Location data is often complex and contains multiple dimensions that are hard to summarize into a manageable location variable. Geospatial clustering techniques handle these problems by reducing the dimensionality of location data into smaller, manageable, and relevant variables for the data analysis process. Clustering technique importance increases as the amount of data grows.
Location clustering can be referred to as the grouping of different objects into clusters that are similar to each other and fall within the same geographic area. Here, similarity is the metric used to indicate how relationships are strong in different locations.
This chapter tries to explain and explore clustering techniques, as we will use machine learning and spatial statistics to derive an insightful location analysis with less dimensional complexity. We...