Explaining the Long-Term Trend in Violent Crime: A Heuristic Scheme and Some Methodological Considerations

There has been a discontinuous but fairly persistent long-term decline in homicide rates in core European countries since about 1500. Since the 1950s, however, we observe an upward trend in violent crime not only in Europe but in almost all of the economically advanced nations that combine democratic political structures with free-market economies. The paper presents an explanatory scheme designed to account for both, the long decline and its apparent reversal. The theoretical model draws heavily upon ideas taken from the sociological work of Emile Durkheim and Norbert Elias—with some modifications and extensions. It seeks to integrate sociological and historical perspectives and to give due weight to both, structural and developmental forces. A key hypothesis is that the pacifying effects of the erosion of traditional collectivism can only be maintained to the extent by which “cooperative individualism” dominates over against the forces of “disintegrative individualism.” Some suggestions are made concerning the selection of appropriate indicators and the handling of methodological problems related to causal attribution.

Hating the neighbors: The role of hate crime in the perpetuation of black residential segregation

Grounded in group conflict theory and the defended neighborhoods thesis, this nationwide empirical study of cities and their residential segregation levels, examines the occurrence of hate crime using data on for all U.S. cities with populations over 95,000, and data compiled from the Uniform Crime Report for hate crime, in conjunction with 2000 census data. Hate crime is any illegal act motivated by pre-formed bias against, in this case, a person’s real or perceived race. This research asks: Do hate crime levels predict white/black segregation levels? How does hate crime predict different measures of white/black segregation? I use the dissimilarity index measure of segregation operationalized as a continuous, binary and ordinal variable, to explore whether hate crime predicts segregation of blacks from whites. In cities with higher rates of hate crime there was higher dissimilarity between whites and blacks, controlling for other factors. The segregation level was more likely to be “high” in a city where hate crime occurred. Blacks are continually multiply disadvantaged and distinctly affected by hate crime and residential segregation. Prior studies of residential segregation have focused almost exclusively on individual choice, residents’ lack of finances, or discriminatory actions that prevent racial minorities from moving, to explore the correlates of segregation. Notably absent from these studies are measures reflecting the level of hate crime occurring in cities. This study demonstrates the importance of considering hate crime and neighborhood conflict when contemplating the causes of residential segregation.

Anomic Crime in Post-Welfarist Societies. Cult of the Individual, Integration Patterns and Delinquency

Post-Fordist economies come along with post-welfarist societies marked by intensified cultural individualism and increased structural inequalities. These conditions are commonly held to be conducive to relative deprivation and, thereby, anomic crime. At the same time, post-welfarist societies develop a new ‘balance of power’ between institutions providing for welfare regulation, such as the family, the state and the (labour) market – and also the penal system. These institutions are generally expected to improve social integration, ensure conformity and thus reduce anomic crime. Combining both perspectives, we analyse the effects of moral individualism, social inequality, and different integration strategies on crime rates in contemporary societies through the lenses of anomie theory. To test our hypotheses, we draw on time-series cross-section data compiled from different data sources (OECD, UN, WHO, WDI) for twenty developed countries in the period 1970-2004, and run multiple regressions that control for country-specific effects. Although we find some evidence that the mismatch between cultural ideal (individual inclusion) and structural reality (stratified exclusion) increases the anomic pressure, whereas conservative (i. e. family-based), social-democratic (i. e. state-based) and liberal (i. e. market-based) integration strategies to a certain extent prove effective in controlling the incidence of crime, the results are not very robust. Moreover, reservations have to be made regarding the effects of “market” income inequality as well as familialist, unionist and liberalist employment policies that are shown to have reversed effects in our sample: the former reducing, the latter occasionally increasing anomic crime. As expected, the mismatch between cultural ideal (individual inclusion) and structural reality (stratified exclusion) increases the anomic pressure, whereas conservative (i. e. family-based), social-democratic (i. e. state-based) and liberal (i. e. market-based) integration strategies generally prove effective in controlling the incidence of crime. Nevertheless, we conclude that the new cult of the individual undermines the effectiveness of conservative and social-democratic integration strategies and drives societies towards more “liberal” regimes that build on incentive as well as punitive elements.

Institutions, Anomie, and Violent Crime: Clarifying and Elaborating Institutional-Anomie Theory

A limited but accumulating body of research and theoretical commentary offers support for core claims of the “institutional-anomie theory” of crime (IAT) and points to areas needing further development. In this paper, which focuses on violent crime, we clarify the concept of social institutions, elaborate the cultural component of IAT, derive implications for individual behavior, summarize empirical applications, and propose directions for future research. Drawing on Talcott Parsons, we distinguish the “subjective” and “objective” dimensions of institutional dynamics and discuss their interrelationship. We elaborate on the theory’s cultural component with reference to Durkheim’s distinction between “moral” and “egoistic” individualism and propose that a version of the egoistic type characterizes societies in which the economy dominates the institutional structure, anomie is rampant, and levels of violent crime are high. We also offer a heuristic model of IAT that integrates macro- and individual levels of analysis. Finally, we discuss briefly issues for the further theoretical elaboration of this macro-social perspective on violent crime. Specifically, we call attention to the important tasks of explaining the emergence of economic dominance in the institutional balance of power and of formulating an institutional account for distinctive punishment practices, such as the advent of mass incarceration in the United States.

