Dataset Information

?Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

ABSTRACT: Google Street View (GSV) was used for urban land use classification, together with airborne light detection and ranging (LiDAR) data and high resolution orthoimagery, by a parcel-based method. In this data article, we present the input raw GSV images, intermediate products of GSV images, and final urban land use classification data that are related to our research article "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View" (Zhang et al., 2017) [1]. More detail about other used data and our findings can be found in Zhang et al. (2017) [1].

SUBMITTER: Zhang W

PROVIDER: S-EPMC5397128 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

Zhang Weixing W Li Weidong W Zhang Chuanrong C Hanink Dean M DM Li Xiaojiang X Wang Wenjie W

Data in brief 20170408

Google Street View (GSV) was used for urban land use classification, together with airborne light detection and ranging (LiDAR) data and high resolution orthoimagery, by a parcel-based method. In this data article, we present the input raw GSV images, intermediate products of GSV images, and final urban land use classification data that are related to our research article "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street ...[more]

PMID: 28459090

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Project description:Triclosan (TCS) and triclocarban (TCC) are antimicrobial agents formulated in a wide variety of consumer products (including soaps, toothpaste, medical devices, plastics, and fabrics) that are regulated by the U.S. Food and Drug Administration (FDA) and U.S. Environmental Protection Agency. In late 2014, the FDA will consider regulating the use of both chemicals, which are under scrutiny regarding lack of effectiveness, potential for endocrine disruption, and potential contribution to bacterial resistance to antibiotics. Here, we report on body burdens of TCS and TCC resulting from real-world exposures during pregnancy. Using liquid chromatography tandem mass spectrometry, we determined the concentrations of TCS, TCC, and its human metabolites (2'-hydroxy-TCC and 3'-hydroxy-TCC) as well as the manufacturing byproduct (3'-chloro-TCC) as total concentrations (Σ-) after conjugate hydrolysis in maternal urine and cord blood plasma from a cohort of 181 expecting mother/infant pairs in an urban multiethnic population from Brooklyn, NY recruited in 2007-09. TCS was detected in 100% of urine and 51% of cord blood samples after conjugate hydrolysis. The interquartile range (IQR) of detected TCS concentrations in urine was highly similar to the IQR reported previously for the age-matched population of the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2004, but typically higher than the IQR reported previously for the general population (detection frequency = 74.6%). Urinary levels of TCC are reported here for the first time from real-world exposures during pregnancy, showing a median concentration of 0.21 μg/L. Urinary concentrations of TCC correlated well with its phase-I metabolite ∑-2'-hydroxy-TCC (r = 0.49) and the manufacturing byproduct ∑-3'-chloro-TCC C (r = 0.79), and ∑-2'-hydroxy-TCC correlated strongly with ∑-3'-hydroxy-TCC (r = 0.99). This human biomonitoring study presents the first body burden data for TCC from exposures occurring during pregnancy and provides additional data on composite exposure to TCS (i.e., from both consumer-product use and environmental sources) in the maternal-fetal unit for an urban population in the United States.

Project description:BACKGROUND:Although previous research has highlighted the association between the built environment and individual health, methodological challenges in assessing the built environment remain. In particular, many researchers have demonstrated the high inter-rater reliability of assessing large or objective built environment features and the low inter-rater reliability of assessing small or subjective built environment features using Google Street View. New methods for auditing the built environment must be evaluated to understand if there are alternative tools through which researchers can assess all types of built environment features with high agreement. This paper investigates measures of inter-rater reliability of GigaPan®, a tool that assists with capturing high-definition panoramic images, relative to Google Street View. METHODS:Street segments (n?=?614) in Pittsburgh, Pennsylvania in the United States were randomly selected to audit using GigaPan® and Google Street View. Each audit assessed features related to land use, traffic and safety, and public amenities. Inter-rater reliability statistics, including percent agreement, Cohen's kappa, and the prevalence-adjusted bias-adjusted kappa (PABAK) were calculated for 106 street segments that were coded by two, different, human auditors. RESULTS:Most large-scale, objective features (e.g. bus stop presence or stop sign presence) demonstrated at least substantial inter-rater reliability for both methods, but significant differences emerged across finely detailed features (e.g. trash) and features at segment endpoints (e.g. sidewalk continuity). After adjusting for the effects of bias and prevalence, the inter-rater reliability estimates were consistently higher for almost all built environment features across GigaPan® and Google Street View. CONCLUSION:GigaPan® is a reliable, alternative audit tool to Google Street View for studying the built environment. GigaPan® may be particularly well-suited for built environment projects with study settings in areas where Google Street View imagery is nonexistent or updated infrequently. The potential for enhanced, detailed imagery using GigaPan® will be most beneficial in studies in which current, time sensitive data are needed or microscale built environment features would be challenging to see in Google Street View. Furthermore, to better understand the effects of prevalence and bias in future reliability studies, researchers should consider using PABAK to supplement or expand upon Cohen's kappa findings.

Dataset Information

?Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

Publications

Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

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Dataset Information

?Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

Publications

​Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.