Thanks to recent advances in high-performance computing and deep learning, computer vision algorithms coupled with spatial analysis methods provide a unique opportunity for extracting human activity patterns from geo-tagged social media images. However, there are only a handful of studies that evaluate the utility of computer vision algorithms for studying large-scale human activity patterns. In this article, we introduce an analytical framework that integrates a computer vision algorithm based on convolutional neural networks (CNN) with kernel density estimation to identify objects, and infer human activity patterns from geo-tagged photographs. To demonstrate our framework, we identify bird images to infer birdwatching activity from approximately 20 million publicly shared images on Flickr, across a three-year period from December 2013 to December 2016. In order to assess the accuracy of object detection, we compared results from the computer vision algorithm to concept-based image retrieval, which is based on keyword search on image metadata such as textual description, tags, and titles of images. We then compared patterns in birding activity generated using Flickr bird photographs with patterns identified using eBird data—an online citizen science bird observation application. The results of our eBird comparison highlight the potential differences and biases in casual and serious birdwatching, and similarities and differences among behaviors of social media and citizen science users. Our analysis results provide valuable insights into assessing the credibility and utility of geo-tagged photographs in studying human activity patterns through object detection and spatial analysis.
Learning Human Activity Patterns using Clustered Point Processes with Active and Inactive States
