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 Committee: David DiBiase (Esri); Wendy Guan (CGA); Elizabeth Langdon-Gray (HDSI); Matt Wilson (CGA)

SPONSORED by:

 

  

 

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

Introduction - Jason Ur (CGA)  AUDIO

 

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

Introduction - Elizabeth Hess (IQSS)  AUDIO