A review of quantitative methods for movement data

Abstract

The collection, visualization, and analysis of movement data is at the forefront of geographic information science research. Movement data are generally collected by recording an object's spatial location (e.g., XY coordinates) at discrete time intervals. Methods for extracting useful information, for example space–time patterns, from these increasingly large and detailed datasets have lagged behind the technology for generating them. In this article we review existing quantitative methods for analyzing movement data. The objective of this article is to provide a synthesis of the existing literature on quantitative analysis of movement data while identifying those techniques that have merit with novel datasets. Seven classes of methods are identified: (1) time geography, (2) path descriptors, (3) similarity indices, (4) pattern and cluster methods, (5) individual–group dynamics, (6) spatial field methods, and (7) spatial range methods. Challenges routinely faced in quantitative analysis of movement data include difficulties with handling space and time attributes together, representing time in GIS, and using classical statistical testing procedures with space–time movement data. Areas for future research include investigating equivalent distance comparisons in space and time, measuring interactions between moving objects, developing predictive frameworks for movement data, integrating movement data with existing geographic layers, and incorporating theory from time geography into movement models. In conclusion, quantitative analysis of movement data is an active research area with tremendous opportunity for new developments and methods.

Keywords:

• mobile objects
• spatio-temporal data modeling
• time geography
• geographic information science
• spatial analysis

Acknowledgements

The authors thank the referees for their constructive remarks on earlier versions of this manuscript. Their comments greatly improved the presentation of this work. Support for this work was obtained from the Natural Sciences and Engineering Research Council of Canada, and GEOIDE through the Government of Canada's Networks for Centres of Excellence program.