Systematic social observation of children's neighborhoods using Google Street View: a reliable and cost-effective method.
Systematic social observation of children's neighborhoods using Google Street View: a reliable and cost-effective method.
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DOI：
10.1111/j.1469-7610.2012.02565.x
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发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Moffitt TE
中科院分区：
文献类型：
--
作者：
Odgers CL;Caspi A;Bates CJ;Sampson RJ;Moffitt TE
与富裕社区相比，在贫困社区长大的儿童更有可能入狱、出现健康问题并早逝。社区条件如何影响我们的行为和健康的问题吸引了几代公共卫生官员和学者的关注。在线工具现在提供了测量邻里特征的新机会，并可能提供一种具有成本效益的方法来加深我们对邻里对儿童健康影响的理解。我们进行了一项虚拟系统社会观察 (SSO) 研究，以测试 Google 街景是否可用于可靠地捕捉参与环境风险 (E-Risk) 纵向双胞胎研究的家庭的社区状况。多个评估者对 120 个社区的子样本进行编码，并通过将虚拟 SSO 措施与以下内容联系起来，对 1,000 多个社区的完整样本进行收敛和判别效度评估：(a) 基于消费者的贫困和健康的地理人口统计分类，(b) 当地居民的混乱和安全调查，以及 (c) 家长和老师对儿童反社会行为、亲社会行为和体重指数的评估。记录了身体紊乱、身体腐烂、危险和街道安全迹象的高度一致的观察结果。所有量表的评估者间一致性估计均落在中等至较大范围内（ICC 范围为 0.48 至 0.91）。负面的社区特征，包括与当地居民报告相对应的 SSO 评级混乱、腐烂和危险性，表明与人口普查定义的社会经济地位指数存在分级关系，并预测当地儿童的反社会行为水平较高。此外，积极的社区特征，包括 SSO 评级的街道安全和绿地百分比，与儿童较高的亲社会行为和健康体重状况相关。我们的结果支持使用谷歌街景作为一种可靠且具有成本效益的工具来衡量当地社区的消极和积极特征。
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.
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影响因子：
15.8
作者：
Murray CJ;Kulkarni SC;Michaud C;Tomijima N;Bulzacchelli MT;Iandiorio TJ;Ezzati M
通讯作者：
Ezzati M
影响因子：
5.5
作者：
Rundle AG;Bader MD;Richards CA;Neckerman KM;Teitler JO
通讯作者：
Teitler JO
影响因子：
7.6
作者：
Moffitt, TE
通讯作者：
Moffitt, TE
DOI：
10.1111/j.1469-7610.1994.tb01289.x
发表时间：
1994-11-01
影响因子：
7.6
作者：
GOODMAN, R
通讯作者：
GOODMAN, R
影响因子：
5.5
作者：
Papas, Mia A.;Alberg, Anthony J.;Klassen, Ann C.
通讯作者：
Klassen, Ann C.