Towards a health-conscious transportation planning: A framework for estimating health impacts of active transportation at local level

Peng, Binbin; Erdoğan, Sevgi; Nasri, Arefeh; Zou, Zhenpeng

doi:10.1016/j.jth.2021.101231

Cited by 11 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Transportation decision making is driven in large part by considerations of monetary costs and benefits. Monetizing the health impacts of changes to the built environment designed to support AT could help speed up implementation of AT interventions by demonstrating the costs and benefits of investment in AT and placing them on a common scale [46][47][48]. However, because different entities are responsible for transportation versus health care costs and benefits flow to different populations and organizations, such monetization might not stimulate more rapid investment in AT.…”

Section: The Chat Program and Research Gaps In Active Transportationmentioning

confidence: 99%

“…In a few cases, such models suggest substantial benefits overall for investments in AT infrastructure that are not always captured in simpler models [55]. For example, a recent analysis of Prince Georges County, Maryland in the United States illustrates the use of the Integrated Transport and Health Impact Modeling (ITHIM) framework and a cost of illness/VSL approach and suggests significant health and monetary benefits of increasing active travel and reduced automobile use [48]. Both increased walking and increased biking resulted in benefits.…”

Section: The Chat Program and Research Gaps In Active Transportationmentioning

confidence: 99%

See 1 more Smart Citation

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

Berrigan

Dannenberg

Lee³

et al. 2021

IJERPH

Self Cite

View full text Add to dashboard Cite

Active transportation (AT) is widely viewed as an important target for increasing participation in aerobic physical activity and improving health, while simultaneously addressing pollution and climate change through reductions in motor vehicular emissions. In recent years, progress in increasing AT has stalled in some countries and, furthermore, the coronavirus (COVID-19) pandemic has created new AT opportunities while also exposing the barriers and health inequities related to AT for some populations. This paper describes the results of the December 2019 Conference on Health and Active Transportation (CHAT) which brought together leaders from the transportation and health disciplines. Attendees charted a course for the future around three themes: Reflecting on Innovative Practices, Building Strategic Institutional Relationships, and Identifying Research Needs and Opportunities. This paper focuses on conclusions of the Research Needs and Opportunities theme. We present a conceptual model derived from the conference sessions that considers how economic and systems analysis, evaluation of emerging technologies and policies, efforts to address inclusivity, disparities and equity along with renewed attention to messaging and communication could contribute to overcoming barriers to development and use of AT infrastructure. Specific research gaps concerning these themes are presented. We further discuss the relevance of these themes considering the pandemic. Renewed efforts at research, dissemination and implementation are needed to achieve the potential health and environmental benefits of AT and to preserve positive changes associated with the pandemic while mitigating negative ones.

show abstract

Section: The Chat Program and Research Gaps In Active Transportationmentioning

confidence: 99%

Section: The Chat Program and Research Gaps In Active Transportationmentioning

confidence: 99%

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

Berrigan

Dannenberg

Lee³

et al. 2021

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…PI as a context-dependent and cross-disciplinary behavioral health has been associated with socioeconomic and built-environment factors (Aljabri 2022 ; Prince et al 2022 ; Rütte et al 2013 ). In terms of the built environment, urban characteristics such as urban form (e.g., residential density and high connectivity street network), neighborhood safety, and green space (Xiao et al 2022 ; Peng et al 2021 ; Passi-Solar et al 2020 ; Feng and Astell-Burt 2019 ; Andersen et al 2018 ) have received increased attention due to their associations with PI, obesity, and cardiovascular disease (Adlakha et al 2021 ; Sims et al 2020 ; An et al 2019 ; Lenhart et al 2017 ; Xu et al 2015 ; Unger et al 2014 ). Evidence suggests that the built environment can positively or adversely influence health behaviors.…”

Section: Introductionmentioning

confidence: 99%

Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA

Lotfata

Georganos

2023

J Geogr Syst

View full text Add to dashboard Cite

The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor’s spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities. Supplementary Information The online version contains supplementary material available at 10.1007/s10109-023-00415-y.

show abstract

“…It reduces a household's ability to pay for health-promoting necessities such as nutritious food, healthcare visits, energy, and home maintenance [14][15][16]. Additionally, urban amenities such as parks, bike lanes, and playgrounds encourage an active lifestyle and reduce the incidence of obesity [17][18][19][20][21]. While green spaces are important in reducing obesity concerns [22], there are inconsistent results concerning the association between green spaces and obesity [23].…”

Section: Introductionmentioning

confidence: 99%

Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Lotfata

Georganos

Kalogirou³

et al. 2022

IJGI

View full text Add to dashboard Cite

Some studies have established relationships between neighborhood conditions and health. However, they neither evaluate the relative importance of neighborhood components in increasing obesity nor, more crucially, how these neighborhood factors vary geographically. We use the geographical random forest to analyze each factor’s spatial variation and contribution to explaining tract-level obesity prevalence in Chicago, Illinois, United States. According to our findings, the geographical random forest outperforms the typically used nonspatial random forest model in terms of the out-of-bag prediction accuracy. In the Chicago tracts, poverty is the most important factor, whereas biking is the least important. Crime is the most critical factor in explaining obesity prevalence in Chicago’s south suburbs while poverty appears to be the most important predictor in the city’s south. For policy planning and evidence-based decision-making, our results suggest that social and ecological patterns of neighborhood characteristics are associated with obesity prevalence. Consequently, interventions should be devised and implemented based on local circumstances rather than generic notions of prevention strategies and healthcare barriers that apply to Chicago.

show abstract

Towards a health-conscious transportation planning: A framework for estimating health impacts of active transportation at local level

Cited by 11 publications

References 33 publications

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

The 2019 Conference on Health and Active Transportation: Research Needs and Opportunities

Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA

Ecological Associations between Obesity Prevalence and Neighborhood Determinants Using Spatial Machine Learning in Chicago, Illinois, USA

Contact Info

Product

Resources

About