Greg Rybarczyk

Racial/ethnic and gender differences in the association between depressive symptoms and higher body mass index

AIM The study investigated the socio-demographic differences in the association between depressive symptoms and higher body mass index (BMI). SUBJECTS AND METHODS In Genesee County, Michigan, random samples of households were drawn from all residential census tracts. The Speak to Your Health! Survey was administered among adults aged 18 years and older in these households. To conduct this cross-sectional study, data from three waves of survey data collection (2007, 2009 and 2011) were combined resulting in a sample of 3381 adults. Self-reported height and weight were used to calculate BMI. Depressive symptoms were assessed with Brief Symptoms Inventory items. Socio-demographic factors included age, race/ethnicity, gender and education. RESULTS Using stepwise linear regression, gender (β = 0.04, P = 0.02) and the interaction terms of race/ethnicity × depressive symptoms (β = 0.15, P < 0.001) and gender × depressive symptoms (β = 0.05, P = 0.01) uniquely predicted BMI. CONCLUSION Women had a higher BMI than men, and depressive symptoms were more strongly associated with BMI among African Americans and women than among non-Latino Whites and men. Tailored interventions to alleviate depressive symptoms in African Americans and females may help decrease racial/ethnic and gender differences in depressive symptoms and obesity.

Simulating bicycle wayfinding mechanisms in an urban environment

With the increased recognition that bicycling is a sustainable transportation mode choice, there is a continued interest in understanding how the built environment affects bicyclist travel behavior. Research on the influence of small-scale built form elements, such as street characteristics, on bicyclist wayfinding is limited. wayfinding is defined as a purposeful way of reaching from point A to point B; it is comprised of decision-making and subsequent movement. This research uses an agent-based model to investigate how small-scale urban design affects bicyclist wayfinding. Using geographic information systems and statistical analysis, different types of simulated bicycle agents were compared to observed bicycle volumes. Statistically significant positive relationships between bicycle agent types and observational data existed. The largest correspondence between agents and field observations occurred along central routes that were accessible from other streets (R2 = .377) and that had fewer decision-making junctions (R2 = .352). Bicyclists selected streets that were wider and with fewer obstructions to one’s forward view. The results support the need to design streetscapes that offer a high level of visibility and reduced stops to promote bicycling. The study also describes a modeling approach that can be replicated by urban planners to understand bicyclist travel patterns.

Examining the impact of urban morphology on bicycle mode choice

Nonmotorized transport modes such as bicycling are becoming important components to urban transportation systems in the United States, in particular with the recent emphases on sustainable urban development. Recent bicycle forecasting methods have included urban design elements to help explain bicycle behavior but most measures lack accountability of microscale built form attributes that address bicyclist perception. This study developed a discrete choice model to examine the impact of urban morphological factors on peoples utilitarian bicycle mode choice decisions. In the model, traditional factors considered include personal, household, and environmental variables. Urban morphology variables from space syntax were also incorporated in the model to test for the marginal influence of microscale design and space characteristics in the decision to bicycle. Results indicate that microscale built form factors that enhance visibility and contain well connected street networks significantly affected bicycle mode choice decisions at the trip origin. The finding that built form variables by and large influence the probability that someone will commute via bicycle suggests that policies and planning efforts aimed at increasing bicycle mode share should include human-scaled built form metrics that address urban space and cognition.

Improving downtown in a mid-sized legacy city: examining responses to potential downtown improvements in Flint, Michigan

While many researchers have examined strategies for revitalizing downtowns, few have done so in small or mid-sized legacy cities. This article adds to the literature by examining 1263 survey responses to statements about potential improvements to the downtown area of Flint, Michigan (USA). Descriptive statistics were used to identify perceptions of the most promising strategies for improvement. Those strategies were further analyzed using regression methods, to determine which characteristics of participants influenced responses. We found that while there are many improvements that might encourage people to come downtown – such as reducing crime, having more to do, making parking easier, and having suitable housing – some traditional improvements advocated by planners (involving proximity, walkability, and bikeability) were not desired. Additionally, we found that responses appeared to be influenced by age, residency status, and perception of downtown, but not by education level, frequency of visiting, or whether or not one currently works downtown.

Toward a spatial understanding of active transportation potential among a university population

ABSTRACT Universities and surrounding communities stand to benefit when active travel mode choices are elevated. Despite this, there is little research on travel mode choice at commuter universities and, in particular, the nonlinear spatial relationships among active travel potential and various contextual and compositional factors. The purpose of this study was to examine and visualize linkages among personal, household, density, diversity, and design factors, and active travel (bicycling, walking, and mass-transit modes) among a commuter-university population residing throughout southeastern Michigan, USA. This was accomplished by employing exploratory spatial data analysis (ESDA), ordinary least squares (OLS) regression, and a geographically weighted regression (GWR) model. The GWR model outperformed the traditional OLS model in terms of goodness of fit (R2 = .534 and R2 = .461, respectively). A novel cartographic mapping technique was employed to depict where statistically significant parameter estimates negatively or positively influenced active travel. The main finding was that personal, household, density, diversity, and design estimates varied in both magnitude and spatiality throughout the universitys study area. Interestingly, distance was not a universal barrier to active travel potential. These variations emphasize the importance of promoting active transportation through localized interventions as well as coordinating efforts among universities and surrounding communities.

The effects of the water crisis on population dynamics in the City of Flint, Michigan

ABSTRACT Since 2014, residents of Flint, Michigan have dealt with the effects of lead in their municipal system in a series of events known as the Flint water crisis. While several studies have examined the health effects of the crisis and changes in water lead levels over time, no study has considered whether the crisis has resulted or could result in residents leaving the city. To explore this question, we surveyed 405 Flint residents and used quantitative and qualitative methods, as well as exploratory spatial data analysis, to test five hypotheses about the water crisis and population dynamics. We found that population loss has not yet accelerated, but nearly half of residents are considering leaving. This consideration was true regardless of residents’ demographics or where they lived in the city. We also discovered that perceptions of the water’s safety and its health effects, more than actual water lead levels, predicted whether one is considering leaving. Overall, our results suggest that as water infrastructure quality deteriorates, population loss could accelerate, resulting in a recursive relationship whereby the city’s population and its infrastructure continue to decline.

Travel and us: the impact of mode share on sentiment using geo-social media and GIS

Abstract Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major US cities: Chicago, Illinois and Washington DC. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favourably influenced valence, albeit only in DC. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.