Sidewalk Conditions in Northern New Jersey: Using Google Street View Imagery and Ordinary Kriging to Assess Infrastructure for Walking

Plascak, Jesse J.; Llanos, Adana A.M.; Chavali, Laxmi; Xing, Cathleen Y.; Shah, Nimit N.; Stroup, Antoinette M.; Plaha, Jessica; McCue, Emily M; Rundle, Andrew; Mooney, Stephen J.

doi:10.5888/pcd16.180480

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…(U.S. Census, 2020). Analyses have been conducted on sidewalk conditions in northern New Jersey (Plascak, et al, 2019) and walking and bicycle routes in central Bergen County (Bergen County Department of Planning & Engineering, 2015), however no critical analysis has been conducted regarding its zoning laws. Despite holding a population of nearly one million people (U.S. Census, 2020), limited walkability research has been done on the county, meaning that a study examining the area could have a substantial impact on a large population.…”

Section: Zoning Codesmentioning

confidence: 99%

The Association Between Walkability, Zoning Codes, and Neighborhood Wealth in Bergen County, NJ

Song

2023

J Stud Res

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Walkability is the measure of how friendly an area is to walking, and is important due to the multiple economic, social, environmental, and health-related benefits of walkable communities. A wealth of literature exists on its impacts, however a gap remains in its association with income levels and the zoning laws which affect it. A correlational analysis of multiple factors of towns in Bergen County, NJ, using Pearson’s correlation coefficient, found that towns of higher income tended to be less walkable, due to low land use diversity, suggesting that these towns could increase their walkability through the incorporation of different types of land use, most notably, commercial zones. The results of this study could be used to advise the government of Bergen County on future redevelopment and provide valuable information for the creation of zoning laws for new developments in the U.S.

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Section: Zoning Codesmentioning

confidence: 99%

The Association Between Walkability, Zoning Codes, and Neighborhood Wealth in Bergen County, NJ

Song

2023

J Stud Res

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“…The contextual characteristics addressed in this collection, Population Health, Place, and Space: Spatial Perspectives in Chronic Disease Research and Practice, range from underlying context (such as neighborhood deprivation [1], racial segregation [2], social capital [3], and resiliency [4]) to the built environment (walkability [5,6], park access [7], and healthy restaurants [8]) and environmental exposures (9). The study comparing cardiovascular disease-resilient neighborhoods with cardiovascular disease-at-risk neighborhoods examines the important, but understudied, concept of neighborhood resiliency as it affects black populations (4). The study of neighborhood risk and pediatric asthma provides additional evidence of the need for interventions that move beyond primary care or clinical settings (1).…”

Section: Examining Connections Between Community-level Characteristicmentioning

confidence: 99%

Population Health, Place, and Space: Spatial Perspectives in Chronic Disease Research and Practice

et al. 2019

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“…Spatial interpolation is another area where machine learning approaches have been applied to neighborhood health research, including in studies of air pollution, of neighborhood physical disorders, and sidewalk conditions [ 4 , 57 – 59 ]. Spatial interpolation models such as kriging and land use regression estimate neighborhood conditions at all locations across a geographic area using measured data from only a sample of locations in the region [ 4 , 57 , 60 – 63 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

2022

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Purpose of review Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health. Recent findings Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas. Summary In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.

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Sidewalk Conditions in Northern New Jersey: Using Google Street View Imagery and Ordinary Kriging to Assess Infrastructure for Walking

Cited by 5 publications

References 6 publications

The Association Between Walkability, Zoning Codes, and Neighborhood Wealth in Bergen County, NJ

The Association Between Walkability, Zoning Codes, and Neighborhood Wealth in Bergen County, NJ

Population Health, Place, and Space: Spatial Perspectives in Chronic Disease Research and Practice

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

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