happens. Either the two take an instant liking to each other, immediately becoming the best of friends, or they take one look at each other and immediately become the worst of enemies. By examining social networking data such as Facebook and Twitter, computer scientists at the Frenemy Institute of Technology have put together a nearly complete database showing which pairs of people are friends and which pairs of people are enemies. The chapter then shows what these researchers can and cannot do with this data. It also presents other NP problems from a few other scientific fields where there is no known efficient algorithms.

While there is continued interest in measuring governance, disagreement on how best to do so has only grown over time. To provide pointers at innovative and rigorous indicator building, this chapter documents innovations in measuring a particularly challenging governance dimension: corruption in public procurement. In hopes of inspiring future research, the chapter critically reviews objective corruption proxies using administrative data on government purchases falling in four broad categories: tendering risk indicators, political connections indicators, supplier risk indicators, and contracting body risk indicators. The findings indicate that the best measurement instruments focus on the transaction level (micro level) while allowing for consistent aggregations for time series and cross-country comparisons. Such actionable indicators capture behaviour as directly as possible rather than remaining at the country level. They also retain the relational or transactional aspects of governance, revealing a much more dynamic picture than widely used population and expert surveys.