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Systematic social observation of children’s neighborhoods using Google Street View: a reliable and cost-effective method

Authors

  • Candice L. Odgers,

    1. Center for Child and Family Policy and the Sanford School of Public Policy, Duke University, Durham, NC, USA
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  • Avshalom Caspi,

    1. Department of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, and Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA
    2. Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, UK
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  • Christopher J. Bates,

    1. Department of Psychology and Social Behavior, University of California, Irvine, CA, USA
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  • Robert J. Sampson,

    1. Department of Sociology, Harvard University, Cambridge, MA, USA
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  • Terrie E. Moffitt

    1. Department of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, and Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA
    2. Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, UK
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  • Conflict of interest statement:
    The authors declare no conflicts of interest. Candice L. Odgers had full access to all the data and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Abstract

Background:  Children growing up in poor versus affluent neighborhoods are more likely to spend time in prison, develop health problems and die at an early age. The question of how neighborhood conditions influence our behavior and health has attracted the attention of public health officials and scholars for generations. Online tools are now providing new opportunities to measure neighborhood features and may provide a cost effective way to advance our understanding of neighborhood effects on child health.

Method:  A virtual systematic social observation (SSO) study was conducted to test whether Google Street View could be used to reliably capture the neighborhood conditions of families participating in the Environmental-Risk (E-Risk) Longitudinal Twin Study. Multiple raters coded a subsample of 120 neighborhoods and convergent and discriminant validity was evaluated on the full sample of over 1,000 neighborhoods by linking virtual SSO measures to: (a) consumer based geo-demographic classifications of deprivation and health, (b) local resident surveys of disorder and safety, and (c) parent and teacher assessments of children’s antisocial behavior, prosocial behavior, and body mass index.

Results:  High levels of observed agreement were documented for signs of physical disorder, physical decay, dangerousness and street safety. Inter-rater agreement estimates fell within the moderate to substantial range for all of the scales (ICCs ranged from .48 to .91). Negative neighborhood features, including SSO-rated disorder and decay and dangerousness corresponded with local resident reports, demonstrated a graded relationship with census-defined indices of socioeconomic status, and predicted higher levels of antisocial behavior among local children. In addition, positive neighborhood features, including SSO-rated street safety and the percentage of green space, were associated with higher prosocial behavior and healthy weight status among children.

Conclusions:  Our results support the use of Google Street View as a reliable and cost effective tool for measuring both negative and positive features of local neighborhoods.

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