Virtual audits of the urban streetscape: comparing the inter-rater reliability of GigaPan® to Google Street View.

BACKGROUND: Although previous research has highlighted the association between the built environment and individual health, methodological challenges in assessing the built environment remain. In particular, many researchers have demonstrated the high inter-rater reliability of assessing large or objective built environment features and the low inter-rater reliability of assessing small or subjective built environment features using Google Street View. New methods for auditing the built environment must be evaluated to understand if there are alternative tools through which researchers can assess all types of built environment features with high agreement. This paper investigates measures of inter-rater reliability of GigaPan®, a tool that assists with capturing high-definition panoramic images, relative to Google Street View. METHODS: Street segments (n = 614) in Pittsburgh, Pennsylvania in the United States were randomly selected to audit using GigaPan® and Google Street View. Each audit assessed features related to land use, traffic and safety, and public amenities. Inter-rater reliability statistics, including percent agreement, Cohen's kappa, and the prevalence-adjusted bias-adjusted kappa (PABAK) were calculated for 106 street segments that were coded by two, different, human auditors. RESULTS: Most large-scale, objective features (e.g. bus stop presence or stop sign presence) demonstrated at least substantial inter-rater reliability for both methods, but significant differences emerged across finely detailed features (e.g. trash) and features at segment endpoints (e.g. sidewalk continuity). After adjusting for the effects of bias and prevalence, the inter-rater reliability estimates were consistently higher for almost all built environment features across GigaPan® and Google Street View. CONCLUSION: GigaPan® is a reliable, alternative audit tool to Google Street View for studying the built environment. GigaPan® may be particularly well-suited for built environment projects with study settings in areas where Google Street View imagery is nonexistent or updated infrequently. The potential for enhanced, detailed imagery using GigaPan® will be most beneficial in studies in which current, time sensitive data are needed or microscale built environment features would be challenging to see in Google Street View. Furthermore, to better understand the effects of prevalence and bias in future reliability studies, researchers should consider using PABAK to supplement or expand upon Cohen's kappa findings.

Validity of environmental audits using GigaPan® and Google Earth Technology.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Reliability and validity of environmental audits using GigaPan® technology in parks.

Park quality and features can contribute to more engaging places for play and recreation. However, assessing park characteristics remains a challenge. This study measured the reliability of GigaPan® as a method for assessing park characteristics as well as the validity of GigaPan® compared to Google Street View (GSV) and direct observation (DO). A total of 65 target areas (16 parks total) in Pittsburgh, PA were assessed using GigaPan®, GSV, and DO from July 2015-January 2016. For reliability and validity, 14 and 28 variables were examined, respectively. Cohen's kappa was used to assess inter-rater reliability. Sensitivity and specificity were used to measure validity. Of the 14 variables included in the inter-rater reliability analysis, five variables had almost perfect reliability (kappa > 0.80) and three variables had substantial reliability (kappa > 0.60). Of the 28 variables included in the validity analysis, GigaPan® was able to correctly classify 17 of the 28 variables and GSV was able to correctly classify 15 of the 28 variables with a sensitivity >80%. There were no significant differences between sensitivity and specificity between GSV and GigaPan®. GigaPan® performed similarly to GSV with DO being used as the gold standard. Further, GigaPan overall had high reliability among the features measured. A strength of GigaPan® is the ability to be implemented quickly in the field, making it a viable alternative to GSV particularly when temporality is an important factor.

Gigapixel big data movies provide cost-effective seascape scale direct measurements of open-access coastal human use such as recreational fisheries.

Collecting data on unlicensed open-access coastal activities, such as some types of recreational fishing, has often relied on telephone interviews selected from landline directories. However, this approach is becoming obsolete due to changes in communication technology such as a switch to unlisted mobile phones. Other methods, such as boat ramp interviews, are often impractical due to high labor cost. We trialed an autonomous, ultra-high-resolution photosampling method as a cost effect solution for direct measurements of a recreational fishery. Our sequential photosampling was batched processed using a novel software application to produce "big data" time series movies from a spatial subset of the fishery, and we validated this with a regional bus-route survey and interviews with participants at access points. We also compared labor costs between these two methods. Most trailer boat users were recreational fishers targeting tuna spp. Our camera system closely matched trends in temporal variation from the larger scale regional survey, but as the camera data were at much higher frequency, we could additionally describe strong, daily variability in effort. Peaks were normally associated with weekends, but consecutive weekend tuna fishing competitions led to an anomaly of high effort across the normal weekday lulls. By reducing field time and batch processing imagery, Monthly labor costs for the camera sampling were a quarter of the bus-route survey; and individual camera samples cost 2.5% of bus route samples to obtain. Gigapixel panoramic camera observations of fishing were representative of the temporal variability of regional fishing effort and could be used to develop a cost-efficient index. High-frequency sampling had the added benefit of being more likely to detect abnormal patterns of use. Combinations of remote sensing and on-site interviews may provide a solution to describing highly variable effort in recreational fisheries while also validating activity and catch.

High-resolution photo-mosaic time-series imagery for monitoring human use of an artificial reef.

Successful marine management relies on understanding patterns of human use. However, obtaining data can be difficult and expensive given the widespread and variable nature of activities conducted. Remote camera systems are increasingly used to overcome cost limitations of conventional labour-intensive methods. Still, most systems face trade-offs between the spatial extent and resolution over which data are obtained, limiting their application. We trialed a novel methodology, CSIRO Ruggedized Autonomous Gigapixel System (CRAGS), for time series of high-resolution photo-mosaic (HRPM) imagery to estimate fine-scale metrics of human activity at an artificial reef located 1.3 km from shore. We compared estimates obtained using the novel system to those produced with a web camera that concurrently monitored the site. We evaluated the effect of day type (weekday/weekend) and time of day on each of the systems and compared to estimates obtained from binocular observations. In general, both systems delivered similar estimates for the number of boats observed and to those obtained by binocular counts; these results were also unaffected by the type of day (weekend vs. weekday). CRAGS was able to determine additional information about the user type and party size that was not possible with the lower resolution webcam system. However, there was an effect of time of day as CRAGS suffered from poor image quality in early morning conditions as a result of fixed camera settings. Our field study provides proof of concept of use of this new cost-effective monitoring tool for the remote collection of high-resolution large-extent data on patterns of human use at high temporal frequency.