David Van Riper

Patterns of Obesogenic Neighborhood Features and Adolescent Weight: A comparison of statistical approaches

BACKGROUND Few studies have addressed the potential influence of neighborhood characteristics on adolescent obesity risk, and findings have been inconsistent. PURPOSE Identify patterns among neighborhood food, physical activity, street/transportation, and socioeconomic characteristics and examine their associations with adolescent weight status using three statistical approaches. METHODS Anthropometric measures were taken on 2682 adolescents (53% female, mean age=14.5 years) from 20 Minneapolis/St. Paul MN schools in 2009-2010. Neighborhood environmental variables were measured using GIS data and by survey. Gender-stratified regressions related to BMI z-scores and obesity to (1) separate neighborhood variables; (2) composites formed using factor analysis; and (3) clusters identified using spatial latent class analysis in 2012. RESULTS Regressions on separate neighborhood variables found a low percentage of parks/recreation, and low perceived safety were associated with higher BMI z-scores in boys and girls. Factor analysis found five factors: away-from-home food and recreation accessibility, community disadvantage, green space, retail/transit density, and supermarket accessibility. The first two factors were associated with BMI z-score in girls but not in boys. Spatial latent class analysis identified six clusters with complex combinations of both positive and negative environmental influences. In boys, the cluster with highest obesity (29.8%) included low SES, parks/recreation, and safety; high restaurant and convenience store density; and nearby access to gyms, supermarkets, and many transit stops. CONCLUSIONS The mix of neighborhood-level barriers and facilitators of weight-related health behaviors leads to difficulties disentangling their associations with adolescent obesity; however, statistical approaches including factor and latent class analysis may provide useful means for addressing this complexity.

Creating a replicable, valid cross-platform buffering technique: The sausage network buffer for measuring food and physical activity built environments

BackgroundObesity researchers increasingly use geographic information systems to measure exposure and access in neighborhood food and physical activity environments. This paper proposes a network buffering approach, the “sausage” buffer. This method can be consistently and easily replicated across software versions and platforms, avoiding problems with proprietary systems that use different approaches in creating such buffers.MethodsIn this paper, we describe how the sausage buffering approach was developed to be repeatable across platforms and places. We also examine how the sausage buffer compares with existing alternatives in terms of buffer size and shape, measurements of the food and physical activity environments, and associations between environmental features and health-related behaviors. We test the proposed buffering approach using data from EAT 2010 (Eating and Activity in Teens), a study examining multi-level factors associated with eating, physical activity, and weight status in adolescents (n = 2,724) in the Minneapolis/St. Paul metropolitan area of Minnesota.ResultsResults show that the sausage buffer is comparable in area to the classic ArcView 3.3 network buffer particularly for larger buffer sizes. It obtains similar results to other buffering techniques when measuring variables associated with the food and physical activity environments and when measuring the correlations between such variables and outcomes such as physical activity and food purchases.ConclusionsFindings from various tests in the current study show that researchers can obtain results using sausage buffers that are similar to results they would obtain by using other buffering techniques. However, unlike proprietary buffering techniques, the sausage buffer approach can be replicated across software programs and versions, allowing more independence of research from specific software.

How Do You Measure Distance in Spatial Models? An Example Using Open-Space Valuation

Spatial distance is a critical component of theories across the social, natural, and information sciences, but too often the methods and metrics used to describe spatial distance are implicit or underspecified. How distance is measured may influence model results in unanticipated ways. We examined the differences among distances calculated in three ways: Euclidean distances, vector-based road-network distances, and raster-based cost-weighted distances. We applied these different measures to the case of the economic value of open space, which is frequently derived using hedonic pricing (HP) models. In HP models, distance to open space is used to quantify access for residential properties. Under the assumption that vector-based road distances better match actual travel distance between homes and open spaces, we compared these distances with Euclidean and raster-based cost-weighted distances, finding that the distance values themselves differed significantly. Open-space values estimated using these distances in hedonic models differed greatly and values for Euclidean and cost-weighted distances to open space were much lower than those for road-network distances. We also highlight computational issues that can lead to counterintuitive effects in distance calculations. We recommend the use of road-network distances in valuing open space using HP models and caution against the use of Euclidean and cost-weighted distances unless there are compelling theoretical reasons to do so.

Terra Populus: Workflows for Integrating and Harmonizing Geospatial Population and Environmental Data

The Terra Populus project (TerraPop) addresses a variety of data management, curation, and preservation challenges with respect to spatiotemporal population and environmental data. In this article, we describe our approaches to these challenges, with a particular focus on geospatial data workflows and associated provenance metadata. The goal of TerraPop is to enable research, learning, and policy analysis by providing integrated spatiotemporal data describing people and their environment. To do so, TerraPop is assembling a globe-spanning and temporally extensive collection of high-quality population and environmental data, ensuring good documentation, and developing a Web-based data access system that enables users to assemble customized integrated data sets drawing on a variety of data sources and formats. We describe TerraPops collection strategies, detail the geospatial workflows involved in preparing data for ingest into the project database and those used to transform data across formats for dissemination, and discuss the system used to capture and manage provenance metadata throughout the project. A key aspect of the project is the development of global current and historical administrative unit boundaries that can be linked to census data. These boundaries serve as the linchpin of TerraPops data integration strategy, and constitute an important data set in their own right.

VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

Do Irregularly Shaped School Attendance Zones Contribute to Racial Segregation or Integration

This research investigates if and how much the shapes of school attendance zones contribute to racial segregation in schools. We find that the typical school attendance zone is relatively compact and resembles a square-like shape. Compact zones typically draw children from local residential areas, and since local areas are often racially homogeneous, this suggests that high levels of racial segregation in the largest school districts are largely structured by existing residential segregation. Still, this study finds that the United States contains some attendance zones with highly irregular shapes—some of which are as irregular as the most irregular Congressional District. Although relatively rare, attendance zones that are highly irregular in shape almost always contain racially diverse student populations. This racial diversity contributes to racial integration within school districts. These findings contradict recent theoretical and empirical scholarship arguing that irregularly shaped zones contribute to racial segregation in schools. Our findings suggest that most racial segregation in school attendance zones is driven by large-scale segregation across residential areas rather than a widespread practice among school districts to exacerbate racial segregation by delineating irregularly shaped attendance zones.

Harmonizing Disparate Data across Time and Place: The Integrated Spatio-Temporal Aggregate Data Series.

Abstract In this article, the authors describe a new data infrastructure project being developed at the Minnesota Population Center. The Integrated Spatio-Temporal Aggregate Data Series (ISTADS) will make it easier for researchers to use publicly available aggregate data for the United States over a time span that covers virtually the entire life of the nation: 1790–2012. In addition to facilitating access and ease of use, ISTADS will facilitate the use of these various data sets in mapping and spatial analysis.

Terra Populus: Integrated Data on Population and Environment

Terra Populus, part of National Science Foundations DataNet initiative, is developing organizational and technical infrastructure to integrate, preserve, and disseminate data describing changes in the human population and environment over time. A large number of high-quality environmental and population datasets are available, but they are widely dispersed, have incompatible or inadequate metadata, and have incompatible geographic identifiers. The new Terra Populus infrastructure enables researchers to identify and merge data from heterogeneous sources to study the relationships between human behavior and the natural world.