Violence, Crime, and Violent Crime

I propose a dual conceptualization of violent crime. Since violent crime is both violence and crime, theories of aggression and deviance are required to understand it. I argue that both harm-doing and rule breaking are instrumental behaviors and that a bounded rational choice approach can account for both behaviors. However, while some of the causes of harm-doing and deviance (and violent and nonviolent crime) are the same, some are different. Theories of crime and deviance cannot explain why one only observes individual and group differences in violent crime and theories of aggression and violence cannot explain why one observes differences in all types of crimes. Such theories are “barking up the wrong tree.”

GPU-based Ray Tracing of Dynamic Scenes

Interactive ray tracing of non-trivial scenes is just becoming feasible on single graphics processing units (GPU). Recent work in this area focuses on building effective acceleration structures, which work well under the constraints of current GPUs. Most approaches are targeted at static scenes and only allow navigation in the virtual scene. So far support for dynamic scenes has not been considered for GPU implementations. We have developed a GPU-based ray tracing system for dynamic scenes consisting of a set of individual objects. Each object may independently move around, but its geometry and topology are static.

Increasing Realism and Supporting Content Planning for Dynamic Scenes in a Mixed Reality System incorporating a Time-of-Flight Camera

For broadcasting purposes MIXED REALITY, the combination of real and virtual scene content, has become ubiquitous nowadays. Mixed Reality recording still requires expensive studio setups and is often limited to simple color keying. We present a system for Mixed Reality applications which uses depth keying and provides threedimensional mixing of real and artificial content. It features enhanced realism through automatic shadow computation which we consider a core issue to obtain realism and a convincing visual perception, besides the correct alignment of the two modalities and correct occlusion handling. Furthermore we present a possibility to support placement of virtual content in the scene. Core feature of our system is the incorporation of a TIME-OF-FLIGHT (TOF)-camera device. This device delivers real-time depth images of the environment at a reasonable resolution and quality. This camera is used to build a static environment model and it also allows correct handling of mutual occlusions between real and virtual content, shadow computation and enhanced content planning. The presented system is inexpensive, compact, mobile, flexible and provides convenient calibration procedures. Chroma-keying is replaced by depth-keying which is efficiently performed on the GRAPHICS PROCESSING UNIT (GPU) by the usage of an environment model and the current ToF-camera image. Automatic extraction and tracking of dynamic scene content is herewith performed and this information is used for planning and alignment of virtual content. An additional sustainable feature is that depth maps of the mixed content are available in real-time, which makes the approach suitable for future 3DTV productions. The presented paper gives an overview of the whole system approach including camera calibration, environment model generation, real-time keying and mixing of virtual and real content, shadowing for virtual content and dynamic object tracking for content planning.

Bitmap Movement Detection: HDR for Dynamic Scenes

Exposure Fusion and other HDR techniques generate well-exposed images from a bracketed image sequence while reproducing a large dynamic range that far exceeds the dynamic range of a single exposure. Common to all these techniques is the problem that the smallest movements in the captured images generate artefacts (ghosting) that dramatically affect the quality of the final images. This limits the use of HDR and Exposure Fusion techniques because common scenes of interest are usually dynamic. We present a method that adapts Exposure Fusion, as well as standard HDR techniques, to allow for dynamic scene without introducing artefacts. Our method detects clusters of moving pixels within a bracketed exposure sequence with simple binary operations. We show that the proposed technique is able to deal with a large amount of movement in the scene and different movement configurations. The result is a ghost-free and highly detailed exposure fused image at a low computational cost.

Generating Realistic Camera Shake for Virtual Scenes

When depicting both virtual and physical worlds, the viewer's impression of presence in these worlds is strongly linked to camera motion. Plausible and artist-controlled camera movement can substantially increase scene immersion. While physical camera motion exhibits subtle details of position, rotation, and acceleration, these details are often missing for virtual camera motion. In this work, we analyze camera movement using signal theory. Our system allows us to stylize a smooth user-defined virtual base camera motion by enriching it with plausible details. A key component of our system is a database of videos filmed by physical cameras. These videos are analyzed with a camera-motion estimation algorithm (structure-from-motion) and labeled manually with a specific style. By considering spectral properties of location, orientation and acceleration, our solution learns camera motion details. Consequently, an arbitrary virtual base motion, defined in any conventional animation package, can be automatically modified according to a user-selected style. In an animation package the camera motion base path is typically defined by the user via function curves. Another possibility is to obtain the camera path by using a mixed reality camera in motion capturing studio. As shown in our experiments, the resulting shots are still fully artist-controlled, but appear richer and more physically plausible.