Spatial Statistics Workshop

The broad availability of spatial data on and in cities means that planners can paint pictures of both what is where and what was where, when with an unprecedented level of detail. However, where questions often produce more questions than answers. Maps are evocative, but they are unable to answer questions that are crucially important to planners: how are phenomena interrelated, clustered, and interdependent? Spatial statistics offer analytical approaches for getting at these complex questions that are often key to understanding urban environments.

Students will develop the technical skills necessary to ask spatial questions, statistically. We will be covering spatial autocorrelation (including local indicators—so-called ‘hotspot’ methods), interpolation and kernel density methods, and spatial regression. Students will also learn to communicate clearly about spatial methods. Students will also learn to perform sophisticated spatial analyses using the R statistical computing language.