Developing and testing a street audit tool using Google Street View to measure environmental supportiveness for physical activity
1 Sport and Health Sciences, University of Exeter, Exeter, UK
2 Department of Public Health, University of Oxford, Oxford, UK
3 Norwich Medical School, University of East Anglia, Norwich, UK
4 London School of Hygiene and Tropical Medicine, London, UK
International Journal of Behavioral Nutrition and Physical Activity 2013, 10:103 doi:10.1186/1479-5868-10-103Published: 23 August 2013
Walking for physical activity is associated with substantial health benefits for adults. Increasingly research has focused on associations between walking behaviours and neighbourhood environments including street characteristics such as pavement availability and aesthetics. Nevertheless, objective assessment of street-level data is challenging. This research investigates the reliability of a new street characteristic audit tool designed for use with Google Street View, and assesses levels of agreement between computer-based and on-site auditing.
The Forty Area STudy street VIEW (FASTVIEW) tool, a Google Street View based audit tool, was developed incorporating nine categories of street characteristics. Using the tool, desk-based audits were conducted by trained researchers across one large UK town during 2011. Both inter and intra-rater reliability were assessed. On-site street audits were also completed to test the criterion validity of the method. All reliability scores were assessed by percentage agreement and the kappa statistic.
Within-rater agreement was high for each category of street characteristic (range: 66.7%-90.0%) and good to high between raters (range: 51.3%-89.1%). A high level of agreement was found between the Google Street View audits and those conducted in-person across the nine categories examined (range: 75.0%-96.7%).
The audit tool was found to provide a reliable and valid measure of street characteristics. The use of Google Street View to capture street characteristic data is recommended as an efficient method that could substantially increase potential for large-scale objective data collection.