Longitudinal relations between sectarian and nonsectarian community violence and child adjustment in Northern Ireland

Although relations between political violence and child adjustment are well documented, longitudinal research is needed to adequately address the many questions remaining about the contexts and developmental trajectories underlying the effects on children in areas of political violence. The study examined the relations between sectarian and nonsectarian community violence and adolescent adjustment problems over 4 consecutive years. Participants included 999 mother-child dyads (482 boys, 517 girls), M ages = 12.18 (SD = 1.82), 13.24 (SD = 1.83), 13.61 (SD = 1.99), and 14.66 (SD = 1.96) years, respectively, living in socially deprived neighborhoods in Belfast, Northern Ireland, a context of historical and ongoing political violence. In examining trajectories of adjustment problems, including youth experience with both sectarian and nonsectarian antisocial behaviors, sectarian antisocial behavior significantly predicted more adjustment problems across the 4 years of the study. Experiencing sectarian antisocial behavior was related to increased adolescent adjustment problems, and this relationship was accentuated in neighborhoods characterized by higher crime rates. The discussion considers the implications for further validating the distinction between sectarian and nonsectarian violence, including consideration of neighborhood crime levels, from the child's perspective in a setting of political violence. Copyright © Cambridge University Press 2013.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.

Green Space Attachment and Health: A Comparative Study in Two Urban Neighborhoods

The positive relationships between urban green space and health have been well documented. Little is known, however, about the role of residents’ emotional attachment to local green spaces in these relationships, and how attachment to green spaces and health may be promoted by the availability of accessible and usable green spaces. The present research aimed to examine the links between self-reported health, attachment to green space, and the availability of accessible and usable green spaces. Data were collected via paper-mailed surveys in two neighborhoods (n = 223) of a medium-sized Dutch city in the Netherlands. These neighborhoods differ in the perceived and objectively measured accessibility and usability of green spaces, but are matched in the physically available amount of urban green space, as well as in demographic and socio-economic status, and housing conditions. Four dimensions of green space attachment were identified through confirmatory factor analysis: place dependence, affective attachment, place identity and social bonding. The results show greater attachment to local green space and better self-reported mental health in the neighborhood with higher availability of accessible and usable green spaces. The two neighborhoods did not differ, however, in physical and general health. Structural Equation Modelling confirmed the neighborhood differences in green space attachment and mental health, and also revealed a positive path from green space attachment to mental health. These findings convey the message that we should make green places, instead of green spaces.

Home and Workplace Built Environment Supports for Physical Activity

BACKGROUND: Physical inactivity has been associated with obesity and related chronic diseases. Understanding built environment (BE) influences on specific domains of physical activity (PA) around homes and workplaces is important for public health interventions to increase population PA.

PURPOSE: To examine the association of home and workplace BE features with PA occurring across specific life domains (work, leisure, and travel).

METHODS: Between 2012 and 2013, telephone interviews were conducted with participants in four Missouri metropolitan areas. Questions included sociodemographic characteristics, home and workplace supports for PA, and dietary behaviors. Data analysis was conducted in 2013; logistic regression was used to examine associations between BE features and domain-specific PA.

RESULTS: In home neighborhoods, seven of 12 BE features (availability of fruits and vegetables, presence of shops and stores, bike facilities, recreation facilities, crime rate, seeing others active, and interesting things) were associated with leisure PA. The global average score of home neighborhood BE features was associated with greater odds of travel PA (AOR=1.99, 95% CI=1.46, 2.72); leisure PA (AOR=1.84, 95% CI=1.44, 2.34); and total PA (AOR=1.41, 95% CI=1.04, 1.92). Associations between workplace neighborhoods' BE features and workplace PA were small but in the expected direction.

CONCLUSIONS: This study offers empirical evidence on BE supports for domain-specific PA. Findings suggest that diverse, attractive, and walkable neighborhoods around workplaces support walking, bicycling, and use of public transit. Public health practitioners, researchers, and worksite leaders could benefit by utilizing worksite domains and measures from this study for future BE assessments.

Use of emerging technologies to assess differences in outdoor physical activity in St. Louis, Missouri.

Introduction: Abundant evidence shows that regular physical activity (PA) is an effective strategy for preventing obesity in people of diverse socioeconomic status (SES) and racial groups. The proportion of PA performed in parks and how this differs by proximate neighborhood SES has not been thoroughly investigated. The present project analyzes online public web data feeds to assess differences in outdoor PA by neighborhood SES in St. Louis, MO, USA.
Methods: First, running and walking routes submitted by users of the website MapMyRun.com were downloaded. The website enables participants to plan, map, record, and share their exercise routes and outdoor activities like runs, walks, and hikes in an online database. Next, the routes were visually illustrated using geographic information systems. Thereafter, using park data and 2010 Missouri census poverty data, the odds of running and walking routes traversing a low-SES neighborhood, and traversing a park in a low-SES neighborhood were examined in comparison to the odds of routes traversing higher-SES neighborhoods and higher-SES parks.
Results: Results show that a majority of running and walking routes occur in or at least traverse through a park. However, this finding does not hold when comparing low-SES neighborhoods to higher-SES neighborhoods in St. Louis. The odds of running in a park in a low-SES neighborhood were 54% lower than running in a park in a higher-SES neighborhood (OR = 0.46, CI = 0.17-1.23). The odds of walking in a park in a low-SES neighborhood were 17% lower than walking in a park in a higher-SES neighborhood (OR = 0.83, CI = 0.26-2.61).
Conclusion: The novel methods of this study include the use of inexpensive, unobtrusive, and publicly available web data feeds to examine PA in parks and differences by neighborhood SES. Emerging technologies like MapMyRun.com present significant advantages to enhance tracking of user-defined PA across large geographic and temporal settings.