NACIS 2018

Monitoring Urban Transit Networks: Map use in transit control centers  (10 minutes)
Presenter: Emily Domanico, The Pennsylvania State Univeristy
In public transit networks, Control Centers are sites where information comes together and is monitored and assessed. In these environments, maps and diagrams play a central role in the work done to coordinate movements. We presented results from observation and interview fieldwork that aims to understand dynamic cartography in situ, in transit control centers. Further, our work explores the technology of digital maps, their use, and the communities of people who work collectively with dynamic maps to monitor rail transit in real time.

Viral Cartography: Or, how to make an affective map 
Presenter: Jerry Shannon, University of Georgia
Copresenter: Kyle Walker, Texas Christian University
Our paper focuses on viral maps, ones that gain widespread traction in social and news media. We focus on two case studies from our own experience: one based on an interactive dot map showing educational attainment and the other a map of the 2017 solar eclipse and Waffle House locations. We argue that the affective nature of these maps played a key role in their popularity, meaning that the emotional response generated by each map fueled public interest. Our paper suggests a taxonomy for describing the emotional registers of viral maps and considers the implications for publicly engaged cartography.

The Ballot Box and Sunsquatch: Elements of Viral Cartography
Presenter: Anthony Robinson, The Pennsylvania State University
Making and sharing maps is easier than ever, and social media makes it possible for maps to attain widespread visibility and engagement to become examples of viral cartography. In this talk we present a framework for evaluating the design and dissemination of viral maps. We apply this framework in two case studies of maps that reached wide audiences on Twitter (2016 US Election & the 2017 Solar Eclipse), and we explore collections of maps generated in response to viral maps using image analysis and machine learning techniques.

Designing a Map-Centric Interface to Improve Geospatial Analytics
Presenter: Ryan Mullins,
Copresenters: Ben Nargi, Aptima Inc.; Patrick Cummings, Ph.D., Aptima Inc.
Recent advances in machine learning methods have created highly automated geospatial analytics. These analytics still require human insight and validation to learn from their mistakes and be able to connect dots across data modalities. Here, we present work-in-progress research to design a map-centric interface that elicits human insight and trains machine learning-based geospatial analytics. This interface engages a human analyst in a forensic task, and transforms their interactions into corrective feedback that is delivered to the analytics, as well as their creators. We discuss the design process and preliminary results from limited user testing.