Environment and Policy Lab, School of Kinesiology, University of Michigan, 1402 Washington Heights, Ann Arbor, MI, 48109-2013, USA.
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Int J Health Geogr. 2018 Jul 6;17(1):26. doi: 10.1186/s12942-018-0147-7.
Health behaviors are shaped by the context in which people live. However, documenting environmental context has remained a challenge. More specifically, direct observation techniques require large investments in time and resources and auditing the environment through web-based platforms has limited stability in spatio-temporal imagery. This study examined the validity of a new methodology, using GigaPan imagery, where we took photos locally and, stitched them together using GigaPan technology, and quantified environmental attributes from the resulting panoramic photo. For comparison, we examined validity using Google Earth imagery.
A total of 464 street segments were assessed using three methods: GigaPan audits, Google Earth audits, and direct observation audits. Thirty-seven different attributes were captured representing three broad constructs: land use, traffic and safety, and amenities. Sensitivity (i.e. the proportion of true positives) and specificity (i.e. the proportion of true negatives) were used to estimate the validity of GigaPan and Google Earth audits using direct observation audits as the gold standard.
Using GigaPan, sensitivity was 80% or higher for 6 of 37 items and specificity was 80% or higher for 31 of 37 items. Using Google Earth, sensitivity was 80% or higher for 8 of 37 items and specificity was 80% or higher for 30 of 37 items. The validity of GigaPan and Google Earth was similar, with significant differences in sensitivity and specificity for 7 items and 2 items, respectively.
GigaPan performed well, especially when identifying features absent from the environment. A major strength of the GigaPan technology is its ability to be implemented quickly in the field relative to direct observation. GigaPan is a method to consider as an alternative to direct observation when temporality is prioritized or Google Earth imagery is unavailable.
人们的健康行为受其生活环境的影响。然而，记录环境背景一直是一个挑战。具体来说，直接观察技术需要大量的时间和资源投入，而通过基于网络的平台对环境进行审计，其时空图像的稳定性有限。本研究检验了一种新方法的有效性，该方法使用 GigaPan 图像，我们在本地拍摄照片，然后使用 GigaPan 技术将它们拼接在一起，并从生成的全景照片中量化环境属性。为了进行比较，我们还使用了谷歌地球图像来检验有效性。
总共评估了 464 个街道段，使用了三种方法：GigaPan 审计、谷歌地球审计和直接观察审计。共捕获了 37 个不同的属性，代表三个广泛的构建：土地利用、交通和安全以及便利设施。使用直接观察审计作为金标准，通过灵敏度（即真阳性的比例）和特异性（即真阴性的比例）来估计 GigaPan 和谷歌地球审计的有效性。
使用 GigaPan，对于 37 个项目中的 6 个，灵敏度达到 80%或更高，对于 37 个项目中的 31 个，特异性达到 80%或更高。使用谷歌地球，对于 37 个项目中的 8 个，灵敏度达到 80%或更高，对于 37 个项目中的 30 个，特异性达到 80%或更高。GigaPan 和谷歌地球的有效性相似，在灵敏度和特异性方面分别有 7 个项目和 2 个项目存在显著差异。
GigaPan 表现良好，尤其是在识别环境中不存在的特征时。GigaPan 技术的一个主要优势是相对于直接观察，它可以在现场快速实施。在时间性优先或谷歌地球图像不可用时，GigaPan 是一种可以考虑替代直接观察的方法。
