Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr

Photo-sharing services provide a rich resource of crowdsourced spatial data consisting of georeferenced imagery and metadata. Shared photos can provide valuable information for a variety of applications and geospatial analysis tasks, such as identifying tourist hot spots or traveled routes. Understanding the spatiotemporal patterns of photo contributions will allow analysts to assess the suitability of these data for related analysis tasks. Using California as a study area, this paper analyzes various aspects of photo contribution patterns of Panoramio and Flickr. It identifies areas where annual photo contributions are still growing and areas that undergo a decline in annual contributions. Multiple regression is used to identify which environmental correlates are associated with an increase in photo-sharing activities. Furthermore, panel data of annual contributions between 2006 and 2013 for California subcounties will be used in a regression model to demonstrate that there is a positive feedback effect between Panoramio and Flickr photo contributions, but no neighborhood effect. The results of this paper provide insight into the data quality of crowdsourced image collections. These collections are commonly used for geospatial applications, including tourist information services and the computation of scenic routes.