Abstract
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. 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. 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. 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.
Highlights
Children who grow up in poor versus affluent neighborhoods are more likely to engage in antisocial behavior, experience mental health problems and become overweight (Chen & Paterson, 2006; Duncan, Brooksgunn, & Klebanov, 1994; Papas et al, 2007)
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
Observed Agreement, kappa and intra-class correlation (ICC) coefficients for the virtual social observation (SSO) measures were computed on a subset of 120 neighborhoods
Summary
Children who grow up in poor versus affluent neighborhoods are more likely to engage in antisocial behavior, experience mental health problems and become overweight (Chen & Paterson, 2006; Duncan, Brooksgunn, & Klebanov, 1994; Papas et al, 2007). Assessments are typically based on the child or the mother’s perceptions of neighborhood context (for notable exceptions see Sampson, Raudenbush, & Earls, 1997; Sastry, Ghosh-Dastidar, Adams, & Pebley, 2006). This strategy is not ideal as the same informant typically provides information on both the outcome (e.g., child’s mental health) and the predictor (e.g., neighborhood disorder). 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. Online tools are providing new opportunities to measure neighborhood features and may provide a cost effective way to advance our understanding of neighborhood effects on child health
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