• Google
  • Nokia
  • Yahoo! Inc.
  • AND Automotive Navigation Data
  • earthmine
  • First American Spatial Solutions
  • Waze
  • Google

Sponsorship Opportunities

For information on exhibition and sponsorship opportunities at the conference, contact Yvonne Romaine at yromaine@oreilly.com

Download the Where 2.0 Sponsor/Exhibitor Prospectus

Media Partner Opportunities

Download the Media & Promotional Partner Brochure (PDF) for information on trade opportunities with O'Reilly conferences or contact mediapartners@ oreilly.com

Press and Media

For media-related inquiries, contact Maureen Jennings at maureen@oreilly.com

Where 2.0 Newsletter

To stay abreast of conference news and to receive email notification when registration opens, please sign up for the Where 2.0 Conference newsletter (login required)

Where 2.0 Ideas

Have an idea for Where to share? Tell us!

Contact Us

View a complete list of Where 2.0 contacts

Computing with Maps: A Gentle Introduction to Spatial Statistics in R

Location: Gold Level: Novice
Average rating: ***..
(3.00, 2 ratings)

As a technical problem, map making has never been easier. As an approach to visualization or even data analysis, however, the story rarely ends with attaching “placemarks,” specifying paths or defining regions over some territory. Instead, when we observe or explore patterns in spatial data (or any data for that matter), we are drawn somewhat naturally to questions of inference: Is there really a pattern here? Do the data provide support for a particular “theory” or explanation for the pattern? “Spatial statistics,” as a term, refers both to a collection of numerical summaries for representing and comparing patterns, as well as a set of inferential tools; and it includes a range of problems including smoothing and interpolation, the estimation of spatial autocorrelations, and the analysis of spatial point patterns (to borrow Ripley’s well-established categories). In our session, we will present an introduction to spatial statistics using R, with the goal of providing new tools for both application developers as well as suggestions for facilitating data analysis by end-users. R is both a language and an environment for statistical computing and graphics and offers a number of packages for representing, visualizing and modeling spatial data. We will assume no familiarity with R or statistics (spatial or otherwise), emphasizing computation and visualization.

Please download and install R prior to the workshop www.r-project.org. If you have
difficulty, please send email to cocteau@stat.ucla.edu.

Photo of Mark Hansen

Mark Hansen


Mark Hansen is the Professor of Statistics at UCLA, where
he also holds joint appointments in the departments of Electrical Engineering
and Design|Media Art. He is currently serving as Co-PI at CENS, the Center
for Embedded Networked Sensing, an NSF STC devoted to research into
the design and deployment of sensor networks.