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2018 CGA Conference: Illuminating Space and Time in Data Science

April 26, 2018 to April 27, 2018
Location  CGIS South Building

AUDIO recordings of the Conference are linked below.

The rapid proliferation of ‘smart’ objects have enabled a variety of sensors operating a wide range of scales -- from the body to the planet -- resulting in unprecedented volumes of digital data. The field of Data Science has been increasingly called upon to take on the unique challenges represented by this proliferation. Lacking any singular identity, Data Science may include discovering, understanding and communicating complex behaviors, patterns, relationships and trends from “big data” using mathematics/statistics, computation/automation, and domain knowledge -- combined. Data Science has as its subject nearly any field for which there exists high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation (Gartner 2012).

The emergence of Data Science has provided a renewed opportunity to consider the importance of spatial relationships at the heart of these smart sensors and Internet of Things (IoT). Indeed, space and time are core properties of ‘big data’, so called, and spatiotemporal analysis is inherently an important facet in Data Science. From satellite images to social media streams, from census and parcels to records of trade, food, energy, climate, disease, crime, conflicts, etc., big data with space and time signatures are essential for understanding our world and responding to its challenges.

This conference aims at bringing together mainstream data scientists and geographic information scientists, to review the status of both fields, explore commonalities between the two, and identify the relevance of space and time in Data Science. The program will highlight new breakthroughs in Data Science; examine how to incorporate them into GIScience; demonstrate GIScience contributions to Data Science, particularly in terms of handling space and time; explore the proper relationship between Data Science and GIScience; discuss opportunities for reaching new audiences; and identify common educational needs for a data scientist and a GIScientist.

The event will start with a half-day hands-on demo and training workshop on Thursday afternoon, followed by a full day of plenary sessions on Friday, which will include a keynote address, presentation sessions, panel discussions, and closing remarks. Invited speakers will engage with the audience in discussions on the current status, achievements, lessons learned, unmet needs, challenges, potentials, and perspectives of spatiotemporal analytics in the context of Data Science, particularly as it relates to academic research and learning.

Keynote Speakers

Francesca Dominici, Co-Director of the Harvard Data Science Initiative, Professor of Biostatistics, Harvard T.H. Chan School of Public Health

Michael F. Goodchild, Emeritus Professor of Geography, University of California at Santa Barbara

Preliminary Program

Organizing CommitteeDavid DiBiase (Esri); Wendy Guan (CGA); Elizabeth Langdon-Gray (HDSI); Matt Wilson (CGA)





 Day 1  -- Workshops -- Thursday, April 26th, 2017

Introduction - Jason Ur (CGA)  AUDIO


Thu 1  Interacting with National Water Model Predictions, Devika Kakkar (CGA), Josh Lieberman (CGA)  AUDIO  SLIDES

Thu 2  Spatiotemporal Methodologies and Analytics for Extreme Weather Study – Using Dust Storm Event as an Example,  Manzhu Yu (STC and GMU)  AUDIO SLIDES

Thu 3 GeoAI: Machine Learning Meets GIS, Omar Maher (ESRI)  AUDIO

Thu 4 Big Flow Data Visual Analytics through TrajAnalytics, Xinyue Ye (KSU and CGA) AUDIO  SLIDES

 Day 2  -- Conference -- April 27th, 2017

Introduction - Elizabeth Hess (IQSS)  AUDIO


  Fri AM Session 1:  Sensors, Smart Objects and Infrastructure for Data Science

Fri (AM 1) 2  Senseable Cities, Carlo Ratti (MIT)  AUDIO  SLIDES

Fri (AM 1) 3  The University of Things (UoT), Peter Fox (Rensselaer)  AUDIO  SLIDES

Fri (AM 1) 4  Sensing in Space and Time, Mike Goodchild (UCSB)  AUDIO  SLIDES

Fri (AM 1) 5  Scientific Discovery in the Age of AI,  Brendan Meade (Harvard EPS)  AUDIO

Fri (AM 1) 6  Big Spatiotemporal Data Challenges and Opportunities,  Phil Yang (GMU)  AUDIO 

  Fri AM Session 2:  Crowdsourcing, Geocomputation, and Spatiotemporal Analysis

Fri (AM 2) 1  Growing Trust and Transparency in Communities Where Predictive Algorithms are Deployed, Amen Ra Mashariki (NYU)  AUDIO  SLIDES

Fri (AM 2) 2  Making Spatial Aggregation More Transparent, Amelia McNamara (Smith)  AUDIO  SLIDES

Fri (AM 2) 3  Transdisciplinary Foundations of Geo-spatial Data Science, Shashi Shekhar (Smith)  AUDIO  SLIDES

Fri (AM 2) 4  Challenges and Solutions for the Analysis of New Forms of Data, Alex Singleton (Liverpool)  AUDIO SLIDES

Fri (AM 2) 5  Progress in the Pipeline: Curating, Analyzing, and Conveying New Insights from Space-time Data, Robert Stewart (ORNL)  AUDIO SLIDES


  Fri PM Session 1:  Data Science for Cities, Health, and Environment

Fri (PM 1) 2  Empowering Local Communities through Data Analytics and AI, Emad Khazraee (Harvard Berkman/Kent State)  AUDIO SLIDES

Fri (PM 1) 3  A Moral Compass for Data Science and AI in the City, Renee Sieber (McGill) AUDIO SLIDES

Fri (PM 1) 4   Putting Clinical (image) Data on a Map, Bjoern Menze (TU Munich) AUDIO

Fri (PM 1) 5 
 Estimating Pedestrian Flows on Street Networks:  Revisiting the betweenness Index, Andres Sevtsuk (Harvard GSD) AUDIO  SLIDES

Fri (PM 1) 6  A Convergence of Spatial Access, Amy Lobben (U Oregon) AUDIO

  Fri PM Session 2:  Geography, Civic Engagement, and the Future of Data Science

Fri (PM 2) 1  SocioEcological Applications of Remote Sensing Analysis at Scale, Jessica Block (UCSD) AUDIO SLIDES

Fri (PM 2) 2  Giving Relevance to Spatial Analytics and Spatial Data, Chris Cappelli (ESRI) AUDIO  SLIDES

Fri (PM 2) 3  Why We Need Both Geography and Data Science to Achieve Sustainable Development, Robert Chen (Columbia/CIESIN) AUDIO  SLIDES

Fri (PM 2) 4 Opening and Maintaining Lines of Communication between Data Science and Geographic Information Science, Diana Sinton (UCGIS) AUDIO SLIDES

Fri (PM 2) 5  Top-Down and Bottom-Up, Krzysztof Janowicz (UCSB) AUDIO  SLIDES