Ross Maciejewski

TL;DR: A comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models, is conducted and several open challenges are presented and potential directions for future research are discussed.

TL;DR: A visual analytics approach that provides users with scalable and interactive social media data analysis and visualization including the exploration and examination of abnormal topics and events within varioussocial media data sources, such as Twitter, Flickr and YouTube is presented.

TL;DR: This chapter explores applications of sentiment analysis and demonstrates how sentiment mining in social media can be exploited to determine how local crowds react during a disaster, and how such information can be used to improve disaster management.

TL;DR: A suite of tools designed to facilitate the exploration of spatiotemporal data sets is presented, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses.

TL;DR: An innovative big data approach to derive street-level morphology and urban feature composition as experienced by a pedestrian from Google Street View (GSV) imagery is developed and constitutes an important step towards building a global morphological database to describe the form and composition of cities from a human-centric perspective.