This short course provides an introduction to the field of spatial regression modeling.  When analyzing data aggregated to geographic areas (e.g., census data for counties), a fresh set of issues arise that are not present in traditional non-spatial data analyses.  These issues need to be recognized and accounted for when properly specifying regression models using attributes that are linked to geographic location.  The topics covered in two afternoon sessions include:

•    Why standard regression models generally fail when analyzing spatial data

•    Defining and understanding “spatial autocorrelation”

•    Causes of spatial autocorrelation

•    Measuring & operationalizing spatial effects

•    Defining spatial “neighborhoods”

•    Creating spatial weights matrices

•    Moran’s I statistic

•    Incorporating spatial effects in spatial regression models

•    Specification & estimation of spatial regression models

•    Spatial regression model diagnostics

•    (Time permitting:  some interesting extensions to related topics)


Examples of estimating spatial regression models will use the open source software suite R (no prior knowledge of R is necessary)




Registration Fees:

• 
  UNC-CH Students - $50
• 
  All Others - $100
  
  
  * Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
  
  
  * Waitlist/ Walk-ins: There may be a waitlist for the courses. Walk-ins will not be accepted. Each attendee must register and pay prior to 3 days before the start of the course.