Walking Distance by Trip Purpose and Population Subgroups

Yang, Yong; Diez‐Roux, Ana V.

doi:10.1016/j.amepre.2012.03.015

Cited by 384 publications

(230 citation statements)

References 33 publications

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“…Park neighborhoods were defined by a one-half-mile buffer around the park, about the distance of a typical walking trip (27). Because the population density of Chapel Hill is considerably lower than that of other cities, the block groups around the parks there were much larger.…”

Section: Data Collectionmentioning

confidence: 99%

Physical Activity Surveillance in Parks Using Direct Observation

Ward¹,

McKenzie²,

Cohen³

et al. 2014

Prev. Chronic Dis.

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Introduction Primary features of observational public health surveillance instruments are that they are valid, can reliably estimate physical activity behaviors, and are useful across diverse geographic settings and seasons by different users. Previous studies have reported the validity and reliability of Systematic Observation of Play and Recreation in Communities (SOPARC) to estimate park and user characteristics. The purpose of this investigation was to establish the use of SOPARC as a surveillance instrument and to situate the findings from the study in the context of the previous literature.

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Section: Data Collectionmentioning

confidence: 99%

Physical Activity Surveillance in Parks Using Direct Observation

Ward¹,

McKenzie²,

Cohen³

et al. 2014

Prev. Chronic Dis.

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“…Parking lots would be the main destination for private vehicles, which may bring congestion around. Since people would walk to their final destination, parking lots' influencing scope should be the typical walking distance, 0.25 miles (Yang & Diez-Roux, 2012), and the buffer was chosen as 500m in this paper.…”

Section: F7mentioning

confidence: 99%

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Zhang

Sun

Shen

et al. 2017

JTLU

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Abstract:With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship between traffic congestion and built environment, including traffic-related factors and land use. Fuzzy C-means clustering was used to conduct an exhaustive study on the 24-hour congestion pattern of road segments in an urban area, so that the spatial autoregressive moving average model (SARMA) could be introduced to analyze the output from the clustering analysis to establish the relationship between built environment and the 24-hour congestion pattern. The clustering result classified the road segments into four congestion levels, while the regression explained the impact of 12 traffic-related factors and land-use factors on the road congestion pattern. The continuous congestion was found to mainly occur in the city center, and the factors, such as road type, bus station in the vicinity, ramp nearby, commercial land use, and so on, had large impacts on congestion formation. The Fuzzy C-means clustering is proposed to be combined with quantitative spatial regression, and the overall evaluation process will assist to assess the spatial-temporal levels of service regarding traffic from the congestion perspective.Keywords: Congestion pattern, taxi GPS data, fuzzy C-means clustering, spatiotemporal regression, built environment factor IntroductionIn urban road network, the recurrent or current congestion of a certain road segment may largely impact the local network and reduce travel efficiency. Consequently, it is important to identify the Compared with mobile phone data, floating car data, cargo transport vehicle record and navigation system, taxi GPS trace data is one of the easiest available sources for accurate travel route and travel time records for a wider area with more road details. Data mining based on taxi trip can be traced back to the 1970s (Goddard, 1970), which has been applied to a wide range of studies, mainly including activity-based and infrastructure-based fields. The activity-based studies mostly focus on driver behavior, supply-demand pattern, and traffic state analysis, while the infrastructure-based studies mainly focused on lanes channelization (Tang, Yang, Kan, & Li, 2015) and signal-timing estimation (Yu & Lu, 2016).From driver behavior perspective, Zhang, Qiu, Duan, Du, and Lu (2015) proposed a space-time visualization method to demonstrate taxi daily trajectories by GIS-T to recognize working time, operating range, and residence location without time division. Qing, Parfenov, and Kim (2015) compared direct extracted datas like travel distance, speed, demand, and supply mismatch of taxi trip between fair weather and extreme storm using Manhattan GPS data, and discovered the reduction in trip distance and supply of drives during the extreme storm. Meanwhile, Hwang, Wu and Jian (2006) used structural equation modeling technique...

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“…11,12 Studies also have examined park access in specific cities by demographic characteristics such as race/ ethnicity and socioeconomic status, though these findings have been mixed, and very few studies have been national in scope. 9,10,[12][13][14][15] Some studies have shown that neighborhoods that are poor, have high unemployment, and have fewer college-educated adults have less park access. 13,15 However, other studies show neighborhoods with lower socioeconomic indicators have better access to parks and play areas.…”

mentioning

confidence: 99%

Park Access among School-Age Youth in the United States

Harris

Paul²,

Young³

et al. 2015

Journal of Physical Activity and Health

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Background: Fewer than 30% of U.S. youth meet the recommendation to be active ≥ 60 minutes/day. Access to parks may encourage higher levels of physical activity. Purpose: To examine differences in park access among U.S. school-age youth, by demographic characteristics and urbanicity of block group. Methods: Park data from 2012 were obtained from TomTom, Incorporated. Population data were obtained from the 2010 U.S. Census and American Community Survey 2006-2010. Using a park access score for each block group based on the number of national, state or local parks within one-half mile, we examined park access among youth by majority race/ethnicity, median household income, median education, and urbanicity of block groups. Results: Overall, 61.3% of school-age youth had park access-64.3% in urban, 36.5% in large rural, 37.8% in small rural, and 35.8% in isolated block groups. Park access was higher among youth in block groups with higher median household income and higher median education. Conclusion: Urban youth are more likely to have park access. However, park access also varies by race/ethnicity, median education, and median household. Considering both the demographics and urbanicity may lead to better characterization of park access and its association with physical activity among youth.

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Walking Distance by Trip Purpose and Population Subgroups

Cited by 384 publications

References 33 publications

Physical Activity Surveillance in Parks Using Direct Observation

Physical Activity Surveillance in Parks Using Direct Observation

Analyzing spatiotemporal congestion pattern on urban roads based on taxi GPS data

Park Access among School-Age Youth in the United States

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