Understanding statistics is critical in GIS as we need to summarize and explore datasets and be able to identify relationships and make predictions. In this class we reviewed the kinds of data that exist, ways of summarizing it and the two main ways of looking for relations: visualizations and quantitative approaches.

One of the most important quantitative approaches in GIS that we discussed in class is regression modelling. It helps answer why we observe certain spatial patterns and if the patterns are significant or just a result of random outcomes. A number of methods can be used, such as ordinary least squares which can help determine the most important variables from the AIC, and the geographically weighted regression which we describe and apply in detail in lab 3.