Exploring movement object patterns

Change and movement across space and over time are observed in our everyday lives, with people commuting, traveling, communicating, moving, migrating, etc. Understanding how and why such change occurs is important for various reasons, including management of resources, planning for service improvements, detecting whether there are anomalies of some sort, etc. The analysis of spatial information associated with change and movement continues to be supported by a range of techniques, most notably cartography-based exploratory methods. Somewhat lacking, however, are confirmatory and predictive methods to support such analysis. This paper details a suite of approaches implemented in the Python programming language for exploratory analysis, as well as measures that enable statistical testing for pattern significance. Application results for housing movement in an urban region are used to demonstrate the efficacy and functionality of these methods.