Marco Miriam, Gracia Enrique, Martín-Fernández Manuel, López-Quílez Antonio
University of Valencia, Valencia, Spain.
J Urban Health. 2017 Apr;94(2):190-198. doi: 10.1007/s11524-017-0134-5.
Recently, there has been a growing interest in developing new tools to measure neighborhood features using the benefits of emerging technologies. This study aimed to assess the psychometric properties of a neighborhood disorder observational scale using Google Street View (GSV). Two groups of raters conducted virtual audits of neighborhood disorder on all census block groups (N = 92) in a district of the city of Valencia (Spain). Four different analyses were conducted to validate the instrument. First, inter-rater reliability was assessed through intraclass correlation coefficients, indicating moderated levels of agreement among raters. Second, confirmatory factor analyses were performed to test the latent structure of the scale. A bifactor solution was proposed, comprising a general factor (general neighborhood disorder) and two specific factors (physical disorder and physical decay). Third, the virtual audit scores were assessed with the physical audit scores, showing a positive relationship between both audit methods. In addition, correlations between the factor scores and socioeconomic and criminality indicators were assessed. Finally, we analyzed the spatial autocorrelation of the scale factors, and two fully Bayesian spatial regression models were run to study the influence of these factors on drug-related police interventions and interventions with young offenders. All these indicators showed an association with the general neighborhood disorder. Taking together, results suggest that the GSV-based neighborhood disorder scale is a reliable, concise, and valid instrument to assess neighborhood disorder using new technologies.
最近，利用新兴技术的优势开发测量邻里特征的新工具的兴趣日益浓厚。本研究旨在评估使用谷歌街景（GSV）的邻里混乱观察量表的心理测量特性。两组评估者对西班牙巴伦西亚市一个区的所有普查街区组（N = 92）进行了邻里混乱的虚拟审核。进行了四种不同的分析来验证该工具。首先，通过组内相关系数评估评估者间信度，表明评估者之间的一致性水平适中。其次，进行验证性因素分析以检验量表的潜在结构。提出了一个双因素解决方案，包括一个一般因素（一般邻里混乱）和两个特定因素（物理混乱和物理衰败）。第三，将虚拟审核分数与实地审核分数进行评估，结果显示两种审核方法之间存在正相关关系。此外，还评估了因素分数与社会经济和犯罪指标之间的相关性。最后，我们分析了量表因素的空间自相关性，并运行了两个全贝叶斯空间回归模型来研究这些因素对与毒品相关的警方干预和对青少年罪犯的干预的影响。所有这些指标都显示与一般邻里混乱有关。综合来看，结果表明基于GSV的邻里混乱量表是一种可靠、简洁且有效的工具，可利用新技术评估邻里混乱。
