Person:

Forsyth, Ann

There is no consensus as to what exactly constitutes a suburb. This article examines the range of suburban definitions in terms of their structure and the topical issues that they grapple with. Suburbs have been defined according to many different dimensions from location and transportation modes to culture and physical appearance. Given this confusion, one approach is to abandon the term; another is to use it with greater precision. This is more than just an issue of semantics. Rather how people talk and think about suburbs shapes how they can see such areas being developed and redeveloped in the future.

Background The prevalence of overweight and obesity in the US population has risen dramatically in recent years. To try to explain this, some studies have examined the association between the built environment and obesity (measured using the body mass index (BMI)). Most of these studies have not sought to identify causal effects, but rather correlations.

Methods Data from the Twin Cities Walking Study were used to examine the effect of population density and block size on BMI. Although the Twin Cities Walking Study is a cross-sectional observational study, the matched-sampling design is novel in that it maximises environmental variance while minimising person variance to enhance exchangeability of subjects and more closely mimic an experimental study.

Results Contrary to expectations, the hypothesised most walkable neighbourhood (high density, small block stratum) had the greatest mean and median BMI. After adjusting for demographic covariates, physical activity and clustering due to neighbourhood, no conclusive effect of population density by block size on BMI was found (β=−1.024, 95% CI −2.408 to 0.359).

Conclusion There is no evidence of an effect of population density by block size on BMI.

Background Obesity 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.

Methods In 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.

Results Results 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.

Conclusions Findings 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.

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.

Understanding the contextual factors associated with why adults walk is important for those interested in increasing walking as a mode of transportation and leisure. This paper investigates the relationships between neighborhood-level sociodemographic context, individual level sociodemographic characteristics and walking for leisure and transport. Data from two community-based studies of adults (n = 550) were used to determine the association between the Area Sociodemographic Environment (ASDE), calculated from U.S. Census variables, and individual-level SES as potential correlates of walking behavior. Descriptive statistics, mean comparisons and Pearson’s correlations coefficients were used to assess bivariate relationships. Generalized estimating equations were used to model the relationship between ASDE, as quartiles, and walking behavior. Adjusted models suggest adults engage in more minutes of walking for transportation and less walking for leisure in the most disadvantaged compared to the least disadvantaged neighborhoods but adding individual level demographics and SES eliminated the significant results. However, when models were stratified for free or reduced cost lunch, of those with children who qualified for free or reduced lunch, those who lived in the wealthiest neighborhoods engaged in 10.7 min less of total walking per day compared to those living in the most challenged neighborhoods (p < 0.001). Strategies to increase walking for transportation or leisure need to take account of individual level socioeconomic factors in addition to area-level measures.

This study used latent class analysis to classify adolescent home neighborhoods (n=344) according to built environment characteristics, and tested how adolescent physical activity, sedentary behavior, and screen time differ by neighborhood type/class. Four distinct neighborhood classes emerged: (1) low-density retail/transit, low walkability index (WI), further from recreation; (2) high-density retail/transit, high WI, closer to recreation; (3) moderate–high-density retail/transit, moderate WI, further from recreation; and (4) moderate–low-density retail/transit, low WI, closer to recreation. We found no difference in adolescent activity by neighborhood class. These results highlight the difficulty of disentangling the potential effects of the built environment on adolescent physical activity.

As the planning academy has grown and evolved, it has developed different ways of doing planning research. People may (a) work at the scientific frontier, (b) investigate issues of practical relevance, (c) reflect on the implications of practice, or (d) try to answer the enduring questions of planning. These are important differences. Different cultures represent varying ideas about what constitutes an important or significant contribution to the field of planning.

This population-based study examined whether residential or school neighborhood access to fast food restaurants is related to adolescents' eating frequency of fast food. A classroom-based survey of racially/ethnically diverse adolescents (n=2724) in 20 secondary schools in Minneapolis/St. Paul, Minnesota was used to assess eating frequency at five types of fast food restaurants. Black, Hispanic, and Native American adolescents lived near more fast food restaurants than white and Asian adolescents and also ate at fast food restaurants more often. After controlling for individual-level socio-demographics, adolescent males living near high numbers fast food restaurants ate more frequently from these venues compared to their peers.